CN110151152B

CN110151152B - Sedentary period detection with wearable electronics

Info

Publication number: CN110151152B
Application number: CN201910510664.5A
Authority: CN
Inventors: J.A.阿诺; A.M.拉塞尔; Z.L.沃森二世; S.G.J.袁
Original assignee: Feibit Co ltd
Current assignee: Feibit Co ltd
Priority date: 2015-03-24
Filing date: 2016-03-24
Publication date: 2022-06-17
Anticipated expiration: 2036-03-24
Also published as: CN110151152A; CN105996984B; CN105996984A

Abstract

Systems and methods for determining a sedentary state of a user are described. Sensor data is collected and analyzed to calculate Metabolic Equivalent (MET) measurements for the task at multiple moments of interest. Based on the MET measurement and a time period for which the MET measurement exceeds a threshold, it is determined whether the user is in a sedentary state. If the user is in a sedentary state, a notification is provided to the user to encourage the user to perform a non-sedentary activity.

Description

Sedentary period detection with wearable electronics

The application is a divisional application of Chinese patent application with the application date of 2016, 03 and 24, the application number of 201610172515.9, and the invention name of 'detection of sedentary time period by using wearable electronic device'.

Technical Field

Embodiments described in this disclosure relate to the field of wearable electronic devices. In particular, embodiments relate to the automatic detection of sedentary periods and the facilitation of non-sedentary behavior with wearable electronic devices.

Background

Trackers have gained popularity among consumers. Trackers are used to track a user's activities using various sensors and help the user maintain a healthy lifestyle. To determine activity, the tracker collects activity data and runs calculations on that data. One difficulty in obtaining an accurate determination of activity is that the trackers (because they are worn by the user) are typically packaged in a tight package containing a less powerful processor on which complex calculations are more difficult to run than larger electronic devices.

Another challenge in tracking activities is the differentiation between stationary but active users and sedentary users (e.g., consuming little energy, etc.). For example, when a user is seated or typing on a computer, the user consumes little energy. Increased total sedentary time and longer, more permanent sedentary periods are associated with physical disability, unhealthy (e.g., obesity, metabolic disorders, etc.).

Disclosure of Invention

In some embodiments, a wearable electronic device to be worn by a user is described. The wearable electronic device includes a set of one or more sensors to generate sensor data associated with a user when the user wears the wearable electronic device. The wearable electronic device also includes a set of one or more processors coupled to the set of sensors and a non-transitory machine-readable storage medium coupled to the set of one or more processors and having instructions stored therein. When the set of one or more processors execute the instructions, the instructions cause the wearable electronic device to track a period of time during which the state of the user is determined to be sedentary. The determination is based on a Metabolic Equivalent (MET) measurement of the task at the time of interest and the MET measurement is calculated based on the sensor data. The instructions additionally cause the user to receive a notification in response to the tracked time period to encourage the user to limit the length of the sedentary time period.

In embodiments, the instructions additionally cause the wearable electronic device to classify the user state for each moment of interest as sedentary or non-sedentary based on the MET measurement for that moment of interest.

In several embodiments, the time period is tracked based on a determination of a contiguous time instant to the time instant of interest at which the state of the user is classified as sedentary.

In some embodiments, when the MET measurement at a time of interest is less than a threshold MET value, the user state at that time of interest is classified as sedentary.

In embodiments, when the MET measurement for a time of interest is between a first threshold and a second threshold, the user state at that time of interest is classified as sedentary and is preceded by a first time of interest within a threshold window of time for which the user's state is sedentary and is followed by a second time of interest for which the user's state is sedentary within the threshold window of time.

In some embodiments, one of the one or more sensors is a photoplethysmography (PPG) sensor, and the MET measurement is based on a heart rate measurement of the user (calculated based on PPG sensor data).

In various embodiments, the instructions cause the wearable electronic device to filter out a period of time during which the user's state is asleep.

In some embodiments, the wearable electronic device includes sensors to generate sensor data associated with the user when the user wears the wearable electronic device. The wearable electronic device also includes a set of one or more processors coupled to the sensor and a non-transitory machine-readable storage medium coupled to the set of one or more processors and having instructions stored therein. When the set of one or more processors execute the instructions, the instructions cause the wearable electronic device to track a period of time during which the state of the user is determined to be sedentary based on the sensor data. The time period has a beginning and an end. The instructions additionally cause the wearable electronic device to detect a transition of the user state from sedentary to non-sedentary for a threshold period of time in response to an end of the period of time. The instructions cause the wearable device to receive a notification in response to the detection to encourage the user to remain non-sedentary.

In several embodiments, the notification is a message displayed on a display device of the wearable electronic device or a vibration of the wearable electronic device or a sound emitted by the wearable electronic device.

In some embodiments, the notification indicates that the user has ended the period of time during which the user's status is sedentary.

In embodiments, the notification is determined based on preferences set by the user.

In some embodiments, the notification is an actuation statement displayed on a display device of the wearable electronic device.

In various embodiments, an apparatus is described for improving the efficiency of notifications provided to a user of a wearable electronic device. The apparatus includes an electronic device including a sedentary status monitor to notify a user of the wearable electronic device to encourage the user to change his/her sedentary behavior based on a period of time during which the user is sedentary during the tracking. The sedentary state monitor includes a set of one or more managers to receive current states from states of a plurality of users during a time period. The state comprises a sedentary state. The one or more managers cause the wearable electronic device to receive a notification based on the current state to notify the user of a length of time during which the user is in the sedentary state.

In some embodiments, the apparatus further comprises a sedentary learning unit coupled to receive data about the notification from each of the one or more managers. A sedentary learning unit is coupled to the set of one or more sensors of the wearable electronic device to determine which notifications have the effect of modifying sedentary behavior of the user, and to determine an updated configuration of at least one of the one or more managers. The updated configuration improves the user's response to the notification to limit the length of the period during which the user is sedentary.

In various embodiments, the one or more managers include a sedentary alert manager that receives a current status of the user over a period of time. The sedentary alert manager generates a sedentary alert based on the detection of the time period exceeding the sedentary time period threshold. The sedentary alert manager sends a notification to the wearable electronic device indicating that the time period exceeds a sedentary time period threshold.

In some embodiments, the one or more managers further comprise a non-sedentary state transition manager to receive the current state of the user. The non-sedentary state transition manager generates a notification based on the detection of the end of the sedentary period of the current state. A notification is sent from the non-sedentary state transition manager to the wearable electronic device.

In embodiments, the sedentary learning unit determines an updated configuration of at least one of the sedentary alarm manager and the non-sedentary state transition manager based on the notification information received from the sedentary alarm manager and the non-sedentary state transition manager. The updated configuration improves the user's response to the notification information to limit the length of the sedentary period during which the user is in a sedentary state.

In some embodiments, the updated configuration includes disabling at least one of the sedentary alert manager and the non-sedentary state transition manager.

In several embodiments, the sedentary learning unit includes a decision tree, a random forest, a support vector machine, a neural network, K-nearest neighbors, naive bayes, or hidden markov models.

In embodiments, the sedentary learning unit allows the user to be notified of an alarm.

In some embodiments, the sedentary learning unit uses data related to the notification of the snooze to determine an updated configuration of at least one of the one or more managers.

In various embodiments, the electronic device is a wearable electronic device.

Drawings

The embodiments described in this disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like references indicate similar elements.

FIG. 1A illustrates sedentary user status detection and sedentary alert management according to embodiments described in the present disclosure.

FIG. 1B illustrates a flowchart of operations for tracking a sedentary time period and having a user receive notifications based on the sedentary time period, according to some embodiments described in the present disclosure.

FIG. 2 illustrates the use of sedentary and non-sedentary states of a user to determine a period of time during which the user's state is sedentary according to several embodiments described in this disclosure.

Fig. 3 illustrates a sedentary status monitor for notifying a user based on tracking of a sedentary period to encourage the user to change his/her sedentary behavior and limit the length of the sedentary period, according to some embodiments described in the present disclosure.

FIG. 4 illustrates the delivery of a notification to a user based on the detection of the end of a sedentary period and the beginning of a non-sedentary period (a threshold period of time has been exceeded) according to embodiments described in this disclosure.

Fig. 5 illustrates delivery of a sedentary alert to a user based on detection of a sedentary time period exceeding a sedentary time period threshold, according to some embodiments described in this disclosure.

Fig. 6A illustrates a recording of Metabolic Equivalent (MET) measurements of a task at successive moments of interest over a period of time and a classification of user states at each moment of interest based on the MET measurements, according to embodiments described in the present disclosure.

Fig. 6B illustrates a recording of MET measurements at successive moments of interest over a period of time and a classification of user states at each moment of interest based on the MET measurements, according to some embodiments described in this disclosure.

Fig. 7 is a block diagram of a wearable electronic device and an electronic device illustrating implementation of the operations disclosed herein, in accordance with various embodiments described in the present disclosure.

Fig. 8 is a block diagram of a wrist-worn electronic device having buttons, a display, and a wristband for securing the wrist-worn electronic device to a user's forearm according to some embodiments described in this disclosure.

Detailed Description

In the following description, numerous specific details are set forth. However, it is understood that embodiments described in this disclosure may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of the embodiments. However, it will be recognized by one skilled in the art that embodiments may be practiced without such specific details. Having described embodiments, those of ordinary skill in the art will be able to implement appropriate functionality of the embodiments without undue experimentation.

In some embodiments, the terms "coupled" and "connected," along with their derivatives, are used. It should be understood that these terms are not intended as synonyms for each other. For example, "coupled" is used to indicate that two or more elements are in direct or no direct physical or electrical contact with each other, cooperate or interact with each other. Further, in this example, "connected" is used to indicate establishment of communication between two or more elements coupled to each other. Further, in various embodiments, a "set," as used herein, refers to any positive integer number of items, including one item, unless a set is otherwise stated (e.g., zero or more).

In some embodiments, the electronic device stores the code internally and/or transmits the code to other electronic devices over a computer network. The code is made up of software instructions and is sometimes referred to as computer program code or a computer program stored in a machine-readable storage medium. In some embodiments, the code contains data for execution of the code. In various embodiments, the machine-readable storage medium is a computer-readable medium. Examples of computer readable media include magnetic disks, optical disks, read-only memories (ROMs), Random Access Memories (RAMs), flash memory devices, phase change memories, and so forth. In various embodiments, the code is transmitted using a machine-readable transmission medium (also known as a carrier wave, e.g., an electrical, optical, radio, acoustical or other form of propagated signal). Further examples of carrier waves include carrier waves, infrared signals, and the like.

In various embodiments, an electronic device (e.g., a computer, etc.) includes hardware and software. For example, an electronic device includes one or more processors coupled to one or more machine-readable storage media storing code for execution on the one or more processors and/or storing data. To further illustrate, the electronic device includes non-volatile memory (including code) and the non-volatile memory stores code or data even when the electronic device is turned off (e.g., upon removal of power to the electronic device, etc.). When the electronic device is turned on, code executed by one or more processors of the electronic device is copied from the non-volatile memory to volatile memory (e.g., Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), etc.) of the electronic device. Non-volatile memory is slower to access than volatile memory. Electronic devices also typically include a collection of one or more network interfaces, e.g., network interface controllers, network interface cards, internet access controllers, etc., each of which establishes a physical or wireless network connection with other electronic devices to communicate code and/or data using propagated signals. Wearable Electronic Devices (WEDs), described in additional detail below, are examples of electronic devices. It should be noted that different combinations of software, firmware, and/or hardware may be used to implement one or more portions of the embodiments described in this disclosure.

Following is an embodiment describing the tracking of the sedentary state of the user and the generation of a sedentary notification.

Fig. 1A illustrates sedentary state detection and sedentary alert management according to some embodiments described in this disclosure. It should be noted that in some embodiments, task blocks 1, 2, 3A, 3B, and 4 of fig. 1A are performed and components 110, 112, 120, 122, 124, and 126 of fig. 1A are implemented in a wearable electronic device or distributed between the wearable electronic device and one or more of the other electronic devices coupled to the wearable electronic device. In some embodiments, the wearable electronic device is worn on a body part of the user, e.g., an arm, a wrist, an ankle, or a chest, etc., or embedded in clothing worn by the user. Examples of one or more other electronic devices include servers (including hardware and software), tablet computers, smart phones, desktop computers, laptop computers, and smart televisions. In some embodiments, one or more other electronic devices execute an application (sometimes referred to as an app) to implement, for example, sensor data analyzer 112, user state tracking unit 190, and/or sedentary notification unit 192.

Task blocks 1-4 illustrate the order in which components 110, 112, 120, 122, 124, and 126 perform operations. As illustrated by task block 1, one or more sensors 110 generate sensor data 150 for a plurality of time intervals. For example, the one or more sensors 110 are implemented in a wearable electronic device such that when worn by a user, at least some of the sensor data is indicative of an activity performed by the user. Examples of sensor data include biometric data. In some embodiments, the one or more sensors 110 that generate the sensor data 150 include motion sensors (e.g., three-axis accelerometers, etc.). The motion sensor generates motion sensor data indicative of the user's motion (e.g., number of steps taken, number of stairs climbed, number of stairs down, etc.). In various embodiments, the one or more sensors 110 include a heart rate sensor (e.g., a photoplethysmography (PPG) sensor, etc.) to generate cardiac sensor data (e.g., PPG sensor data, etc.) indicative of the heart rate of the user. In several embodiments, the motion sensor and the heart rate sensor are both placed in the same wearable electronic device or in different wearable electronic devices. In some embodiments, other types of sensors are placed in the wearable electronic device or devices, such as gyroscopes, gravity sensors, rotation vector sensors, magnetometers, temperature sensors (measuring the temperature of the user's skin and/or the environment surrounding the user), ambient light sensors (measuring ambient light of the environment), galvanic skin response sensors, capacitance sensors, humidity sensors, sound sensors, and the like. Examples of environments surrounding the user include a room in which the user is located, a street on which the user is standing or driving, an interior of a vehicle in which the user is located, and so forth. In several embodiments, some or all of the above-described sensor data 150 is generated by and received by the wearable electronic device from one of the one or more other electronic devices.

The sensor data 150 generated during the time interval is processed by the sensor data analyzer 112. In some embodiments, a subset of the sensor data 150 is generated by performing statistical operations (e.g., averaging, etc.) on the sensor data 150 and processed by the sensor data analyzer 112. At task block 2, sensor data analyzer 112 analyzes sensor data 150 received from one or more sensors 110 and calculates analyzed sensor information 152 for each moment of interest, and the analyzed sensor information 152 is used to determine a status of the user (e.g., sedentary status or non-sedentary status, etc.). In some embodiments, the analyzed sensor information 152 for a plurality of time instants of interest is calculated at regular time intervals (e.g., intervals in the range of 30 seconds-1.5 minutes, 1 minute intervals, intervals in the range of 0.5 seconds-1.5 seconds, 1 second intervals, etc.). In various embodiments, the time interval is configurable and dynamically adjusted (e.g., decreased or increased) based on various factors and the like, and/or can be automatically disabled and/or manually disabled over a span of time by a user to conserve power.

In some embodiments, the analyzed sensor information 152 is Metabolic Equivalent (MET) measurements of the task, where each MET is determined for a time of interest. MET measurements are normalized measures of energy expenditure, which increases with activity and is non-zero at the time of interest. For example, MET measurements for inactive or sleep or unworn states are close to 1.0, MET measurements for users who are walking are typically greater than 2.0, and MET measurements for users who are swimming are between 10.0 and 11.0. While in some embodiments, the analyzed sensor information 152 is a MET measurement, embodiments use different measurements (e.g., a motion measurement indicative of motion of a user wearing the wearable electronic device, a heart rate measurement indicative of a heart rate of the user, etc.). Motion measurements are sometimes referred to herein as movement measurements. Examples of motion measurements include the number of steps a user takes, the number of stairs a user climbs or descends, and so forth. In embodiments, a heart rate sensor (e.g., heart rate monitoring, etc.) generates heart sensor data indicative of a user's heart rate and calculates a user's heart rate measurement.

The user state tracking unit 190 generates the states 156 of the user for different time periods using the analyzed sensor information 152 at the time of interest. In some embodiments, each time segment typically contains multiple contiguous time instances of interest. In embodiments, each time period is as small as one time of interest. The user state tracking unit 190 contains a user state identifier 120 that classifies each time of interest 154 as a state of the user based on the analyzed sensor information 152 for that time of interest. As indicated in task block 3A, the user's status is divided into a sedentary status and a non-sedentary status.

In some embodiments, the user state classifier 120 classifies the state of the user at the time of interest 154 as sedentary (e.g., sitting and typing on a computer, etc.) or non-sedentary (e.g., active, running, walking, exercising, dancing, swimming, etc.). It should be noted that in embodiments, the user consumes more energy when the user's state is classified as non-sedentary than when the user's state is classified as sedentary. Several methods for classifying the moments of interest 154 are described in more detail below with reference to fig. 6A-6B.

In embodiments, MET measurements are used to determine a non-sedentary state of a user and to associate the non-sedentary state with a particular type of activity of the user. For example, from the MET measurements, the user status classifier 120 determines whether the user is running, walking, sprinting, cycling, swimming, or performing another type of non-sedentary activity.

As described in task block 3B, the time period detector 122 of the user state tracking unit 190 detects a time period during which the state of the user is sedentary, based on the adjoining time of interest at which the state of the user is classified as sedentary. For example, according to some embodiments described below with reference to fig. 2, when the time period detector 122 determines that the time frame contains contiguous moments of interest for a sedentary state, the time period detector 122 determines that the time frame has a sedentary state.

Sedentary notification unit 192 uses the user's status 156 for different time periods to generate one or more sedentary alerts 158 to notify the user. Examples of one or more sedentary alerts 158 are provided below. The one or more sedentary alerts 158 encourage the user to limit the length of the sedentary period. At task block 4, sedentary status monitor 124 of sedentary notification unit 192 generates one or more sedentary alerts 158 (e.g., notifications, etc.) for providing to the user to encourage the user to change his/her sedentary behavior. One or more sedentary alerts 158 are provided to the user through user interface 126 (a display device including a wearable electronic device). In some embodiments, the user receives one or more sedentary alerts 158 through vibrations of the wearable electronic device, messages displayed on a display device of the wearable electronic device, and/or sounds emitted by a speaker within the wearable electronic device.

In some embodiments, the sensor data analyzer 112 is located inside the wearable electronic device or inside one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as being performed by the sensor data analyzer 112. In several embodiments, the user state classifier 120 is located inside the wearable electronic device or inside one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as being performed by the user state classifier 120. In various embodiments, the time period detector 122 is located inside the wearable electronic device or inside one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as performed by the time period detector 122. In several embodiments, sedentary status monitor 124 is located inside a wearable electronic device or inside one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as sedentary state monitor 124 performs. In some embodiments, the user interface 126 is located inside the wearable electronic device or inside one of the other electronic devices. For example, a processor of one of the wearable electronic devices or other electronic devices performs the operations described herein as performed by the user interface 126. Examples of processors include Application Specific Integrated Circuits (ASICs), Programmable Logic Devices (PLDs), central processing units, microprocessors, controllers, microcontrollers, and the like.

In some embodiments, a different processor of the wearable electronic device performs operations, rather than the processor of the wearable electronic device performing the operations described herein as performed by the sensor data analyzer 112, the user state classifier 120, the time period detector 122, the sedentary state monitor 124, and the user interface 126. For example, a processor of the wearable electronic device performs the operations described herein as performed by sensor data analyzer 112, another processor of the wearable electronic device performs the operations described herein as performed by user state classifier 120, yet another processor of the wearable electronic device performs the operations described herein as performed by time period detector 122, another processor of the wearable electronic device performs the operations described herein as performed by sedentary state monitor 124, and another processor of the wearable electronic device performs the operations described herein as performed by user interface 126. Similarly, in embodiments, a different processor of one of the other electronic devices performs operations, but a processor of one of the other electronic devices performs operations described herein as being performed by sensor data analyzer 112, user state classifier 120, time period detector 122, sedentary state monitor 124, and user interface 126.

In various embodiments, one or more processors of the wearable electronic device perform the operations described herein as performed by sensor data analyzer 112, user state classifier 120, time period detector 122, sedentary state monitor 124, and user interface 126. Similarly, in some embodiments, one or more processors of one of the other electronic devices perform the operations described herein as performed by sensor data analyzer 112, user state classifier 120, time period detector 122, sedentary state monitor 124, and user interface 126.

It should be noted that in embodiments in which components 112, 120, 122, and 124 located in one of the other electronic devices perform the tasks described above with reference to task blocks 2-4, the wearable electronic device includes a display device to display one or more sedentary alerts 158. Further, in these embodiments, one of the wearable electronic device and the other electronic device communicates with each other via a communication medium (e.g., a universal serial bus cable, a wireless protocol air medium, a serial cable, a parallel cable, etc.). An example of a wireless protocol includes bluetooth (tm).

FIG. 1B illustrates a flow diagram of a method for tracking a sedentary time period and having a user receive notifications based on the sedentary time period, in accordance with embodiments described in the present disclosure. At operation 102 of the method, a time period detector 122 tracks a time period in which the user's status is determined to be sedentary (sometimes referred to herein as a "sedentary time period"). The determination of the time periods is based on MET measurements at respective non-overlapping moments of interest within each time period. Additionally, in some embodiments, MET measurements are generated within the wearable electronic device based on sensor data 150 received from one or more sensors 110 (fig. 1A) (e.g., a tri-axial accelerometer and heart rate sensor, etc.). Examples of MET measurements include movement measurements calculated based on sensor data 150 from a motion sensor and heart rate measurements calculated based on sensor data 150 from a heart rate sensor (placed within the same wearable electronic device). If the length of the sedentary period exceeds a predetermined threshold period, then period detector 122 determines that the user's status is sedentary.

At operation 104 of the method for tracking a sedentary period, an action is performed that provides a notification to the user based on the tracking at operation 102 to encourage the user to limit the length of the sedentary period. In some embodiments, operation 104 is performed within a wearable electronic device and the user is notified by receiving a message on a display device of the wearable electronic device, a vibration of the wearable electronic device, and/or a sound emitted by a speaker within the wearable electronic device.

In some embodiments, a notification as described herein is an electronic notification sent to a display device. For example, the processor renders the electronic notification for display on a display device. In various embodiments, the electronic notification is provided in the form of a vibration or sound.

FIG. 2 illustrates the use of a sedentary or non-sedentary user state to determine a period of time in which the user state is sedentary according to some embodiments described in this disclosure. As illustrated in fig. 2, the status is illustrated as sedentary or non-sedentary over time (e.g., at each moment of interest, etc.). From sedentary or non-sedentary states, non-overlapping, consecutive time periods are obtained. During each successive time period, the user is in a sedentary state or a non-sedentary state. Each successive time period spans one or more time instants of interest. Also, as illustrated in fig. 2, the continuous time period has different user states, and the continuous time period is described by a task block 3B.

In particular, fig. 2 shows that successive time periods are obtained: sedentary period 252, non-sedentary period 254, and sedentary period 256, each spanning multiple contiguous moments of interest where the user status is the same. Sedentary period 252 contains 6 moments of interest. Each time instant has a state classified as sedentary. In contrast, non-sedentary period 254 contains 5 moments of interest, each with a state classified as non-sedentary. Transitions between the user's states are represented by edges of time periods (e.g., when one time period ends and the next begins, etc.). For example, at the end of the sedentary period of time 252 and at the beginning of the non-sedentary period of time 254, the user's state transitions from a sedentary state to a non-sedentary state.

In some embodiments, the time period detector 122 (FIG. 1A) detects and records alternating time periods in which the user's status is sedentary or non-sedentary. For example, sedentary periods 252 and 256 and non-sedentary period 254, illustrated in fig. 2, are recorded over a span of time (e.g., hours, days, weeks, etc.) to be presented to the user or to perform additional analysis related to the sedentary behavior of the user. The determined time period with the status (e.g., sedentary status, non-sedentary status, etc.) is presented to the user on a display device of the wearable electronic device or a display device of one of the other electronic devices (e.g., tablet, smartphone, computer, etc.) that receives the determined time period and status as data from the wearable electronic device. In some embodiments, one of the other electronic devices generates the determined time period and status. The user then views his/her sedentary behavior as indicated by the determined time period and status, and tracks his/her improvement over time.

In some embodiments, a user shares information about his/her sedentary behavior with friends, colleagues, or team members via a network (e.g., a social network, etc.). Friends, colleagues, or team members compete with each other based on their respective sedentary status as determined by their respective recorded sedentary time periods.

In embodiments, the additional sensor data is used to additionally disambiguate between periods of sedentary time and other periods of time when the user is sleeping and/or not wearing the wearable electronic device. The wearable electronic device is capable of detecting periods of time during which the user is sleeping and/or not wearing the wearable electronic device. For example, one or more sensors 110 may not be able to detect information about the user (e.g., motion sensor data, cardiac sensor data, number of steps taken, etc.). To further illustrate, the one or more sensors detect that the number of steps taken by the user is zero for a period of time when the user is not wearing the wearable electronic device. The time period satisfies the MET measurement or motion-based criteria of sedentary time, but is not sufficient as sedentary because the user is not wearing the wearable electronic device or the one or more sensors 110 detect that sleep. Before or during making the determination of sedentary and non-sedentary states, a processor of the wearable electronic device or a processor of one of the other electronic devices filters out MET measurements during periods of time when the user is not wearing the wearable electronic device (e.g., while sleeping, while bathing, etc., or while sleeping).

The following is an embodiment describing a sedentary state monitor.

Through automatic detection and tracking of a period of time in which the user's status is sedentary, the user receives notifications to encourage him/her to change his/her behavior and be less sedentary. The notification facilitates interrupting a sedentary state for a long time and reduces the overall time that the user is sedentary. Fig. 3 is a block diagram of an embodiment of a sedentary state monitor 124 according to several embodiments described in the present disclosure, for notifying a user based on tracking of a sedentary period to encourage the user to change his/her sedentary behavior and to limit the length of the sedentary period. Sedentary status monitor 124 includes a management unit 310 that receives the status of a user over a period of time. For example, the state is provided at the end of a time period (after which the user's previous state has changed). For example, after a transition between two consecutive states of the user, the current time period and the state of the user are indicated to the management unit 310 to have started. As another example, a state is provided as a current stream of information containing transitions between two consecutive states. As yet another example, the status is provided in batches at regular intervals. For example, the current time period thus reaches X times long, and the management unit 310 detects a transition between two consecutive states during X.

Although FIG. 3 illustrates the non-sedentary state transition manager 320 of the management unit 310, the sedentary alert manager 330 of the management unit 310, and the non-sedentary goal manager 340 of the management unit 310, in some embodiments, the management unit 310 has more, fewer, and/or different types of managers. In embodiments with multiple types of managers, one or some combination of these managers are used at different times to interact with the user, e.g., to encourage the user to change or end sedentary behavior based on the sedentary status of the user by sending a notification to the user through the user's wearable electronic device or through one of the other electronic devices (as discussed in more detail below).

Following are embodiments describing behavior triggered alerts (e.g., non-sedentary state transitions, etc.).

According to several embodiments, for example, upon detecting that the sedentary period has ended and the non-sedentary period has begun (e.g., the user begins moving, etc.), and upon determining that the non-sedentary period exceeds a threshold period, the user is notified on the wearable electronic device (via display of a notification, etc.). While in some embodiments the threshold time periods for all types of activities are the same, in embodiments the threshold time periods for at least some types of activities are different. The user receives a notification (e.g., a message displayed on a display device of the wearable electronic device, a vibration of the wearable electronic device, and/or a congratulatory sound on the wearable electronic device), etc., that notifies the user that he/she has just finished the sedentary period. The notification is intended to encourage the user to remain mobile and remain active to limit the total amount of time in which the user status is sedentary.

Fig. 4 illustrates the delivery of a notification to a user based on the detection of the end of the sedentary period 424 and the beginning of the non-sedentary period 426 (a threshold period of time has been exceeded), according to some embodiments described in this disclosure. In some embodiments, time period detector 122 (fig. 1A) detects a sedentary time period 424 of the user and upon detecting 412 that the state of the user has changed from sedentary to non-sedentary for a threshold time period, time period detector 122 notifies non-sedentary state transition manager 320 (fig. 3), which communicates 414 a notification (e.g., non-sedentary state transition notification information, etc.) to the user's wearable electronic device. As an example, the state of the user is detected to have transitioned from sedentary to non-sedentary when the set of one or more moments of interest contained in the non-sedentary time period 426 satisfies a threshold time period. In some embodiments, the user's status is detected as non-sedentary when the user performs one of various activities (e.g., swimming, jogging, fast walking, strolling, etc.) for a period of time (e.g., 30 seconds, 10 seconds, 1 minute, 3 minutes, etc.).

In embodiments, the time period detector 122 determines the non-sedentary time period 426 based on the status of the user at the time of interest. In some embodiments, the end of the sedentary period 424 is detected when a non-sedentary status of the user is detected.

In several embodiments, the non-sedentary state is detected based on the type of activity the user is performing. For example, when the user runs for 30 seconds (e.g., the user's status is classified as "non-sedentary, running" 30 seconds, etc.), a change in the user's status from sedentary to non-sedentary is detected and the user receives a notification. As another example, when the user runs for at least 10 seconds (e.g., the user's state is classified as "non-sedentary, sprint" for at least 10 seconds, etc.), a change in the user's state from sedentary to non-sedentary is detected and the user receives a notification. As yet another example, when the user walks quickly for 1 minute (e.g., the user's state is classified as "non-sedentary, walking" for 1 minute, etc.), a change in the user's state from sedentary to non-sedentary is detected and the user receives a notification. As yet another example, when the user strolls for at least 3 minutes (e.g., the user's state is classified as "non-sedentary, strolling" for at least 3 minutes, etc.), a change in the user's state from sedentary to non-sedentary is detected and the user receives a notification.

In some embodiments, the time period detector 122 detects a sedentary time period 424 and detects 412 a non-sedentary moment of interest (e.g., one or more moments of interest included in a non-sedentary time period 426, etc.), the time period detector 122 notifies the non-sedentary state transition manager 320, the non-sedentary state transition manager 320 determines that the non-sedentary time period exceeds a threshold time period and communicates 414 a notification to the user's wearable electronic device regarding satisfaction of the threshold time period. The notification that the threshold time period is met indicates that the user has ended the sedentary time period 424 and is now active. The notification is intended to encourage the user to be more active and interrupt the sedentary period more frequently.

In embodiments, the notification that the threshold period of time is met is in the form of a sentence or motivational statement or an active message displayed on a display device of the wearable device. Examples of a non-exhaustive list of exemplary incentive statements indicating whether a threshold time period is met include "do good," "outstanding work," "continue moving," "continue," "don't stop," "go multi-point," "xx hours sedentary" (where xx is how long the user has sedentary); "go on"; "walk xx minutes" (where xx is a value between 1-20, e.g., 2); "you stand up after sitting xx hours yy minutes" (where xx is the hours the user is sitting long and yy is the minutes); "you are in step x. Can you do x + 200? "(where x is the number of steps taken since the end sedentary period); "do you take a very long way? "; "a meeting of walking? "; "starting Bar! "; run successfully! "; let us call you butter because you are rolling! "; "you fire all open! "; "try to move! "; "do not stop moving! | A "; "grab a friend and continue walking! "; "you cannot catch me! "; "do well, you sit for about x hours and now you finally stand up"; "get up"; "walk just like you just robbed a bank"; "keep peak! "; "go to bar"; "move up! "; to do! "; "you done! "; "look healthy! "; "benefit you"; "Tai Zhu! "; score! "; and "Excellent! "and the like.

Next follows a description of embodiments of scrolling alarms.

In embodiments, upon detecting that the user has been sedentary for a threshold amount of time (sometimes referred to herein as a threshold sedentary period), the user is notified by the wearable electronic device. When the user has sedated for an extended period of time, a sedentary alert is delivered to the user to notify him/her for the extended period of time and to encourage him/her to end the extended period of time. A sedentary alarm is a message displayed on a display device of the wearable electronic device, a sound emitted by the wearable electronic device, and/or a vibration of the wearable electronic device. The sedentary alert is intended to encourage the user to start moving and become active to end the sedentary period.

In some embodiments, the threshold sedentary period is 1 hour or 2 hours or 20 minutes or 40 minutes or seconds or minutes or hours. In embodiments, the threshold sedentary time period is configurable, e.g., the user selects the length of the sedentary time window after which he/she would like to be notified to end the sedentary time. In several embodiments, the threshold sedentary period is dynamically adjusted (e.g., decreased or increased) based on various factors, etc., and/or can be automatically disabled by a user and/or manually disabled.

In some embodiments, the user sets preferences for the types of alerts to be received. Such as a user selection of a sound, a particular message to be displayed, and/or a vibration. In embodiments, the user sets sedentary status monitor 124 (fig. 1A) such that the sedentary period is monitored over a specific time interval. For example, a sedentary or non-sedentary period is monitored between the early 8 o 'clock and the late 8 o' clock of the day and is not monitored for the remainder of the day.

In some embodiments, input (e.g., one or more preferences, etc.) is received from a user via an input device of a wearable electronic device (e.g., a keypad, a touch screen of a display device, a stylus, a keyboard, a mouse, etc.), or via an input device of one of the other electronic devices (e.g., a keypad, a touch screen of a display device, a stylus, a keyboard, a mouse, etc.). In embodiments where the input is received at the wearable electronic device and the sedentary state monitor 124 is located internal to one of the other electronic devices, the input is communicated from the communication device of the wearable electronic device to the communication device of the one of the other electronic devices. The communication device of one of the other electronic devices provides input to the sedentary status monitor 124 of one of the other electronic devices. Examples of communication devices include devices that apply bluetooth protocol, Internet Protocol (IP), ethernet protocol, transmission control protocol/IP (TCP/IP) protocol, universal serial bus protocol, serial transport protocol, parallel transport protocol, and the like. In embodiments where the input is received at one of the other electronic devices and sedentary state monitor 124 is located inside the wearable electronic device, the input is communicated from a communication device in the other electronic device to the communication device of the wearable electronic device. The wearable electronic device's communication device provides input to the wearable electronic device's sedentary status monitor 124.

FIG. 5 illustrates delivery of a sedentary alert to a user based on detection of a sedentary time period exceeding a sedentary time period threshold according to embodiments described in the present disclosure. The time period detector 122 (fig. 1A) detects 516 a sedentary time period 528 for the user and upon detecting 516 that the sedentary time period 528 exceeds a threshold sedentary time period Δ Τ, the sedentary alert manager 330 transmits 518 a sedentary alert (e.g., a sedentary alert notification message, etc.) to the wearable electronic device of the user. The sedentary alert indicates that the user has spent more than the Δ T period in a sedentary state and encourages him/her to end the sedentary period 528 by performing more aggressive tasks (e.g., walking, running) or performing more energy intensive physical activities than sedentary, etc. The sedentary alarm is intended to inform the user of his/her sedentary behavior and to encourage him/her to be more active. In some embodiments, the sedentary alert is in the form of a vibration of the wearable electronic device or a sound emitted by the wearable electronic device through a speaker of the wearable electronic device or a message displayed on a display device of the wearable electronic device.

A non-exhaustive list of sedentary alarms includes: "is a time-to-start and a cheer! "; "how do you go? "; "move up"; "Do willing to walk"; "move those muscle bars! "; "let us move up"; "please stand up"; "how do you go once? "; "stepping on! "; "take a rest"; "extend leg bar"; "you have been stationary for xx minutes" (where xx is the time the user has been sedentary); "where you are today is your thought decision! "; "Care your body"; get up! "; "do not sit there alone"; making a regular script model; "get up, stand up"; get up, build body! "; "you have sat for 1 hour"; "what time you know! "; ' wo! Let us walk down! "; "number of steps given"; "move never late! "; "walk up at the time"; let us move down "; move! Move! Move! "; "I are bored. Let us shake up! "; "get them! "; "you can do anything you let your mind go"; "now fly! "; "I dare to say you want to move! "; "please add several more steps"; "move your buttocks"; "get up"; "stretch"; "walk"; "get up to go"; "walk one go"; "grab a friend, walk one go"; "when sitting at present, sit for a long time! "; "I believe you can fly! "; "what you do today makes me feel pride? "; "I want to run"; "grab do today! "; run out! "; "I am behind you! "; "the English people came from"; "Puck, your blood sugar is rising"; "close mouth and go up"; "number of aspirations steps"; "error: too low a number of steps "; "step error"; "a feeling that you have forgotten to walk? "; "if you are my Fitbit, I will walk you"; "has been for a while"; "is time! "; "move up"; "stop making Stand erect ear"; "jump up"; "jump like a rabbit alone"; "it is time again! "; "let us get some water"; "let us go to tour"; "is willing to walk? "; "foot bar extension! "" stretch "; "walk two streets"; "go out now"; "walk out"; "left right left! "; and "let us find some stairs! "and the like. In some embodiments, the user alternatively or additionally receives one or more icons and/or one or more animated images (e.g., an animated foot, an animated step, etc.) displayed on a display device of the wearable electronic device and one or more icons and/or one or more animated images indicating that the user is sedentary greater than a threshold sedentary time period Δ Τ.

In embodiments, upon receiving a sedentary alert, the user ends the sedentary period. In several embodiments, upon receiving a sedentary alert, the user remains sedentary and continues to receive sedentary alerts from sedentary status monitor 124 at regular intervals to encourage him/her to move. For example, the user receives a sedentary alarm every hour.

In some embodiments, if the user ends the sedentary period 528, the user receives a congratulatory message (also sometimes referred to herein as a celebratory message) from the sedentary status monitor 124 via the wearable electronic device to encourage him/her to attempt to end the sedentary period 528 (as described above). The celebration message is one of the celebration messages described further below.

An embodiment describing a mini target alert follows.

In some embodiments, the user is encouraged to achieve a mini goal (e.g., 250 steps, or 15 minutes of continuous non-sedentary time of interest, etc.) during a predetermined time window. The mini goal is a step towards achieving a predetermined goal. Non-sedentary goal manager 340 (fig. 3) tracks the user's sedentary time period and non-sedentary activities, and non-sedentary goal manager 340 interacts with the wearable device's display device and sends one or more notifications (e.g., mini-goal notification information, etc.) to the wearable device's display device, providing information about the user's progress in reaching the mini-goal. For example, non-sedentary goal manager 340 sends a progress indication to inform the user of the remaining activity to perform to achieve the mini-goal prior to the end of the predetermined time window via vibration of the wearable electronic device and/or a message displayed on a display device of the wearable electronic device and/or a sound emitted by a speaker of the wearable electronic device. A non-exhaustive exemplary list of notifications and messages that a user receives as part of a mini-target includes "also xx steps"; "xx Steps! "; "left xx step"; "go xx steps before 3 pm"; "xx Steps to Per hour target"; "10 minutes to go through xx Steps! "; "walk xx/yy steps, walk xx steps! "; "count per step! Go through xx steps during this hour! "; only go xx steps until yy "; and "walk xx (animated foot/step)", where xx is replaced with the number of remaining steps and yy is replaced with the total number of steps set to achieve the mini goal.

In some embodiments, rather than receiving an indication of how many steps remain or the remaining length of activity to achieve the mini-goal, the user receives a notification (e.g., a message, mini-goal notification information, etc.) via vibration of the wearable electronic device and/or a message on a display device of the wearable electronic device and/or a sound emitted by a speaker within the wearable electronic device, wants him/her to begin becoming active, e.g., walking, running, etc., and later receives a "celebration message" that achieves the mini-goal via vibration of the wearable electronic device and/or a message on a display device of the wearable electronic device and/or a sound emitted by a speaker within the wearable electronic device. For example, non-sedentary goal manager 340 determines that a mini-goal is achieved and provides a notification to the user that the mini-goal is achieved via a vibration of the wearable electronic device and/or a message on a display device of the wearable electronic device and/or a sound emitted by a speaker within the wearable electronic device. The mini-object is sometimes referred to herein as a mini celebration. For example, the mini celebration is "hum + smile" when the user reaches yy steps during a predetermined time window set for the mini-object (e.g., during 1 hour, etc.). The hum is an example of vibration of the wearable electronic device. In some embodiments, the wearable electronic device includes a haptic feedback device that vibrates to provide haptic feedback to the user to provide a notification to the user.

While in some embodiments the predetermined time window to achieve a mini goal (e.g., a non-sedentary goal, etc.) is 1 hour, in embodiments, a different predetermined time window is used (e.g., about 10 minutes to 6 hours, or about 20 minutes to 3 hours, every 2 hours, etc.). In several embodiments, the predetermined time window for achieving the mini-goal (e.g., non-sedentary goal, etc.) is configurable, e.g., the user selects the length of the predetermined time window for achieving the mini-goal by setting preferences in the manner described above. In some embodiments, the predetermined time window for achieving the mini-goal is dynamically adjusted (e.g., decreased or increased) by sedentary status monitor 124 based on various factors and the like. In some embodiments, the predetermined time window for achieving the mini-goal can be disabled automatically by the sedentary status monitor 124 and/or manually by the user.

In embodiments, the user additionally determines preferences regarding the timing of receiving his/her notifications and reminders of progress toward the mini-goals and provides the preferences to sedentary status monitor 124 via an input device of the wearable electronic device or via an input device of one of the other electronic devices. For example, the user wants to receive a notification a few minutes before the end of the predetermined time window (e.g., 50 minutes within an hour, etc.) before the mini-objective is achieved. The notification contains information indicating the mini-goal to be achieved and the remaining activity (e.g., number of non-sedentary minutes, steps, etc.) to be performed to achieve the mini-goal. If the user completes the mini-goal before the end of the predetermined time window, the user receives the reward message from sedentary state monitor 124 and that realized reward from sedentary state monitor 124.

Herein, a non-exhaustive exemplary list of celebration messages received by a user to achieve a mini goal is presented as: ". major; "do well! "; ": -D: -D: -D "; ": ): ): ): ) "; "xx/yy! | A | A "; one hour more! "; "winner"; "winner chicken dinner"; the champion! Champion! "; under "xx! "; "very good! "; "every more step is important! "; "you is a treadmill"; "you is the fire full open! "; "you are extremely good! "; "number of steps per hour champion"; "xx steps are not even so great"; and "my hero" where xx is replaced by the number of steps completed during the predetermined time window assigned to the mini goal and yy is replaced by the number of steps set to achieve the mini goal. Additionally, in some embodiments, users compete with friends via social networks for the most mini-goals.

In embodiments, the non-sedentary goal manager 340 tracks and records the mini-goals set and achieved by the user and presents the set and/or achieved mini-goals to the user. The mini-object is presented to the user on a display device of one of the wearable electronic device or other electronic device, the display device receiving the mini-object from the wearable electronic device via a communication device of the wearable electronic device and a communication device of the one of the other electronic devices. The user then looks at his/her sedentary behavior and tracks his/her improvement in achieving the mini goal over time.

In several embodiments, sedentary alert manager 330 differs from non-sedentary target manager 340 in that sedentary alert manager 330 operates on the current sedentary period that begins when the user's state transitions to sedentary, while non-sedentary target manager 340 closes the set time window, regardless of whether the state is sedentary at the beginning of each time window. As discussed previously, two or more managers are used in combination to interact with the user via the sedentary state monitor 124 and wearable electronics to change his/her sedentary behavior.

Next follows a description of embodiments of learning alarms.

In some embodiments, sedentary learning unit 350 (FIG. 3) of sedentary state monitor 124 is coupled to managers 320, 330, and 340 and receives notification information (e.g., one or more notifications, etc.) sent from each of user managers 320, 330, and 340 to the user via sedentary state monitor 124 (FIG. 1A) and determines which of the one or more notifications affect modifying the sedentary behavior of the user. For example, the sedentary learning unit 350 determines which of the one or more notifications successfully changed the user's general sedentary behavior by limiting the length of the sedentary period.

While in some embodiments each of managers 320, 330, and 340 transmit notification information regarding the time to send one or more notifications (e.g., non-sedentary state transition notification information, sedentary alert notification information, mini target notification information, etc.), in embodiments managers 320, 330, and 340 transmit more, less, or different data as part of the notification information. For example, managers 320, 330, and 340 transmit one type of notification (e.g., a message, vibration, and/or sound) to the sedentary learning unit 350. As another example, the managers 320, 330 and 340 transmit information about the result of the notification sent to the user (e.g., whether the user has ended his/her sedentary period, etc.) to the sedentary learning unit 350.

In some embodiments, the sedentary learning unit 350 receives sensor data 150 (fig. 1A) from one or more sensors 110 (fig. 1A) to determine whether the communicated notification affects modifying the sedentary behavior of the user. The sedentary learning unit 350 records the sensor data 150 over time to learn what type of notification (e.g., personality or tone of the message, etc.) and what content (e.g., time, location, etc.) has a desired impact on the user. Examples of desirable effects include a reduction in long sedentary periods, a reduction in the number of sedentary states over time, and the like. The sedentary learning unit 350 determines improved preferences and settings (e.g., configuration parameters, etc.) for configuring at least one of the managers 320, 330, and 340 based on the received notification information.

The sedentary learning unit 350 learns the user's behavior in response to notifications from the managers 320, 330, and 340 and improves the user's response to the notifications in response to the user's behavior. For example, the sedentary learning unit 350 changes the configuration of one of the managers 320, 330, and 340 (e.g., by transmitting configuration parameters to that manager, etc.) to change to the time of day when the user can respond to the notification, when the sedentary learning unit 350 determines that the user does not respond to the notification at another particular time of day. As another example, the sedentary learning unit 350 changes the configuration of the sedentary alert manager 330 by modifying the length of the threshold sedentary period after sending the notification to the user. As yet another example, the sedentary learning unit 350 modifies the type of notification sent to the user, e.g., one of the configuration managers 320, 330, or 340 sends a message that will be displayed on the display of the wearable electronic device in place of a vibration alert (e.g., buzzing, etc.) or produces an emission of a sound by a speaker of the wearable electronic device in place of a vibration or changes the sound emitted or changes the tone of the message or modifies the type of notification.

In some embodiments, the sedentary learning unit 350 changes a plurality of configuration parameters such that one of the managers 320, 330, and 340 operates at a given time of day. For example, sedentary learning unit 350 determines that between certain hours of the day (e.g., 8 am to 12 pm, etc.), the user's response to notifications received from non-sedentary state transition manager 320 is better than the user's response to notifications received from sedentary alert manager 330. In this example, the sedentary learning unit 350 determines configuration parameters that prohibit use of the sedentary alert manager 330 during those hours (e.g., 8 am to 12 pm, etc.). While the sedentary alert manager 330 and the non-sedentary state transition manager 320 are described in this example, in embodiments, the sedentary learning unit 350 determines configuration parameters to disable or enable another manager (e.g., the non-sedentary goal manager 340, etc.), and/or to determine other hours of the day during which the managers 320, 330, 340 are configured. While in several embodiments, the sedentary learning unit 350 changes the configuration of at least one of the managers 320, 330, 340, in some embodiments, the sedentary learning unit 350 transmits a suggestion of the configuration of at least one of the managers 320, 330, 340 to a display device of the wearable electronic device for approval by the user prior to the configuration of at least one of the managers 320, 330, 340 having the changed configuration parameters.

In embodiments, the sedentary learning unit 350 allows the user to snooze the sedentary alert notification information so that the wearable electronic device later reminds the user to perform a non-sedentary activity. The sedentary learning unit 350 records data relating to the action of the alarm stay alert notification information performed by the user via an input device of the wearable electronic device or an input device of one of the other electronic devices. For example, data related to the action of an alarm-off includes the type of notification of the alarm-off, the time of the alarm-off notification, the state of the user at that time (e.g., sedentary, non-sedentary, etc.), the geographic location of the alarm-off notification, and the like. The sedentary learning unit 350 uses the data relating to the action to change the configuration of one or more of the managers 320, 330 and 340. For example, if a user stops at one or more of managers 320, 330, and 340 at a particular time of day, the configuration is changed in the manager to avoid operation during that time. This serves to improve the user experience and instills greater confidence in the wearable electronic device. In some embodiments, the sedentary learning unit 350 is implemented implementing one or more of the following: decision trees, random forests, support vector machines, neural networks, K-nearest neighbors, naive Bayes, and hidden Markov models.

In embodiments, the user is able to set preferences based on the type of notifications he/she receives for sedentary behavior. For example, a user can select a subset of subunits of sedentary state monitor 124 (e.g., non-sedentary state transition manager 320, sedentary alert manager 330, non-sedentary goal manager 340, etc.) via an input device of a wearable electronic device or an input device of one of the other electronic devices for notifying the user based on his/her sedentary behavior. Further, the user is able to select a sound, a particular message to be displayed on the display device of the wearable electronic device, and a vibration for each type of message received on the wearable electronic device. The user selects a combination of types of notifications that are received simultaneously. The user sets the sedentary status monitor 124 such that the sedentary period is monitored over a specific time interval via an input device of the wearable electronic device or an input device of one of the other electronic devices. For example, a user wants to monitor a sedentary or non-sedentary period between 8 am and 8 pm of a day.

Following is an embodiment describing classification of the user's state based on MET measurements.

In some embodiments, MET measurements are used to determine sedentary status or the status of a non-sedentary user. Thus, the MET measurement is sometimes referred to as the sedentary coefficient. The user status classifier 120 (fig. 1A) receives the MET measurement at the time of interest and determines whether the MET measurement is less than a predetermined threshold. When the MET measurement is less than a predetermined threshold, the user state classifier 120 classifies the user state for that moment of interest as a sedentary state. When the MET measurement is greater than a predetermined threshold, the user state classifier 120 classifies the user state for that moment of interest as non-sedentary and the user state classifier 120 determines the user as active.

Fig. 6A illustrates a recording of MET measurements at successive moments of interest over a period of time and a classification of user states 612 at each moment of interest by the user state classifier 120 (fig. 1A) based on the MET measurements, according to some embodiments described in the present disclosure. According to several embodiments described in this disclosure, the user state classifier 120 classifies 614 the user state 612 at the time of interest as non-sedentary based on the MET measurement at that time of interest exceeding a threshold MET value. Sensor data analyzer 112 (FIG. 1A) generates a plurality of moments of interest (e.g., F)₁、F₂…F_NEtc.), and the user status classifier 120 compares each MET measurement (e.g., MET value, etc.) to a threshold MET value for a determination 630 of a sedentary status. The MET measurements 624 are all less than the threshold MET value (determined by the user status classifier 120), and the user status classifier 120 classifies 614 each moment of interest of the MET measurements 624 and records within a memory device (e.g., a computer-readable medium of one of the wearable electronic devices or other electronic devices, etc.) as a sedentary state. In contrast, MET measurements 62 all exceed the threshold MET value (determined by user status classifier 120), and user status classifier 120 records each moment of interest of MET measurement 626 as a non-sedentary state within a memory device. The user state classifier 120 associates the MET measurements 628 with the sedentary and non-sedentary moments of interest. For example, both MET measurements 628 are greater than a threshold MET value (determined by user status classifier 120), and user status classifier 120 identifies each time instant of interest 628B of both MET measurements 628 as a non-sedentary state. Two other illustrated MET values of MET measurements 628 are less than the threshold MET value (determined by user state classifier 120). User state classifier 120 identifies each time of

interest

628A and 628C of the two other MET values as a sedentary state. In some embodiments, the threshold MET value is in the range of 0.8-1.8MET (e.g., 1.5MET, etc.). The classification 614 of the moment of interest illustrated at FIG. 6A results in sedentary and non-sedentary moments of interest illustrated in FIG. 2.

Fig. 6B illustrates a recording of MET measurements at successive moments of interest over a period of time and a classification of user states by the user state classifier 120 (fig. 1A) at each moment of interest based on the MET measurements, according to embodiments described in the present disclosure. The state of the user at a time of interest is classified as non-sedentary based on the MET measurement at that time of interest exceeding a first threshold MET value. The state of the user at a time of interest is classified as sedentary based on the MET measurement at that time of interest being less than a second threshold MET value. Further, if the MET value exceeds the second threshold MET value, is less than the first threshold MET value, and is otherwise a moment of interest with a sedentary state, the state of the user at the moment of interest is classified as sedentary. In some embodiments, a set of N consecutive time instants of interest is classified as sedentary if the MET measurement associated with each time instant is between the first and second threshold MET values and the set of N consecutive time instants of interest is immediately before and after the time instant of interest with sedentary status. Examples of N consecutive time instants of interest include time instants of interest, each occurring at 1 minute intervals (e.g., where N is between 1 and 5), or each occurring at intervals between 1 minute and 5 minutes, or each having a 1 second interval (e.g., where N is between 1 and 300), or occurring at intervals between 1 second and 300 seconds, etc. If N moments of interest occur at longer intervals (e.g., every 10 minutes, etc.), then N is smaller (e.g., 2, etc.). In the embodiments discussed above, the second threshold MET value is smaller than the first threshold MET value.

The user state classifier 120 compares each MET measurement generated by the sensor data analyzer 112 to a first threshold MET value (for recording the non-sedentary state 632) and to a second threshold MET value (for recording the sedentary state 634). The user state classifier 120 performs the recording of the non-sedentary state 632 and the sedentary state 634 in the memory device. The MET measurements 646 are all greater than the first threshold MET value, and the user state classifier 120 records each time instant of interest for the MET measurements 646 determined by the user state classifier 120 to have a non-sedentary state. The MET measurements 644 are all less than the second threshold MET value (determined by the user state classifier 120), and the user state classifier 120 records each moment of interest of the MET measurements 644 with sedentary status. In contrast, some MET measurements 648 exceed the second threshold MET value but are less than the first threshold MET value (determined by the user status classifier 120), while other MET measurements 648 are less than the second threshold MET value (determined by the user status classifier 120). A first time of interest of a set of contiguous time of interest of the MET measurements 648 has a MET value less than a second MET threshold value (determined by the user state classifier 120), while a second time of interest of the MET measurements 648 and a third time of interest have a MET value between the first and second threshold MET values (determined by the user state classifier 120), immediately followed by a time of interest having a MET value less than the second threshold MET value. In this example, the user state classifier 120 determines all of the moments of interest of the MET measurements 648 as having a sedentary state, even though two moments within the set of contiguous moments have MET measurements that exceed the second threshold MET value.

As described above, in some embodiments, MET measurements determine the status of a non-sedentary user associated with a particular type of activity of the user. For example, from the MET measurements, the user status classifier 120 determines whether the user is running, walking, sprinting, cycling, swimming, or performing another type of non-sedentary activity. For additional illustration, if the MET measurement is in the range of 2.5 to 3.2, the user's state is classified as "non-sedentary, bicycling". As another example, if the MET measurement is in the range of 3.2 to 3.8, the user's state is classified as "non-sedentary, walking". As yet another example, if the MET measurement is between 6.7 and 7.3 (e.g., 7.0, etc.), the state of the user is classified as "non-sedentary, jogging.

Following is an embodiment describing classification based on other sensor information.

In some embodiments, user state classifier 120 determines a sedentary state of the user at a time of interest based on sensor data 150 (fig. 1A) (e.g., motion sensor data and/or biometric data, etc.) received from one or more sensors 110 (fig. 1A) without generation of MET measurements. For example, the sedentary state of the user is determined based on a calculation of motion measurements (e.g., also sometimes referred to as movement measurements, etc.) and/or heart rate measurements without MET measurements.

Following is a description of an exemplary apparatus with automatic detection of a sedentary or non-sedentary status of a user and providing notification to the user based on the sedentary status.

As previously described, while in some embodiments one or more of the operations described above are implemented in a wearable electronic device, in various embodiments one or more operations are distributed among electronic devices, e.g., wearable electronic devices and other electronic devices, etc. Fig. 7 illustrates an example of one such distribution. Fig. 7 is a block diagram illustrating a wearable electronic device 702 and an electronic device 700 that implement the operations disclosed in accordance with various embodiments described in the present disclosure. Electronic device 700 is an example of one of the other electronic devices. Wearable electronic device 702 includes a processor 742 and one or more sensors 110. In some embodiments 2, multiple processors are used in wearable electronic device 702 instead of processor 742.

In some embodiments, the one or more sensors 110 include motion sensors 727, examples of which include multi-axis accelerometers, gyroscopes, gravity sensors, rotation vector sensors, and magnetometers. Further, in various embodiments, the one or more sensors 110 include one or more other sensors 714 (including a photoplethysmography sensor 720). In several embodiments, the one or more other sensors 714 include a temperature sensor 721, an ambient light sensor 722, a galvanic skin response sensor 723, a capacitance sensor 724, a humidity sensor 725, and a sound sensor 726.

Wearable electronic device 702 also includes a non-transitory machine-readable storage medium 718 that includes sensor data analyzer 112 as discussed herein above. When executed by processor 742, sensor data analyzer 112 causes wearable electronic device 702 to generate analyzed sensor information 152 for a moment of interest. Wearable electronic device 702 performs functionality relating to user state classifier 120, period of time detector 122, and/or sedentary state monitor 124, some or all of which are contained in a Sedentary Tracking and Notification Module (STNM)750, with STNM 750 stored in a non-transitory machine-readable storage medium 718. When executed by processor 742, STNM 750 causes wearable electronic device 702 to perform the corresponding operations discussed herein above. Wearable electronic device 702 also includes user interface 126 with display device 732. Examples of the display device include a Liquid Crystal Display (LCD) display device, a Light Emitting Diode (LED) display device, a plasma display device, and the like. In some embodiments, user interface 126 includes a speaker, a haptic screen, and/or a vibration mechanism (e.g., a haptic communication device, a joystick, a somatosensory communication device, etc.) to allow communication and interaction with a user wearing wearable electronic device 702.

In some embodiments, one or more other sensors 714 are not placed inside wearable electronic device 702. One or more other sensors 714 are distributed around the user. For example, one or more other sensors 714 are placed on the user's chest or on a mattress on which the user lies or on a bedside table on which the user is located, while the user is wearing wearable electronic device 702.

Fig. 7 also includes an embodiment of an electronic device 700, such as a server, tablet, smartphone, etc. (including applications) that include hardware and software. In some embodiments, electronic device 700 performs functionality relating to user state classifier 120, period of time detector 122, and/or sedentary state monitor 124, some or all of which are contained in STNM 750, with STNM 750 stored in non-transitory machine-readable storage medium 748 of electronic device 700. For example, STNM 750 is stored in a non-transitory machine-readable storage medium 748 of electronic device 700 for execution by processor 752 of electronic device 700, rather than in non-transitory machine-readable storage medium 718 of wearable electronic device 702. In some embodiments, sensor data analyzer 112 is stored in non-transitory machine-readable storage medium 748 (rather than non-transitory machine-readable storage medium 718) and executed by processor 752.

When executed by processor 752, STNM 750 causes electronic device 700 to perform the corresponding operations discussed herein above. In some embodiments, electronic device 700 includes Virtual Machines (VMs) 762A-762R, each executing

software instance

766 or 768 of STNM 950. Hypervisor 754 presents a virtual operating platform for virtual machines 762A through 762R.

Wearable electronic device 702 collects one or more types of sensor data 150 (e.g., biometric data, etc.) from one or more sensors 110 and/or external devices, and then utilizes sensor data 150 in various ways. Examples of biometric data include data pertaining to physical characteristics of the human body (e.g., heart beat, heart rate, perspiration level, etc.). Other examples of sensor data 150 include data related to the human body and the physical interaction of the environment (e.g., accelerometer readings, gyroscope readings, etc.). Examples of external devices include external heart rate sensors or monitors, e.g., chest belt heart rate sensors or monitors, etc. Examples of variously utilizing the sensor data 150 include making calculations based on the sensor data 150, storing calculations in a non-transitory machine-readable storage medium 718, automatically acting on the sensor data 150, automatically acting on calculations, communicating the sensor data 150 over a computer network (e.g., the internet, a wide area network, a local area network, etc.) to a communication device (e.g., one or more network interface controllers 744, etc.) of the electronic device 700, and communicating calculations over the computer network to the communication device. Examples of automatically acting on the computation include automatic watch checking and hand-waving gesture detection. As described herein, wearable electronic device 702 also receives data (e.g., notifications, etc.) from one of the other electronic devices for storage and/or display on display device 732.

In some embodiments, the electronic device 700 includes a display device for presenting any of the notifications described herein, such as non-sedentary state transition notification information, sedentary alert notification information, mini-target notification information, and the like, received from the wearable electronic device 702. For example, sedentary status monitor 124 of wearable electronic device 702 generates a notification and sends the notification to a display device of electronic device 700 for display on display device 700 via a communication device of wearable electronic device 702 and a communication device of electronic device 700.

In various embodiments, sensor data 150 is acquired by wearable electronic device 702 and sent to STNM 750 of electronic device 700 via a communication device of wearable electronic device 702 and a communication device of electronic device 700 for performing the operations described herein.

In several embodiments, the notification is a display of the notification generated by the electronic device 700 and sent from the one or more network interface controllers 744 to the communication device of the wearable electronic device 702 via a computer network for display on the display device 732. In embodiments, sedentary status monitor 124 of electronic device 700 generates and sends a notification to display device 732 for display on display device 732 via a communication device of electronic device 700 and a communication device of wearable electronic device 702.

Fig. 8 is a block diagram of an embodiment of a wrist-worn electronic device having buttons, a display, and a wristband for securing the wrist-worn electronic device to a user's forearm according to several embodiments described in the present disclosure. For example, fig. 8 depicts a wearable electronic device 702, such as illustrated in fig. 7, and worn on a user's forearm, like a wristwatch. In fig. 8, the wrist-worn electronic apparatus has a housing 802 containing electronic equipment (e.g., components illustrated in fig. 7, etc., associated with the wrist-worn electronic apparatus), buttons 804, and a display screen 806 that is touchable or viewable through the housing 802. Display screen 806 is display device 732 (fig. 7). Wristband 808 is integrated with housing 802.

In some embodiments, the wrist-worn electronic device incorporates one or more user interfaces including, but not limited to, visual, audible, touch/vibration, or a combination thereof. In some embodiments, the wrist-worn electronic device provides tactile feedback (e.g., vibration by a motor). In some implementations, one or more sensors 110 (fig. 1A) are used as part of one or more user interfaces, e.g., an accelerometer sensor is used to detect when a user clicks on the housing 802 of the wrist-worn electronic device with a finger or other object and then interprets such data as user input for controlling the wrist-worn electronic device. For example, the wrist-worn electronic device recognizes a double tap of the housing 802 of the wrist-worn electronic device as a user input.

Although fig. 8 illustrates a wrist-worn electronic device implementation, in some embodiments, the wrist-worn electronic device has other shapes and sizes for coupling to (e.g., securing, wearing, carrying, etc.) a user's body or clothing. For example, wrist-worn electronic devices are designed to be inserted into and removed from a plurality of compatible housings or shells or supports, such as a wrist band worn on the forearm of a user or a back clip shell attached to the user's clothing. As used herein, the term "wristband" refers to a band designed to completely or partially encircle a user's forearm proximate the wrist joint. The tape may be continuous (e.g., without any breaks) or discontinuous or simply open. Examples of continuous bands include bands that stretch to conform to a user's hand or have extensions similar to a watch band. Examples of discontinuous bands include clasps or other attached bands (similar to wrist straps) that allow the band to be closed. An example of an open band is one having a C-shape that clasps the user's wrist.

It should be noted that in some embodiments, the user accesses information (e.g., notifications, etc.) after logging into the user account. For example, the user provides his/her user information (e.g., user name, password, etc.), and the user logs into the user account when the server authenticates the user information. In these embodiments, the notification is published within the user account. The user account is stored on the server.

In some embodiments, the user accesses the user account to view the graphs illustrated in fig. 2, 4, 5, 6A, and 6B. The map is viewed on a display device of the wearable electronic device or a display device of one of the other electronic devices.

It should be noted that in an embodiment, one or more features from any embodiment described herein are combined with one or more features of any other embodiment described herein without departing from the scope of the embodiments described in this disclosure.

Various embodiments of the present disclosure may be described according to the following clauses:

1. a method, comprising: receiving, by a server, a sample of sensor information acquired from a wearable electronic device at a plurality of moments of interest, the sensor information generated while a user associated with the wearable electronic device performs one or more activities, the sample being a metabolic equivalence measure of a normalized task; classifying, by the processor of the server, each of the time of interest samples as at least one of a sedentary state and a non-sedentary state based on comparing each of the time of interest samples to a predetermined threshold; detecting, by the processor, a time period during which a plurality of consecutive ones of the samples indicate the sedentary state; determining, by the processor, whether the time period is greater than a threshold time period; and identifying, by the processor, that the user is in a sedentary state upon determining that the time period is greater than the threshold time period.

2. The method of clause 1, further comprising: generating an electronic notification in response to identifying that the user is in the sedentary state; and sending the electronic notification from the server to the wearable electronic device via a computer network for presentation of the electronic notification on the wearable electronic device, wherein the electronic notification includes a message advising the user to end the sedentary state.

3. The method of clause 1, further comprising: determining that a value of one of the samples of one of the time instants of interest is less than the predetermined threshold; and classifying said one of said samples as having said sedentary state in response to determining that said value is less than said predetermined threshold.

4. The method of clause 1, wherein each of the samples is classified as having the sedentary state or the non-sedentary state or a sleep state.

5. The method of clause 1, wherein each of the samples is classified as having the sedentary state or the non-sedentary state or a state indicating that the user is not wearing the wearable electronic device.

6. The method of clause 1, further comprising: detecting, by the processor, a time period during which a plurality of consecutive ones of the samples indicate the non-sedentary state; determining, by the processor, that the plurality of consecutive ones of the samples indicate that the time period of the non-sedentary state is greater than a predetermined time period; and identifying, by the processor, that the user is in the non-sedentary state upon determining that the plurality of consecutive ones of the samples indicate that the time period of the non-sedentary state is greater than the predetermined time period.

7. The method of clause 6, further comprising: determining, by the processor, that a time period during which a plurality of consecutive ones of the samples indicate the non-sedentary state is adjacent to a time period during which a plurality of consecutive ones of the samples indicate the sedentary state; identifying, by the processor, that the user has transitioned from the sedentary state to the non-sedentary state upon determining that a period of time during which a plurality of consecutive ones of the samples indicated the non-sedentary state is adjacent to a period of time during which a plurality of consecutive ones of the samples indicated the sedentary state; and upon identifying that the user has transitioned to the non-sedentary state, sending an electronic notification from the server to the wearable electronic device via a computer network for display via the wearable electronic device, the electronic notification including an incentive message.

8. The method of clause 1, wherein the metabolic equivalent measurement of the task is a measurement of energy expenditure, wherein each of the metabolic equivalent measurements of the task is non-zero.

9. The method of clause 1, further comprising: determining that a value of one of the samples for one of the time instants of interest is greater than the predetermined threshold; and classifying the one of the samples as having the non-sedentary state in response to determining that the value is greater than the predetermined threshold.

10. The method of clause 1, further comprising: determining that a value of one of the samples of one of the time instants of interest is greater than a second predetermined threshold value higher than the predetermined threshold value; and classifying said one of said samples as having said non-sedentary state in response to determining that said value is greater than said second predetermined threshold.

11. The method of clause 1, further comprising: determining that a value of a particular sample of the samples for a particular time instant of interest is greater than the predetermined threshold and less than a second predetermined threshold, the second predetermined threshold being higher than the predetermined threshold; determining that a value of one of the samples of the preceding time of interest that precedes the particular time of interest corresponds to the sedentary state; determining that a value of one of the samples at a later time of interest that is later than the particular time of interest corresponds to the sedentary state; and classifying the particular one of the samples as having the sedentary state.

12. The method of clause 1, further comprising: determining that values of a set of consecutive samples of the time instance of interest are each greater than the predetermined threshold and less than a second predetermined threshold, the second predetermined threshold being higher than the predetermined threshold; determining that a value of one of the samples of a preceding time of interest prior to the set of consecutive times of interest corresponds to the sedentary state; determining that a value of one of the subsequent time-of-interest samples subsequent to the set of consecutive time-of-interest corresponds to the sedentary state; and classifying each of the samples of the set of consecutive time instants of interest as having the sedentary state.

13. The method of clause 12, further comprising: determining that a size of the set of consecutive time instants of interest is less than a size threshold before classifying each of the samples of the set of consecutive time instants of interest as having the sedentary state.

14. The method of clause 1, wherein the moments of interest occur at regular intervals.

15. A method, comprising: receiving, by a server, a sample of sensor information acquired from a wearable electronic device at a plurality of moments of interest, the sensor information generated while a user associated with the wearable electronic device performs one or more activities, the sample being a metabolic equivalence measure of a normalized task; classifying, by a processor of the server, a particular sample of the samples for a particular one of the moments of interest as sedentary based on determining that a value of the particular sample is between a first threshold and a second threshold and that the particular one of the moments of interest is preceded by a first moment of interest at which the user state is classified as sedentary and that the particular one of the moments of interest is followed by a second moment of interest at which the user state is classified as sedentary; detecting, by the processor, a time period during which a plurality of consecutive ones of the samples indicate the sedentary state; determining, by the processor, whether the time period is greater than a threshold time period; and identifying, by the processor, that the user is in a sedentary state upon determining that the time period is greater than the threshold time period.

16. A system, comprising: a communication device configured to receive a sample of sensor information obtained from a wearable electronic device at a plurality of moments of interest, the sensor information generated when a user associated with the wearable electronic device performs one or more activities, the sample being a metabolic equivalence measure of a normalized task; and a processor coupled to the communication device, the processor configured to classify each of the time of interest samples as at least one of sedentary and non-sedentary based on comparing each of the time of interest samples to a predetermined threshold; the processor is configured to detect a period of time during which a plurality of consecutive ones of the samples indicate the sedentary state; the processor is configured to determine whether the time period is greater than a threshold time period; and the processor is configured to identify that the user is in a sedentary state upon determining that the period of time is greater than the threshold period of time.

17. The system of clause 16, wherein the processor is further configured to generate an electronic notification in response to identifying that the user is in the sedentary state, wherein the communication device is configured to send the electronic notification to the wearable electronic device via a computer network for presentation of the electronic notification on the wearable electronic device, wherein the electronic notification comprises a message suggesting that the user end the sedentary state.

18. The system of clause 16, wherein the processor is further configured to determine that a value of one of the samples for one of the time instants of interest is less than the predetermined threshold, wherein the processor is further configured to classify the one of the samples as having the sedentary state in response to determining that the value is less than the predetermined threshold.

19. The system of clause 16, wherein each of the samples is classified as having the sedentary state or the non-sedentary state or a sleep state.

20. The system of clause 16, wherein each of the samples is classified as having the sedentary state or the non-sedentary state or a state indicating that the user is not wearing the wearable electronic device.

21. The system of clause 16, wherein the processor is configured to: detecting a time period during which a plurality of consecutive ones of the samples indicate the non-sedentary state; determining that the period of time for which the plurality of consecutive ones of the samples indicate the non-sedentary state is greater than a predetermined period of time; and identifying that the user is in a non-sedentary state upon determining that the time period over which the plurality of consecutive ones of the samples indicate the non-sedentary state is greater than the predetermined time period.

22. The system of clause 16, wherein the processor is configured to: determining that a period of time during which a plurality of consecutive samples of the samples indicate the non-sedentary state is adjacent to a period of time during which a plurality of consecutive samples of the samples indicate the sedentary state, wherein the processor is further configured to: upon determining that a period of time during which a plurality of consecutive ones of the samples indicate the non-sedentary state is adjacent to a period of time during which a plurality of consecutive ones of the samples indicate the sedentary state, identifying that the user has transitioned from the sedentary state to the non-sedentary state, wherein the communications device is configured to: upon determining that the user has transitioned into the non-sedentary state, sending an electronic notification to the wearable electronic device via a computer network for display via the wearable electronic device, the electronic notification including an incentive message.

23. The system of clause 16, wherein the metabolic equivalent measurement of the task is a measurement of energy expenditure, wherein each of the metabolic equivalent measurements of the task is non-zero.

24. The system of clause 16, wherein the processor is configured to: determining that a value of one of the samples for one of the time instants of interest is greater than the predetermined threshold; and classifying the one of the samples as having the non-sedentary state in response to determining that the value is greater than the predetermined threshold.

25. The system of clause 16, wherein the processor is configured to: determining that a value of one of the samples of one of the time instants of interest is greater than a second predetermined threshold value higher than the predetermined threshold value; and classifying said one of said samples as having said non-sedentary state in response to determining that said value is greater than said second predetermined threshold.

26. The system of clause 16, wherein the processor is configured to: determining that a value of a particular sample of the samples for a particular time instant of interest is greater than the predetermined threshold and less than a second predetermined threshold, the second predetermined threshold being higher than the predetermined threshold; determining that a value of one of the samples of the preceding time of interest that precedes the particular time of interest corresponds to the sedentary state; determining that a value of one of the samples at a later time of interest that is later than the particular time of interest corresponds to the sedentary state; and classifying the particular one of the samples as having the sedentary state.

27. The system of clause 16, wherein the processor is configured to: determining that values of a set of consecutive samples of the time instance of interest are each greater than the predetermined threshold and less than a second predetermined threshold, the second predetermined threshold being higher than the predetermined threshold; determining that a value of one of the samples of the preceding time of interest prior to the set of consecutive time of interest corresponds to the sedentary state; determining that a value of one of the samples at a later time of interest subsequent to the set of consecutive times of interest corresponds to the sedentary state; and classifying each of the samples of the set of consecutive time instants of interest as having the sedentary state.

28. The system of clause 27, wherein the processor is configured to: determining that a size of the set of consecutive time instants of interest is less than a size threshold before classifying each of the samples of the set of consecutive time instants of interest as having the sedentary state.

29. The system of clause 16, wherein the moments of interest occur at regular intervals.

30. A system, comprising: a communication device configured to receive samples of sensor information acquired from a wearable electronic device at a plurality of moments of interest, the sensor information generated while a user associated with the wearable electronic device performs one or more activities, the samples being metabolic equivalent measurements of a normalized task; a processor coupled to the communication device, the processor configured to determine a value of a particular one of the samples based on a determination that the value is between a first threshold and a second threshold, and is preceded by a first moment of interest at which the user state is classified as sedentary, and a second moment of interest at which the user state is classified as sedentary is followed by a particular moment of interest of the moments of interest, while a particular sample of the samples at a particular time of the time of interest is classified as sedentary, the processor is configured to detect a period of time during which a plurality of consecutive ones of the samples indicate the sedentary state, the processor is configured to determine whether the time period is greater than a threshold time period, the processor being configured to identify that the user is in a sedentary state upon determining that the time period is greater than the threshold time period.

While the invention has been described in terms of several embodiments, those skilled in the art will recognize that the invention is not limited to the embodiments described, but can be practiced with modification and alteration within the spirit and scope of the appended claims. The description is thus to be regarded as illustrative instead of limiting.

Claims

1. A system, comprising:

a wearable electronic device to be worn by a user, the wearable electronic device including one or more sensors configured to generate sensor data for a plurality of moments of interest;

one or more processors; and

a non-transitory machine-readable storage medium storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to:

obtain sensor data generated by the one or more sensors of the wearable electronic device,

determining analyzed sensor information for each of a first set of moments of interest of the plurality of moments of interest based on sensor data for the corresponding moment of interest, the first set corresponding to a first time window, wherein the analyzed sensor information indicates whether the user is sedentary or non-sedentary at the corresponding moment of interest, wherein the first time window is a predetermined time window,

determining, based at least on the analyzed sensor information for the first set of moments of interest, that a first point before the end of the first time window has not achieved a first goal for the first time window, wherein the first goal is an amount of non-sedentary activity, and

causing generation of a first notification in response to determining that the first goal has not been achieved at a first point prior to an end of the first time window.

2. The system of claim 1, wherein the first goal is based on input received from a user.

3. The system of claim 1, wherein the first goal is a number of steps taken during the first time window.

4. The system of claim 1, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to:

classifying each moment of interest in the first set as a state of a plurality of states based at least on the analyzed sensor information, wherein the plurality of states includes a sedentary state and a non-sedentary state, an

Determining whether the first goal of the first time window has been achieved based on a state of a time of interest of the first set.

5. The system of claim 4, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to: determining whether the first goal of the first time window has been achieved based on whether a duration of consecutive moments of interest in the first set that have been classified as having the non-sedentary state exceeds a threshold.

6. The system of claim 1, wherein a duration of the first time window is based on input received from a user.

7. The system of claim 1, wherein the duration of the first time window is between 10 minutes and 6 hours.

8. The system of claim 1, wherein the duration of the first time window is 60 minutes and the first point is preset 10 minutes before the end of the first time window.

9. The system of claim 1, wherein the first point is a predetermined amount of time before the end of the first time window.

10. The system of claim 9, wherein the amount of time is based on input received from a user.

11. The system of claim 1, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to:

determining, based at least on the analyzed sensor information for the moments of interest of the first set, that the first target of the first time window has been achieved at a second point in time, the second point in time being before the end of the first time window and after the first point in time; and

in response to determining that the first goal has been achieved, causing a second notification to be generated.

12. The system of claim 1, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to:

determining analyzed sensor information for each time instant of interest of a second set of the plurality of time instants of interest, the second set corresponding to a second time window, the second time window being immediately subsequent to the first time window, based on the sensor data for the corresponding time instant of interest,

determining, based on at least the analyzed sensor information for the second set of moments of interest, that a second point before the end of the second time window has not achieved a second goal for the second time window, and

causing generation of a second notification in response to determining that the goal has not been achieved at the second point prior to the end of the second time window.

13. The system of claim 12, wherein the duration of the first time window and the duration of the second time window are equal.

14. The system of claim 12, wherein the first point and the second point are the same amount of time before the end of the respective time window.

15. The system of claim 12, wherein the first target and the second target are the same.

16. The system of claim 1, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to:

classifying each moment of interest of the plurality of moments of interest as a state of a plurality of states including a sedentary state and a non-sedentary state based at least on the analyzed sensor information;

detecting a first period of time during which a plurality of consecutive moments of interest have been classified as having the sedentary state;

classifying the first time period as a sedentary state based on the first time period being greater than a threshold; and

causing a notification to be generated based on the first time period being greater than the threshold.

17. The system of claim 16, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to:

determining that a user associated with the wearable electronic device is asleep or not wearing the wearable electronic device at a first moment of interest; and

the first moment of interest is not classified as having a sedentary state or a non-sedentary state.

18. The system of claim 1, wherein the analyzed sensor information is metabolic equivalence of a task measurement, a sports measurement, or a heart rate measurement.

19. The system of claim 1, wherein the moments of interest occur at regular intervals.

20. The system of claim 1, wherein the analyzed sensor information for each time instant of interest is a single value.

21. The system of claim 1, wherein the one or more processors and the non-transitory machine-readable storage medium are located in the wearable electronic device.

22. The system of claim 1, wherein at least one of the one or more processors and the non-transitory machine-readable storage medium are located in a different electronic device than the wearable electronic device.

23. A system, comprising:

one or more processors; and

determining analyzed sensor information for each time instance of interest of the plurality of time instances of interest based on the sensor data for the corresponding time instance of interest,

classifying each moment of interest of the plurality of moments of interest as a state of a plurality of states including a sedentary state and a non-sedentary state based at least on the analyzed sensor information,

detecting a first period of time during which a plurality of consecutive moments of interest have been classified as having said sedentary state,

classifying the first time period as sedentary based on the first time period being greater than a first threshold,

detecting a second time period during which a plurality of consecutive moments of interest have been classified as having the non-sedentary state, the second time period immediately following the first time period,

classifying the second time period as a non-sedentary state based on the second time period being greater than a second threshold,

causing a notification to be generated based on the second time period being greater than the second threshold and the first time period being classified as a sedentary state.

24. The system of claim 23, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to:

classifying the second time period as corresponding to a certain activity type, an

Selecting the second threshold value according to the activity type.

25. The system of claim 24, wherein the activity type is selected from walking, jogging, running, sprinting, swimming, and cycling.

26. The system of claim 23, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to classify each of the plurality of moments of interest as a certain state of the plurality of states by:

classifying a moment of interest at which the analyzed sensor information exceeds a threshold as the non-sedentary state, an

Classifying a moment of interest for which the analyzed sensor information does not exceed the threshold as the sedentary state.

27. The system of claim 23, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to classify each of the plurality of moments of interest as a certain state of the plurality of states by:

classifying a moment of interest at which the analyzed sensor information exceeds a first threshold as the non-sedentary state,

classifying a moment of interest for which the analyzed sensor information does not exceed a second threshold as the sedentary state, wherein the second threshold is lower than the first threshold, an

Classifying a set of N consecutive moments of interest as sedentary as follows: the analyzed sensor information for the set of N consecutive time instants of interest is between the first threshold and the second threshold and immediately before and after the time instant of interest with a sedentary status.

28. The system of claim 27, wherein N is between 1 and 5.

29. The system of claim 27, wherein the N consecutive instants of interest correspond to time intervals having a duration between 1 and 300 seconds.

30. The system of claim 23, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to:

determining that a user is asleep or not wearing the wearable electronic device during a first moment of interest; and

not classifying the first moment of interest as having the sedentary state or the non-sedentary state.

31. The system of claim 23, wherein the analyzed sensor information is metabolic equivalence of a task measurement, a sports measurement, or a heart rate measurement.

32. The system of claim 23, wherein the moments of interest occur at regular intervals.

33. The system of claim 23, wherein the analyzed sensor information for each time instant of interest is a single value.

34. The system of claim 23, wherein the one or more processors and the non-transitory machine-readable storage medium are located in the wearable electronic device.

35. The system of claim 23, wherein at least one of the one or more processors and the non-transitory machine-readable storage medium are located in a different electronic device than the wearable electronic device.

36. A system, comprising:

one or more processors; and

classifying each of the plurality of moments of interest as an activity type of a plurality of activity types based at least on the analyzed sensor information, the plurality of activity types including at least one activity type associated with a non-sedentary state,

detecting that a plurality of consecutive moments of interest have been classified as having a first period of sedentary status,

detecting that a plurality of consecutive moments of interest have been classified as a second time period having a certain activity type, the second time period being immediately subsequent to the first time period,

causing a notification to be generated based on the second time period being greater than the second threshold and the first time period being classified as sedentary.

37. The system of claim 36, wherein the plurality of activity types includes walking, jogging, running, sprinting, swimming, and cycling.

38. The system of claim 36, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to: selecting the second threshold based on the activity type for the time of interest for the second time period.

39. The system of claim 38, wherein the second threshold is the same for all activity types.

40. The system of claim 38, wherein the second threshold is different for each activity type.

41. The system of claim 36, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to classify each of the plurality of moments of interest as a state of the plurality of states by:

42. The system of claim 36, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to classify each of the plurality of moments of interest as a state of the plurality of states by:

43. The system of claim 42, wherein N is between 1 and 5.

44. The system of claim 42, wherein the N consecutive instants of interest correspond to time intervals having a duration between 1 and 300 seconds.

45. The system of claim 36, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to:

46. The system of claim 36, wherein the analyzed sensor information is metabolic equivalence of a task measurement, a sports measurement, or a heart rate measurement.

47. The system of claim 36, wherein the time of interest occurs at regular intervals.

48. The system of claim 36, wherein the analyzed sensor information for each time instant of interest is a single value.

49. The system of claim 36, wherein the one or more processors and the non-transitory machine-readable storage medium are located in the wearable electronic device.

50. The system of claim 36, wherein at least one of the one or more processors and the non-transitory machine-readable storage medium are located in a different electronic device than the wearable electronic device.

51. A system, comprising:

one or more processors; and

classifying a set of N consecutive moments of interest as the sedentary state of a plurality of states including a sedentary state and a non-sedentary state based on each moment of interest of the set of N consecutive moments of interest as follows: the analyzed sensor information for each time instant of interest in the set of N consecutive time instants of interest is between a first threshold and a second threshold and immediately preceding and succeeding the set of N consecutive time instants of interest is a time instant of interest with a sedentary status,

detecting a plurality of consecutive moments of interest having a first period of time of said sedentary state, an

Classifying the first time period as a sedentary state based on the first time period being greater than a threshold time.

52. The system of claim 51, wherein the first threshold is greater than the second threshold, and the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to classify each of the plurality of moments of interest as the sedentary state or the non-sedentary state by:

classifying a moment of interest at which the analyzed sensor information exceeds the first threshold as a non-sedentary state, an

Classifying a moment of interest at which the analyzed sensor information does not exceed the second threshold as a sedentary state.

53. The system of claim 51, wherein N is between 1 and 5.

54. The system of claim 51, wherein the N consecutive moments of interest correspond to time intervals between 1 and 300 seconds.

55. The system of claim 51, wherein the analyzed sensor information is metabolic equivalence of a task measurement, a sports measurement, or a heart rate measurement.

56. The system of claim 51, wherein the moments of interest occur at regular intervals.

57. The system of claim 51, wherein the analyzed sensor information for each time instant of interest is a single value.

58. The system of claim 51, wherein the non-transitory machine-readable storage medium stores additional computer-executable instructions to cause the one or more processors to:

determining that a user associated with the wearable electronic device is asleep or not wearing the wearable electronic device during a first moment of interest; and

59. The system of claim 51, wherein the one or more processors and the non-transitory machine-readable storage medium are located in the wearable electronic device.

60. The system of claim 51, wherein at least one of the one or more processors and the non-transitory machine-readable storage medium are located in a different electronic device than the wearable electronic device.