CN105682034B - Step counting method and related device, detection method and related device - Google Patents
Step counting method and related device, detection method and related device Download PDFInfo
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- CN105682034B CN105682034B CN201610061724.6A CN201610061724A CN105682034B CN 105682034 B CN105682034 B CN 105682034B CN 201610061724 A CN201610061724 A CN 201610061724A CN 105682034 B CN105682034 B CN 105682034B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
- H04W4/027—Services making use of location information using location based information parameters using movement velocity, acceleration information
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
- G01C22/006—Pedometers
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
Abstract
The embodiment of the invention provides a step counting method and a related device, and a detection method and a related device. The step counting method comprises the following steps: acquiring moving speed data and gravitational acceleration data of a terminal in a time period, wherein the moving speed data and the gravitational acceleration data are aligned on a time axis; determining at least one travel mode of the user in the time period according to the moving speed data and/or the gravity acceleration data; and counting steps according to the corresponding gravity acceleration data in the at least one travel mode. Therefore, in the embodiment of the invention, the travel mode of the user is determined according to at least one of the moving speed data and the gravity acceleration data in a time period, and then the corresponding gravity acceleration data in the travel mode is counted. Therefore, when the user goes out, different steps are counted according to different going modes. Compared with the step counting only by using the gravity acceleration data, the step counting method has higher pertinence and accuracy in the user traveling process.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a step counting method and a related device, and a detection method and a related device.
Background
The pedometer on the terminal can acquire the gravity acceleration data through the gravity acceleration sensor and count the steps according to the gravity acceleration data. However, in the traveling process of the user, in some special scenes, for example, when the user travels by means of a bus, a high-speed rail, and the like, the counting is inaccurate.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a step counting method and related apparatus, a detection method and related apparatus, so as to improve the counting accuracy.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
a step counting method, comprising:
acquiring moving speed data and gravitational acceleration data of a terminal in a time period, wherein the moving speed data and the gravitational acceleration data are aligned on a time axis;
determining at least one travel mode of the user in the time period according to the moving speed data and/or the gravity acceleration data;
and counting steps according to the corresponding gravity acceleration data in the at least one travel mode.
A step counting device is applied to a terminal and comprises:
the terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring moving speed data and gravity acceleration data of the terminal in a time period, and the moving speed data and the gravity acceleration data are aligned on a time axis;
a travel mode determining unit, configured to determine at least one travel mode of the user within the time period according to the moving speed data and/or the gravitational acceleration data;
and the step counting unit is used for counting steps according to the corresponding gravity acceleration data in the at least one travel mode.
A terminal comprises the step counting device.
Based on the technical scheme, the embodiment of the invention determines the travel mode of the user according to at least one of the moving speed data and the gravity acceleration data in a time period, and then counts the corresponding gravity acceleration data in the travel mode. Therefore, when the user goes out, different steps are counted according to different going modes. Compared with the step counting only by using the gravity acceleration data, the step counting method has higher pertinence and accuracy in the user traveling process.
A method of detection, comprising:
acquiring moving speed data and gravity acceleration data;
and determining a travel mode by using the moving speed data and the gravity acceleration data.
A detection device is applied to a terminal, and the step counting device comprises:
the terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring moving speed data and gravity acceleration data of the terminal in a time period, and the moving speed data and the gravity acceleration data are aligned on a time axis;
and the travel mode determining unit is used for determining at least one travel mode of the user in the time period according to the moving speed data and/or the gravity acceleration data.
A detection device is applied to a terminal, and comprises:
the terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring moving speed data and gravity acceleration data of the terminal in a time period, and the moving speed data and the gravity acceleration data are aligned on a time axis;
and the travel mode determining unit is used for determining the travel mode of the user in the time period by using the moving speed data and the gravity acceleration data.
A terminal comprises the detection device.
Based on the detection technical scheme, the travel mode of the user is determined according to at least one of the moving speed data and the gravity acceleration data in a time period, and the follow-up processing based on the travel mode is ensured to be more targeted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic diagram of a hardware architecture of a terminal according to an embodiment of the present invention;
FIGS. 2, 6 and 7 are exemplary flowcharts of a step counting method according to an embodiment of the present invention;
3-5, 8-9 provide graphs of changes in gravitational acceleration data over a period of time for embodiments of the present invention;
FIG. 10 is a diagram illustrating an exemplary structure of a step counter according to an embodiment of the present invention;
FIG. 11a is a flowchart illustrating an exemplary detection method according to an embodiment of the present invention;
FIG. 11b is a diagram illustrating a structure of a detecting device according to an embodiment of the present invention;
FIG. 12 is a diagram illustrating an example of a general computer architecture provided by an embodiment of the present invention.
Detailed Description
The invention provides a step counting method, a step counting device and a terminal.
The step counting method or the step counting device can be applied to the terminal, such as a mobile terminal (e.g., a smart phone), a wearable device (e.g., a smart watch, a bracelet), and the like.
Fig. 1 shows a hardware architecture of the above terminal, which may include a baseband chip, a pedometer (or gravity Sensor), a coprocessor (Sensor Hub), and a main processor.
The baseband chip, the Sensor Hub and the main processor can be regarded as processors. The basic functions of the above devices are as follows:
the baseband chip is mainly responsible for completing the work of demodulation, descrambling, despreading and decoding of wireless signals (cellular network signals) in a mobile network (cellular network), and transmitting finally decoded digital signals to an upper layer processing system for processing. In the present invention, it can also provide the Sensor Hub with moving speed data, or, alternatively, provide the Sensor Hub with cellular network signal strength and cell handover. The baseband chip may provide the moving speed data (or cellular network signal strength and cell handover) periodically and, when the data changes, provide the moving speed data (or cellular network signal strength and cell handover).
The Sensor Hub is an application of a Microcontroller (MCU), and in the system design, the main function of the Sensor Hub is to process various information from various sensors, and awaken a main processor from a sleep mode when necessary, so as to achieve the purpose of reducing the power consumption of the system.
The pedometer (gravity sensor) and the coprocessor belong to a step-counting device, and the pedometer and the coprocessor can cooperate to complete the step-counting method. The division of the pedometer (gravity sensor) and the co-processor will be described later.
Fig. 2 shows an exemplary flow of the step counting method, which may include:
and S1, acquiring moving speed data and gravity acceleration data in a time period.
The moving speed data and the gravitational acceleration data are aligned on a time axis. In one example, the moving velocity data and the gravitational acceleration data at the same time may be combined into a data pair, stored in the terminal.
The moving speed data may be a specific speed value, and the moving speed data may be provided by a GPS or the like.
However, taking GPS positioning on a smartphone as an example, it needs to find satellites to perform positioning based on cellular network delivery information. In some scenes (such as 3G and 4G) with poor network signals, the GPS of the smart phone cannot complete positioning, and thus cannot provide accurate moving speed data.
In one example, at least one of the cellular network signal strength change information or the cell switching information received by the terminal may be acquired, and the moving speed data may be acquired according to the at least one of the cellular network signal strength change information or the cell switching information. The moving speed data obtained in this way may be a specific numerical value or may be a quantized value. By using the method of obtaining the moving speed data by the signal strength change of the cellular network or the cell switching, the user can obtain the moving speed data without opening the GPS function. And, taking 2G network as an example, the base station coverage is the widest, which provides great convenience for obtaining the moving speed data.
Moreover, the moving speed data is obtained according to the signal strength change information of the cellular network and the cell switching information, and information is not required to be transmitted based on the cellular network, so that the tolerance of the signal strength of the cellular network is higher than that of the GPS.
In terms of hardware, the baseband chip can provide the moving speed data to the step counting device, and also can provide at least one of the information of the signal intensity change of the cellular network or the cell switching information to the step counting device, and the step counting device can analyze the moving speed data to obtain the moving speed data.
The gravity acceleration data includes a characteristic waveform (or referred to as a characteristic value). For example, referring to fig. 3, the portion enclosed by the square frame is a characteristic waveform.
In terms of hardware, the pedometer (or the gravity Sensor) can provide original data of acquired gravity change for the Sensor Hub, and the Sensor Hub analyzes gravity acceleration data containing characteristic waveforms; the pedometer may also analyze itself to obtain the gravitational acceleration data including the characteristic waveform, and if the Sensor Hub performs the subsequent steps S2 and S3, the pedometer (gravity Sensor) needs to provide the original data of the change of gravity or the gravitational acceleration data to the Sensor Hub.
How to analyze the gravity acceleration data including the characteristic waveform can refer to the existing analysis method, which is not described herein.
It should be noted that the ending time of the time period may be the current time, that is, the step counting method provided by the present invention may perform step counting in real time. In addition, the ending time of the time period can be earlier than the current time, that is, the acquired historical data in a certain historical time period, and the step counting method provided by the invention can correct the count in the historical time period.
And S2, determining at least one travel mode of the user in the time period according to the moving speed data and/or the gravity acceleration data.
Optionally, the travel modes may include a first travel mode, a second travel mode, and a third travel mode.
Taking a common scene as an example, the first travel mode may be a walking mode, the second travel mode may be a bus (or automobile) mode, and the third travel mode may be a high-speed rail (or train) mode.
The main difference between the walking mode and the high-speed rail mode and the bus mode is the speed.
In the walking mode, the user does not use a transportation means (e.g., bus, high-speed rail, train, private car), i.e., the user is in a state of moving at a low speed. And in a high-speed rail (train) mode or a bus (automobile) mode, the user is in a state of high speed or rapid movement.
As for the bus (automobile) mode and the high-speed rail (train) mode, the speeds of the bus (automobile) mode and the high-speed rail (train) mode are also different, and the average moving speed in the high-speed rail mode is generally higher than that in the bus mode.
Of course, the travel mode may be classified in other manners, for example, the travel mode may include a walking mode, a running mode, a transportation mode, and the like, and the present invention is not limited thereto.
In one example, travel patterns within the above time period may be determined using only gravitational acceleration data. For example, assuming that there are first to third travel modes, when the characteristic waveform in the gravitational acceleration data matches the first travel mode within a certain period of time, it is determined that the current travel mode is the first travel mode.
In another example, travel patterns within the above-described time period may be determined using only movement speed data. Following the previous example, assuming that the moving speed data matches the moving speed characteristics of the second travel mode, it is determined that the current travel mode is the second travel mode.
In yet another example, travel patterns over the above time period may be determined in conjunction with movement velocity data and gravitational acceleration data. This allows more accurate determination of travel patterns. Following the previous example, when the movement speed data matches the movement speed characteristics of the second travel mode in a certain period of time, and the characteristic waveform in the gravitational acceleration data also matches the second travel mode, it is determined that the current travel mode is the second travel mode.
And S3, counting steps according to the corresponding gravity acceleration data in at least one travel mode.
Steps S2 and S3 may be performed by Sensor Hub or by a pedometer. If done by a pedometer, Sensor Hub is required to forward the movement speed data to the pedometer.
Therefore, in the embodiment of the invention, the travel mode of the user is determined according to at least one of the moving speed data and the gravity acceleration data in a time period, and then the corresponding gravity acceleration data in the travel mode is counted. Therefore, when a user (a user of the terminal) goes out, different steps can be counted according to different going modes. Compared with the step counting only by using the gravity acceleration data, the step counting method has higher pertinence and accuracy in the user traveling process.
In other embodiments of the present invention, the step S3 may further include the following steps:
step A, acquiring a user bump state waveform according to gravity acceleration data;
wherein the pitch state waveform comprises a waveform exhibiting regular, continuous fluctuations in amplitude, and further comprises a waveform having a maximum amplitude greater than or equal to a set amplitude threshold, such as a waveform having reduced amplitude oscillations.
The user bump state waveform includes at least one signature.
B, identifying a first characteristic waveform from the waveform of the user in the bumping state; the first signature is a signature that matches the non-walking features.
Taking the gravity acceleration data in a certain time period shown in fig. 4 as an example, the characteristic waveform 1 represents the change of the gravity acceleration caused by the start of the bus, the characteristic waveform 2 represents the change of the gravity acceleration caused by the brake of the bus, and only the characteristic waveform 3 represents the change of the gravity acceleration caused by the walking of a person on the bus.
Both the start and brake are non-walking characteristics, and signature 1 and signature 2 match, and are therefore removed.
Step C, filtering the first characteristic waveform from the waveform of the user in the bumping state;
and D, counting steps according to the user bump state waveform obtained after filtering treatment.
Following the previous example, the gravity acceleration data in fig. 4 has the characteristic waveforms 1 and 2 removed, and the characteristic waveform 3 remains. Step count statistics may be performed on the signature 3.
In the walking mode, for example, shaking the hand-held terminal will also generate a signature, but the signature is matched with the non-walking characteristics and can be removed.
Alternatively, in other embodiments of the present invention, the step S3 may further include the following steps:
and A1, acquiring a user bump state waveform according to the gravity acceleration data.
For details, refer to step a, which is not described herein.
Step B1, a second signature is identified from the user bump state waveform.
And the second characteristic waveform is a characteristic waveform matched with the walking characteristic in at least one travel mode.
And step C1, counting steps according to the identified second characteristic waveform.
Still taking the gravity acceleration data in a certain time period shown in fig. 4 as an example, the characteristic waveform 3 in fig. 4 is matched with the walking characteristics in the bus mode, and the characteristic waveform 3 can be extracted for step counting.
The walking characteristics in bus mode exhibit regular continuous fluctuations in amplitude. And, because the space of bus is limited, the fluctuation of regularity can not exist too much.
It should be noted that the gravity acceleration data in a certain time period may correspond to two or more travel modes.
For example, referring to fig. 5, if the user changes from walking to taking a bus in the time period from T0 to T100, the time period from T0 to T100 corresponds to two travel modes, and the steps can be respectively counted for the gravitational acceleration data in each travel mode.
Next, how to acquire the moving speed data of the terminal will be described.
The foregoing mentions that the moving speed data can be obtained according to at least one of the signal strength change information of the cellular network or the cell switching information. In an actual scene, the electronic device can receive a plurality of cellular network signals at the same time, and can select the signal with the strongest signal in the cell to which the electronic device belongs currently to analyze. When the strongest signal strength is weakened from strong, the user generally moves away from the current home cell base station, whereas when the signal strength is weakened from weak, the user generally moves close to the base station.
In addition, if the signal strength in the current home cell is weakened from strong and the signal strength from the neighboring cell is weakened from weak, the user generally moves to the cell boundary.
If the user moves at a low speed, the change of the signal strength in the unit time is possibly not changed greatly, and similarly, the cell switching times in the unit time are not too many. If the user is on a high-speed rail, and the user crosses multiple cells in a unit time (e.g. one minute), the number of cell handovers is also high when the user moves at a lower speed, and therefore, referring to fig. 6, obtaining the moving speed data of the terminal may include:
s601: the first movement velocity data is generated when the change (absolute) value of the cellular network signal strength per unit time is lower than a first threshold value and/or the number of cell handovers per unit time is lower than a second threshold value.
And/or means that at least one is satisfied.
That is, the signal strength change information may include a (absolute) value of a change in signal strength of the cellular network per unit time. The cell handover information may include the number of cell handovers per unit time.
A cellular network signal strength change (absolute) value below a first threshold per unit time and/or a number of cell handovers below a second threshold per unit time may be referred to as a first condition.
S602: second movement velocity data is generated when the (absolute) value of the change in signal strength of the cellular network per unit time is above the first threshold but below a third threshold and/or the number of cell handovers per unit time is above the second threshold but below a fourth threshold.
Obviously, the third threshold is greater than the first threshold, and the fourth threshold is greater than the second threshold.
The cellular network signal strength variation (absolute) value per unit time above the first threshold but below the third threshold and/or the number of cell handovers per unit time above the second threshold but below the fourth threshold may be referred to as a second condition.
S603: third movement speed data is generated when the change (absolute) value of the cellular network signal strength per unit time is higher than a third threshold value and/or the number of cell handovers per unit time is higher than a fourth threshold value.
A value of the change (absolute) in the cellular network signal strength per unit time above a third threshold and/or a number of cell handovers per unit time above a fourth threshold may be referred to as a third condition.
The first to third moving speed data may be specific speed values, and the first to third moving speed data may also be quantized values for representing the moving state. For example, the first moving speed data (e.g., 00) may represent a low-speed moving state, the second moving speed data (e.g., 01) may represent a medium-speed moving state, and the third moving speed data (11) represents a high-speed moving state.
The foregoing mentions that the travel modes may include a first travel mode (walking mode), a second travel mode (bus mode), and a third travel mode (high-speed rail mode). The three travel modes have respective moving speed characteristics.
When the first to third moving speed data are specific speed values, the moving speed characteristic of the travel mode may be a moving speed range, and different travel modes correspond to different moving speed ranges. Thus, when a specific speed value falls within a certain moving speed range, it is considered that the travel pattern corresponding to the moving speed range matches.
When the first to third moving speed data represent moving states for the quantized values, the moving speed characteristics of the trip mode may be specific moving states, and different trip modes correspond to different moving states. For example, the moving speed of the first travel mode (walking mode) is characterized by a low-speed moving state, the moving speed of the second travel mode (bus mode) is characterized by a medium-speed moving state, and the moving speed of the third travel mode (high-speed rail mode) is characterized by a high-speed moving state. In this case, the first moving speed data matches the first travel pattern, the second moving speed data matches the second travel pattern, and the third moving speed data matches the third travel pattern.
Accordingly, referring to fig. 7, determining at least one travel mode of the user during the time period may include:
s701: when the moving speed data in one time window of the time period is first moving speed data, and the characteristic waveform in the gravity acceleration data in the time window is matched with the moving characteristic of a first trip mode, determining that the trip mode is the first trip mode;
the length of the time window may be equal to the length of the entire time period, or may be less than the length of the entire time period.
S702: when the moving speed data in one time window of the time period is second moving speed data, and the characteristic waveform in the gravity acceleration data in the time window is matched with the moving characteristic of a second trip mode, determining that the trip mode is the second trip mode;
s703: and when the moving speed data in one time window of the time period is third moving speed data, and the characteristic waveform in the gravity acceleration data in the time window is matched with the moving characteristic of a third travel mode, determining that the travel mode is the third travel mode.
Wherein the movement characteristic may include at least one of a walking characteristic and a non-walking characteristic.
The non-walking characteristics may include at least start-up, inbound and outbound for high-speed rail mode and bus mode.
In addition, for the public transportation mode, the non-walking characteristics may also include braking, traffic jams, and turns. The braking is similar to the waveform of the starting, the traffic jam waveform can comprise braking and starting, and the frequency of the occurrence of the braking and the starting is higher than a certain threshold value; turning, which is characterized in that the direction of the gravitational acceleration changes. For example, the x direction is changed to the y direction in a short time.
In one example, a time period occupied by a certain moving speed data may be determined, and then whether a characteristic waveform matching a moving characteristic of a certain travel pattern exists in the time period may be retrieved.
Taking the example shown in fig. 8 as an example, assuming that the first moving speed data occupies T0-T10 (time window), and that the characteristic waveform 1 is retrieved to match the walking characteristics in the first travel mode within the T0-T10 time window, it is determined that the first travel mode (walking mode) is within the T0-T10 time window. And the second moving speed data occupies T11-T100 (time window), and if the characteristic waveform 2 is retrieved to match the vehicle start characteristic in the second travel mode within the time window T11-T100, the second travel mode (bus mode) within the time window T0-T10 is determined.
After the travel mode is determined, the steps are respectively counted in different counting modes according to the characteristic waveforms in different travel modes in fig. 8.
In another example, it may be determined that the characteristic waveform in the gravitational acceleration data matches the movement characteristic in which travel mode, and then it is determined whether the movement speed data matches the travel mode within the time window corresponding to the characteristic waveform, and if both of the movement speed data and the travel mode match, the travel mode corresponding to the characteristic waveform is determined.
Taking the example shown in fig. 9 as an example, assuming that the signature 1 occupies T4-T20 (time window), the signature 1 matches the walking characteristics in the first travel mode, and the first moving speed data is within the T4-T20 time window, the first travel mode (walking mode) is determined within the T4-T20 time window.
Similarly, assuming that the signature 2 occupies T21-T28 (time window), and the signature 2 matches the signature of the vehicle start in the second travel mode and the third travel mode, but is the third moving speed data within the T21-T28 time window, it is determined that the third travel mode (high-speed mode) is within the T21-T28 time window.
So on, it will not be described in detail.
Of course, if the characteristic waveform conforms to the movement characteristics of one of the travel patterns and the moving speed data conforms to the speed characteristics of the other travel pattern, the travel pattern may be preferentially determined from the moving speed data.
After the travel mode is determined, the steps are respectively counted in different counting modes according to the characteristic waveforms in different travel modes in fig. 9.
It should be noted that, by using the step counting method provided by the invention, real-time step counting can be performed according to real-time moving speed data and real-time gravity acceleration data.
In addition, because the time period occupied by some characteristic waveforms is relatively large, for example, the amplitude of the characteristic waveform started by the bus in fig. 3 is firstly reduced by oscillation, and then is relatively stable for a period of time. In real-time step counting, erroneous determination may occur. Therefore, the step counting method provided by the invention can be used for correcting the step number: obtaining historical moving speed data and historical gravitational acceleration data in a longer time period, determining a travel mode of a user in the time period by using the historical moving speed data and the historical gravitational acceleration data, and counting steps by adopting a step counting mode corresponding to the travel mode, so as to finally obtain the corrected step number.
In the following, the step-counting device provided by the embodiment of the present invention is introduced, and the step-counting device described below and the step-counting method described above can be referred to correspondingly.
Referring to fig. 10, the step counting device includes:
the acquiring unit 1 is used for acquiring moving speed data and gravitational acceleration data of the terminal in a period of time. The moving speed data and the gravitational acceleration data are aligned on a time axis.
A travel mode determining unit 2, configured to determine at least one travel mode of the user in the time period according to the moving speed data and/or the gravitational acceleration data;
and the step counting unit 3 is used for counting steps according to the corresponding gravity acceleration data in the at least one travel mode.
For details, please refer to the above description, which is not repeated herein.
In another embodiment of the present invention, in terms of acquiring the moving speed data of the terminal, the acquiring unit 1 is configured to:
acquiring at least one of cellular network signal strength change information or cell switching information received by a terminal;
and obtaining the moving speed data according to at least one of the signal intensity change information of the cellular network or the cell switching information.
For details, please refer to the above description, which is not repeated herein.
The moving speed data may include first moving speed data, second moving speed data, or third moving speed data.
In another embodiment of the present invention, in terms of acquiring the moving speed data of the terminal, the acquiring unit 1 may be specifically configured to:
generating first moving speed data (or determining the moving speed data as the first moving speed data) when the change value of the cellular network signal strength in unit time is lower than a first threshold value and/or the number of times of cell switching in unit time is lower than a second threshold value;
generating second moving speed data (or determining the moving speed data to be the second moving speed data) when the change value of the signal strength of the cellular network in unit time is higher than the first threshold but lower than the third threshold and/or the number of times of cell switching in unit time is higher than the second threshold but lower than the fourth threshold;
and generating third moving speed data (or determining the moving speed data to be the third moving speed data) when the change value of the signal intensity of the cellular network in unit time is higher than a third threshold value and/or the number of times of cell switching in unit time is higher than a fourth threshold value.
For details, please refer to the embodiment shown in fig. 6, which will not be described herein.
The travel modes comprise a first travel mode, a second travel mode and a third travel mode;
in other embodiments of the present invention, in the aspect of determining at least one travel mode of the user in the time period by using the moving speed data and the gravitational acceleration data, the travel mode determining unit 2 in all the embodiments described above may be configured to:
when the moving speed data is first moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristics of a first trip mode, determining that the trip mode is the first trip mode;
when the moving speed data is second moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristic of a second trip mode, determining that the trip mode is the second trip mode;
and when the moving speed data is third moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristic of a third travel mode, determining that the travel mode is the third travel mode.
For details, please refer to the embodiment shown in fig. 7, which is not described herein.
The embodiment of the invention also discloses a detection method, a detection device and a terminal.
The detection method or the detection device can be applied to the terminal, such as a mobile terminal (e.g., a smart phone), a wearable device (e.g., a smart watch, a bracelet), and the like.
Referring to fig. 11a, the detection method includes:
and S1, acquiring the moving speed data and the gravity acceleration data of the terminal in a time period.
The movement velocity data and the gravitational acceleration data are aligned on a time axis.
And S2, determining at least one travel mode of the user in the time period according to the moving speed data and/or the gravity acceleration data.
For details, please refer to the detailed description about steps S1 and S2, which is not repeated herein.
Referring to fig. 11b, the detecting device includes:
the terminal comprises an acquisition unit 1, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring moving speed data and gravity acceleration data of the terminal in a time period, and the moving speed data and the gravity acceleration data are aligned on a time axis;
a travel mode determining unit 2, configured to determine at least one travel mode of the user in the time period according to the moving speed data and/or the gravitational acceleration data.
For details, please refer to the foregoing steps S1 and S2 and the detailed description of the embodiment shown in fig. 10, which are not repeated herein.
Based on the above detection technical scheme, the embodiment of the invention determines the travel mode of the user according to at least one of the moving speed data and the gravitational acceleration data in a time period, and ensures that the subsequent processing based on the travel mode is more targeted. It should be noted that the subsequent processing based on the travel mode is not limited to step counting. For example, different communication modes or communication protocols may be used in the present or future according to different travel modes.
Fig. 12 shows a general computer system structure of the electronic device described above.
The computer system may include a bus, a processor 1201, a memory 1202, a communication interface 1203, an input device 1204, and an output device 1205. The processor 1201, the memory 1202, the communication interface 1203, the input device 1204, and the output device 1205 are connected to each other by a bus. Wherein:
a bus may include a path that transfers information between components of a computer system.
The processor 1201 may be a general-purpose processor, such as a general-purpose Central Processing Unit (CPU), a Network Processor (NP), a microprocessor, etc., an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program according to the present invention. But may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
Processor 1201 may include the baseband chip, Sensor Hub, and main processor described previously in fig. 1.
The memory 1202 stores a program for executing the present invention, and may store an operating system and other application programs. In particular, the program may include program code including computer operating instructions. More specifically, memory 1202 may be a read-only memory (ROM), another type of static storage device that may store static information and instructions, a Random Access Memory (RAM), another type of dynamic storage device that may store information and instructions, a disk storage device, or the like.
The input device 1204 may include a means for receiving data and information input by a user, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor, among others.
Output device 1205 may include means, such as a display screen, printer, speakers, etc., for allowing information to be output to a user.
The communication interface 1203 may include any means for communicating with other devices or communication networks, such as an ethernet, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), etc., using any transceiver or the like.
The processor 1201 executes the program stored in the memory 1202 and invokes other devices, which can be used to implement the steps of the step counting method or the detection method provided by the foregoing embodiments of the present invention.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (14)
1. A step counting method, comprising:
acquiring at least one of cellular network signal strength change information or cell switching information received by a terminal;
acquiring moving speed data of the terminal in a time period according to at least one of the cellular network signal strength change information or the cell switching information, and acquiring gravitational acceleration data of the terminal in the time period, wherein the moving speed data and the gravitational acceleration data are aligned on a time axis;
determining at least one travel mode of the user in the time period according to the moving speed data and the gravity acceleration data;
step counting is carried out according to the corresponding gravity acceleration data in the at least one travel mode, wherein the step counting comprises the following steps: acquiring a user bump state waveform according to the gravity acceleration data; identifying a first characteristic waveform from the user bump state waveform, wherein the first characteristic waveform is a characteristic waveform matched with non-walking characteristics; filtering the first signature from the user bump state waveform; and step counting is carried out according to the obtained user bumpiness state waveform after filtering processing.
2. The method of claim 1, wherein said step counting according to the corresponding gravitational acceleration data in the at least one travel mode further comprises:
acquiring a user bump state waveform according to the gravity acceleration data;
identifying a second characteristic waveform from the user bump state waveform, wherein the second characteristic waveform is a characteristic waveform matched with the walking characteristic in the at least one travel mode;
and counting steps according to the identified second characteristic waveform.
3. The method of any one of claims 1-2,
the movement speed data includes first movement speed data, second movement speed data, and third movement speed data;
the acquiring the moving speed data of the terminal includes:
generating first moving speed data when the cellular network signal strength variation value in unit time is lower than a first threshold value and/or the cell switching times in unit time is lower than a second threshold value;
generating second moving speed data when the signal strength change value of the cellular network in unit time is higher than the first threshold value but lower than a third threshold value, and/or the number of times of cell switching in unit time is higher than the second threshold value but lower than a fourth threshold value;
and generating third moving speed data when the change value of the cellular network signal strength in the unit time is higher than a third threshold value and/or the number of times of cell switching in the unit time is higher than a fourth threshold value.
4. The method of claim 3,
the travel modes comprise a first travel mode, a second travel mode and a third travel mode;
the determining at least one travel mode of the user within the time period according to the moving speed data and/or the gravitational acceleration data includes:
when the moving speed data is first moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristics of a first trip mode, determining that the trip mode is the first trip mode;
when the moving speed data is second moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristic of a second trip mode, determining that the trip mode is the second trip mode;
and when the moving speed data is third moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristic of a third travel mode, determining that the travel mode is the third travel mode.
5. A method of detection, comprising:
acquiring at least one of the intensity change of cellular network signals received by a terminal and cell switching;
acquiring moving speed data of the terminal in a time period according to at least one of the cellular network signal strength change information or the cell switching information, and acquiring gravitational acceleration data of the terminal in the time period, wherein the moving speed data and the gravitational acceleration data are aligned on a time axis;
determining at least one travel mode of the user in the time period according to the moving speed data and the gravity acceleration data;
the corresponding gravity acceleration data in the at least one travel mode is used for step counting, and a user bump state waveform is obtained according to the gravity acceleration data; identifying a first characteristic waveform from the user bump state waveform, wherein the first characteristic waveform is a characteristic waveform matched with non-walking characteristics; filtering the first signature from the user bump state waveform; and step counting is carried out according to the obtained user bumpiness state waveform after filtering processing.
6. The method of claim 5,
the movement speed data includes first movement speed data, second movement speed data, and third movement speed data;
the acquiring the moving speed data of the terminal includes:
generating first moving speed data when the cellular network signal strength variation value in unit time is lower than a first threshold value and/or the cell switching times in unit time is lower than a second threshold value;
generating second moving speed data when the signal strength change value of the cellular network in unit time is higher than the first threshold value but lower than a third threshold value, and/or the number of times of cell switching in unit time is higher than the second threshold value but lower than a fourth threshold value;
and generating third moving speed data when the change value of the cellular network signal strength in the unit time is higher than a third threshold value and/or the number of times of cell switching in the unit time is higher than a fourth threshold value.
7. The method of claim 6,
the travel modes comprise a first travel mode, a second travel mode and a third travel mode;
the determining at least one travel mode of the user within the time period according to the moving speed data and/or the gravitational acceleration data includes:
when the moving speed data is first moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristics of a first trip mode, determining that the trip mode is the first trip mode;
when the moving speed data is second moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristic of a second trip mode, determining that the trip mode is the second trip mode;
and when the moving speed data is third moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristic of a third travel mode, determining that the travel mode is the third travel mode.
8. A step counting device is applied to a terminal, and comprises:
the terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring at least one of cellular network signal strength change information or cell switching information received by the terminal; acquiring moving speed data of the terminal in a time period according to at least one of the cellular network signal strength change information or the cell switching information, and acquiring gravitational acceleration data of the terminal in the time period, wherein the moving speed data and the gravitational acceleration data are aligned on a time axis;
a travel mode determining unit, configured to determine at least one travel mode of the user within the time period according to the moving speed data and the gravitational acceleration data;
a step counting unit, configured to count steps according to the corresponding gravitational acceleration data in the at least one travel mode, including: acquiring a user bump state waveform according to the gravity acceleration data; identifying a first characteristic waveform from the user bump state waveform, wherein the first characteristic waveform is a characteristic waveform matched with non-walking characteristics; filtering the first signature from the user bump state waveform; and step counting is carried out according to the obtained user bumpiness state waveform after filtering processing.
9. The step counting device of claim 8,
the movement speed data includes first movement speed data, second movement speed data, or third movement speed data;
in the aspect of the acquiring the moving speed data of the terminal, the acquiring unit is configured to:
generating first moving speed data when the cellular network signal strength variation value in unit time is lower than a first threshold value and/or the cell switching times in unit time is lower than a second threshold value;
generating second moving speed data when the signal strength change value of the cellular network in unit time is higher than the first threshold value but lower than a third threshold value, and/or the number of times of cell switching in unit time is higher than the second threshold value but lower than a fourth threshold value;
and generating third moving speed data when the change value of the cellular network signal strength in the unit time is higher than a third threshold value and/or the number of times of cell switching in the unit time is higher than a fourth threshold value.
10. The step counting device of claim 9, wherein said travel modes include a first travel mode, a second travel mode and a third travel mode;
in respect of determining at least one travel mode of the user over the time period from the movement velocity data and/or the gravitational acceleration data, the travel mode determination unit is configured to:
when the moving speed data is first moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristics of a first trip mode, determining that the trip mode is the first trip mode;
when the moving speed data is second moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristic of a second trip mode, determining that the trip mode is the second trip mode;
and when the moving speed data is third moving speed data and the characteristic waveform in the corresponding gravity acceleration data is matched with the moving characteristic of a third travel mode, determining that the travel mode is the third travel mode.
11. A detection device, applied to a terminal, the detection device comprising:
the acquiring unit is used for acquiring at least one of the intensity change of the cellular network signal received by the terminal and the cell switching; acquiring moving speed data of the terminal in a time period according to at least one of the cellular network signal strength change information or the cell switching information, and acquiring gravitational acceleration data of the terminal in the time period, wherein the moving speed data and the gravitational acceleration data are aligned on a time axis;
a travel mode determining unit for determining at least one travel mode of the user within the time period using the moving speed data and the gravitational acceleration data;
the corresponding gravity acceleration data in the at least one travel mode is used for step counting, and a user bump state waveform is obtained according to the gravity acceleration data; identifying a first characteristic waveform from the user bump state waveform, wherein the first characteristic waveform is a characteristic waveform matched with non-walking characteristics; filtering the first signature from the user bump state waveform; and step counting is carried out according to the obtained user bumpiness state waveform after filtering processing.
12. A terminal, characterized in that it comprises a step-counting device according to any one of claims 8-10, or a detection device according to claim 11.
13. An electronic device comprising a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute a program stored in the memory to implement the step counting method according to any one of claims 1 to 4 or the detection method according to any one of claims 5 to 7.
14. A computer-readable storage medium, in which a software module is stored, which, when executed by a processor, implements the step counting method according to any one of claims 1 to 4, or the detection method according to any one of claims 5 to 7.
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| CN106092131A (en) * | 2016-07-06 | 2016-11-09 | 珠海市魅族科技有限公司 | A kind of method and apparatus counting step |
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| CN106289306A (en) * | 2016-07-29 | 2017-01-04 | 广东欧珀移动通信有限公司 | Step-recording method and device |
| CN106501825B (en) * | 2016-09-22 | 2019-07-19 | 广东乐心医疗电子股份有限公司 | Method and device for automatically adjusting GPS sampling frequency and wearable equipment |
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