WO2017095270A1

WO2017095270A1 - System and method for detecting and tracking pivotal motion of individual or pivoting object based on measurements of earth's magnetic field

Info

Publication number: WO2017095270A1
Application number: PCT/RU2016/000848
Authority: WO
Inventors: Vladimir Vyacheslavovivch SAVCHENKO
Original assignee: Savchenko Vladimir Vyacheslavovivch
Priority date: 2015-12-03
Filing date: 2016-12-05
Publication date: 2017-06-08

Abstract

A motion sensor is configured to detect and track a pivotal motion of at least one part of an individual, such as a limb, or a pivoting object, such as an athletic equipment. The motion sensor comprises a magnetic field sensor configured to repeatedly generate Earth's magnetic field data relative to a magnetic pole of the Earth and representing the pivotal motion of the individual or the pivoting object. The motion sensor also includes a data processor configured to process the Earth's magnetic field data to produce pivotal motion data characterizing the pivotal motion of the individual or the pivoting object. The motion sensor also includes a transmitter, such as a wireless transmitter, configured to repeatedly transmit the pivotal motion data to a host device such as a smart phone. The motion sensor may accurately track the pivotal motion without the use of an accelerometer or gyroscope.

Description

SYSTEM AND METHOD FOR DETECTING AND TRACKING PIVOTAL MOTION OF INDIVIDUAL OR PIVOTING OBJECT BASED ON MEASUREMENTS OF EARTH'S MAGNETIC FIELD

TECHNICAL FIELD

[0001] This disclosure generally relates to motion detectors and activity trackers, including motion sensors used in the athletic industry. More particularly, this disclosure relates to a system and a method for detecting and tracking a pivotal motion of at least one part of an individual, such as a limb or a body of the individual, or a pivotal motion of a pivoting object, such as an athletic equipment, substantially based on measurements and processing of Earth's magnetic field.

DESCRIPTION OF RELATED ART

[0002] Athletic activity is an important factor for many individuals in

maintaining a healthy lifestyle. It was found to be advantageous to track and record athletic activities, individual performance, and health data. For these ends, there can be found various wearable activity trackers, motion sensors, fitness monitors, smart watches, and similar devices on the market. Many existing activity trackers are portable and employ sensors for measuring motion parameters.

[0003] Typically, the sensors used in activity trackers include accelerometers, gyroscopes, or any combination thereof. These sensors can measure strides, a cadence, and a speed of an individual when she is engaged, for example, in a running or walking activity. The same sensors can be used to measure motion characteristics such as a linear or angular acceleration.

[0004] However, the accelerometers and gyroscopes are found to be space- consuming and power-consuming elements, which became relevant in designing products having a small form factor. The accelerometers and gyroscopes may not be desirable to use for detecting motions when there is a need for the activity sensor to be of a small size and low power consumption. Accordingly, there is still a need in the art to improve activity sensors configured to measure motions of an individual or a pivoting object. SUMMARY

[0005] This section is provided to introduce a selection of concepts in a simplified form that are further described below in the section of Detailed Description of Example Embodiments. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0006] According to one aspect of this disclosure, there is provided a motion sensor for tracking a pivotal motion of an individual or a pivoting object. The motion sensor comprises at least one magnetic field sensor configured to repeatedly generate Earth's magnetic field data relative to a magnetic pole of the Earth, where the Earth's magnetic field data represents at least one pivotal motion of at least one part of the individual or the pivoting object. The motion sensor further comprises a data processor operatively connected to the at least one magnetic field sensor and configured to process the Earth's magnetic field data to produce pivotal motion data characterizing the at least one pivotal motion of the at least one part of the individual or the pivoting object. The motion sensor comprises a transmitter operatively connected to the data processor and configured to repeatedly transmit the pivotal motion data to a host device.

[0007] According to another aspect of the disclosure, there is provided a system for pivotal motion sensing. The system comprises a plurality of motion sensors for tracking pivotal motion of at least a part of an individual or a pivoting object and optionally a host device. Each of the motion sensors comprises at least one magnetic field sensor configured to repeatedly generate Earth's magnetic field data relative to a magnetic pole of the Earth, where the Earth's magnetic field data represents at least one pivotal motion of the at least one part of the individual or the pivoting object. Each of the motion sensors can also comprise a data processor operatively connected to the at least one magnetic field sensor and configured to process the Earth's magnetic field data to produce pivotal motion data characterizing the at least one pivotal motion of the at least one part of the individual or the pivoting object. Each of the motion sensors also comprises a transmitter operatively connected to the data processor and configured to repeatedly transmit the pivotal motion data to the host device. The host device can be configured to receive the pivotal motion data from each of the plurality of motion sensors, correlate the pivotal motion data from each of the plurality of motion sensors with one another, and process the pivotal motion data from each of the plurality of motion sensors to produce a common visual or audio representation including the at least one motion of the at least one part of the individual or the pivoting object.

[0008] According to yet another aspect of the disclosure, there is provided a method for pivotal motion sensing. The method comprises repeated generating, by at least one magnetic field sensor, Earth's magnetic field data relative to a magnetic pole of the Earth, wherein the Earth's magnetic field data represents a pivotal motion of at least one part of the individual or the pivoting object. The method also comprises processing, by a data processor operatively connected to the at least one magnetic field sensor, the Earth's magnetic field data to produce pivotal motion data characterizing the at least one pivotal motion of the at least one part of the individual or the pivoting object. The method also comprises repeated transmitting, by a transmitter operatively connected to the data processor, the pivotal motion data to a host device.

[0009] Additional objects, advantages, and novel features of the examples will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following description and the

accompanying drawings or may be learned by production or operation of the examples. The objects and advantages of the concepts may be realized and attained by means of the methodologies, instrumentalities and combinations particularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which:

[0011] FIG. 1 shows a block diagram of an example system for pivotal motion sensing according to one embodiment;

[0012] FIG. 2 shows an individual and locations where a motion sensor can be attached to produce pivotal motion data according to one embodiment;

[0013] FIG. 3 A shows a data chart acquired by a motion sensor attached to an individual involved in a running activity;

[0014] FIG. 3B shows an actual angular motion chart corresponding to FIG. 3A for a motion sensor attached to an individual involved in a running activity;

[0015] FIG. 4A shows a data chart acquired by a motion sensor attached to a rowing oar;

[0016] FIG. 4B shows an actual angular motion chart corresponding to FIG. 4A for a motion sensor attached to rowing oar;

[0017] FIG. 5 shows a rowing boat where one or more motion sensors can be attached to measure certain motion angles;

[0018] FIGs. 6A-6F illustrate various pivoting objects, such as athletic equipment, on which one or more motion sensors can be attached;

[0019] FIG. 7 shows a model of pivoting motion according to one example embodiment;

[0020] FIG. 8 shows a flow diagram of a method for pivotal motion sensing according to an example embodiment; and

[0021] FIG. 9 is a computer system that may be used to implement the method for pivotal motion sensing. DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Introduction

[0022] The following detailed description of embodiments includes references to the accompanying drawings, which form a part of the detailed description. Approaches described in this section are not prior art to the claims and are not admitted to be prior art by inclusion in this section. The drawings show illustrations in accordance with example embodiments. These example embodiments, which are also referred to herein as

"examples," are described in enough detail to enable those skilled in the art to practice the present subject matter. The embodiments can be combined, other embodiments can be utilized, or structural, logical and operational changes can be made without departing from the scope of what is claimed. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope is defined by the appended claims and their equivalents.

[0023] Aspects of embodiments will now be presented with reference to a system and a method for pivotal motion sensing. These system and method may be implemented using electronic hardware, computer software, or any combination thereof. Whether such aspects of this disclosure are implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. By way of example, an element, or any portion of an element, or any combination of elements may be implemented with a "data processor" that includes one or more microprocessors, microcontrollers, Central Processing Units (CPUs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform various functions described throughout this disclosure. The data processor may execute software, firmware, or middleware (collectively referred to as "software"). The term "software" shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software components, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0024] Accordingly, in one or more embodiments, the functions described herein may be implemented in hardware, software, or any combination thereof. If implemented in software, the functions may be stored on or encoded as one or more instructions or code on a non-transitory computer-readable medium. Computer-readable media includes computer storage media. Storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise a random-access memory (RAM), a read-only memory (ROM), an electrically erasable programmable ROM (EEPROM), magnetic disk storage, solid state memory, or any other data storage devices, combinations of the aforementioned types of computer-readable media, or any other medium that can be used to store computer executable code in the form of instructions or data structures that can be accessed by a computer.

[0025] For purposes of this patent document, the terms "or" and "and" shall mean "and/or" unless stated otherwise or clearly intended otherwise by the context of their use. The term "a" shall mean "one or more" unless stated otherwise or where the use of "one or more" is clearly inappropriate. The terms "comprise," "comprising," "include," and "including" are interchangeable and not intended to be limiting. For example, the term "including" shall be interpreted to mean "including, but not limited to."

[0026] It should be also understood that the terms "first," "second," "third," and so forth can be used herein to describe various elements. These terms are used to distinguish one element from another, but not to imply a required sequence of elements. For example, a first element can be termed a second element, and, similarly, a second element can be termed a first element, without departing from the scope of present teachings. Moreover, it shall be understood that when an element is referred to as being "on" or "connected" or "coupled" to another element, it can be directly on or connected or coupled to the other element or intervening elements can be present. In contrast, when an element is referred to as being "directly on" or "directly connected" or "directly coupled" to another element, there are no intervening elements present.

[0027] The term "individual" shall mean a human. The term "at least one part of an individual" shall mean at least one limb of individual or a body portion of the individual. Some examples of "at least one part of an individual" include an arm, forearm, elbow, shoulder, leg, shin, foot, heel, neck, and head.

[0028] The term "pivoting object" shall be construed to mean an athletic equipment or athletic device configured to make any pivotal motion when directly moved by an individual. Some examples of pivoting objects include a tennis racket, squash racket, golf club, bicycle, kayak paddle, canoe paddle, outrigger paddle, standup boarding paddle, paddle blades, baseball bat, cricket bat, hockey stick, lacrosse stick, skates, athletic apparel, wearable products, bracelets, wearable bands, skis, snowboards, javelins, and so forth.

[0029] The term "magnetic field sensor" shall be construed to mean a device configured to measure Earth's magnetic fields. Examples of magnetic field sensors include magnetometers such as microelectromechanical (MEMS) magnetometers.

[0030] The term "host device" shall be construed to mean any computing or electronic device with a display or image-projecting device, including a mobile device, cellular phone, mobile phone, smart phone, Internet phone, user equipment, mobile terminal, tablet computer, laptop computer, desktop computer, workstation, thin client, server computer, personal digital assistant, multimedia player, navigation system, access gateway, networking switch, network storage computer, game console, entertainment system, infotainment system, vehicle computer, bicycle computer, virtual reality device, or any other computing device comprising at least a network module and a processor module.

[0031] As outlined above, the aspects of embodiments of this disclosure provide a motion sensor for tracking pivotal motions of at least one part of an individual or a pivoting object, a system for pivotal motion sensing including one or more of the motion sensors, and a method for pivotal motion sensing. The motion sensors can be fixed (permanently or releasably), attached, coupled, connected, secured, or mounted to certain places on an individual, pivoting object, vehicle, boat, or any other object to measure its motion characteristics in a two-dimensional (2D) or three-dimension (3D) space using a magnetic field of the Earth as a point of reference. One of the advantages of using a magnetic field sensor over other sensors is that it is not as susceptible to a vibration or shock commonly experienced in motion.

[0032] The motion sensor can detect various pivotal motions as follows. For example, when the motion is attached to a foot of an individual or on a shoe, the motion sensor can detect a type of pivotal motion or a type of activity such as walking, running, jogging, high jumping, length (long) jumping, soccer, football, basketball, roller blading, skating stride, rotation, or spinning (such as in gymnastics or FIG. skating). Specifically, when the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is walking, sport walking, or running, the motion sensor can acquire one or more of the following activity-specific data: a cadence (usually measured in steps per minute), a step distance, a step type (such as heel to toes, flat, toes to heel, or toes only), and a foot motion pivotal angle in 3D. When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is high jumping, the motion sensor can acquire one or more of the following activity- specific data: a step count before a jump, an individual step distance, a jump height, and a jump air time. When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is length (long) jumping, the motion sensor can acquire one or more of the following activity-specific data: a step count before a jump, an individual step distance, a jump height, and a jump air time. When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is soccer or football, the motion sensor can acquire one or more of the following activity-specific data: a step count, an individual step distance, a foot swing angle, and a foot swing speed. When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is basketball, the motion sensor can acquire one or more of the following activity-specific data: a step count before a jump, an individual step distance, a jump height, and a jump air time. When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is roller blading, skating, or cross-country skiing, the motion sensor can acquire one or more of the following activity-specific data: a cadence (usually measured in steps per minute), a stride distance, and a stride type (such as classical or skating). When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is cycling, spinning, or rotation-like activity, the motion sensor can acquire one or more of the following activity-specific data: a rotation angle, a rotation count, a rotation direction (such as a forward direction and a reverse direction), and a spinning speed.

[0033] When the motion sensor is attached to an arm of an individual or a pivoting object, the motion sensor can detect a type of motion or a type of activity in sports and activities such as a swing in tennis, squash, badminton, racquet ball, golf, hokey, baseball, cricket, volleyball, handball, swimming, running, walking, skating, skiing, also including a rotation and spinning, for example, in gymnastics. Particularly, when the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is tennis, squash, badminton, any racquet sport, golf, hockey, baseball, or cricket, the motion sensor can acquire one or more of the following activity-specific data: an arm swing and pivotal angle relative to a shoulder, an elbow, or a wrist joint of the individual, and arm swing distance, an arm swing speed, an arm swing type (e.g., forehand or backhand, upwards, downwards, or straight). When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is volleyball, handball, or any related sport, the motion sensor can acquire one or more of the following activity-specific data: an arm swing pivotal angle relative to a shoulder, an elbow, or a wrist joint of the individual, arm swing distance, arm swing speed, arm swing type (such as upwards, downwards, straight, push, or serve). When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is swimming, the motion sensor can acquire one or more of the following activity-specific data: an arm movement cadence (usually measured in strokes per minute) and a wrist twist angle. When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is walking, sport walking, running, skating, or cross country skiing, the motion sensor can acquire one or more of the following activity-specific data: an arm movement cadence (usually measured in strides per minute), and an arm stride count. When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is a rotation or spinning related activity (e.g., gymnastics), the motion sensor can acquire one or more of the following activity-specific data: a rotation angle (usually measured in degrees or fraction of a circle), a rotation count, a rotation direction in space, and a spinning speed.

[0034] When the motion sensor or a host device operatively connected to the motion sensor detects that the type of activity is general activity, the motion sensor can acquire one or more of the following activity-specific data indicating one or more of the following: sleeping, rolling from side to side, getting up, laying down, falling, walking, and sitting.

[0035] When the motion sensor is attached near joints of an individual on an arm, elbow, shoulder, or near a hip or knee of the individual, the motion sensor can acquire one or more of the following activity-specific data: a motion angle of the joint in 3D and a motion cadence usually measured in swings per minute.

[0036] In certain embodiments, the motion sensor can be attached to a pivoting object to measure its pivotal motion. For example, when the motion sensor is attached to a rowing oar, the following characteristics can be detected: a rowing cadence (usually measured in strokes per minute), a stroke drive duration, an oar pivotal angle in the oarlock and oar swing distance, a pull duration, and a recovery duration. When the motion sensor is attached to a paddle with two blades, such as kayak or surf ski, the motion sensor can acquire one or more of the following activity-specific data: a paddling cadence (usually measured in strokes per minute), a stroke duration, a stroke distance, a paddle swing angle, a paddle blade rotation angle, and a paddle swing distance. When the motion sensor is attached to a paddle with one blade, such as a paddle for a canoe, outrigger or standup paddle board, the motion sensor can acquire one or more of the following activity-specific data: a paddling cadence (usually measured in strokes per minute), a stroke duration, a stroke angle, a paddle blade pivot angle, and a paddle swing distance.

[0037] In some embodiments, the motion sensor can also be attached to a pivoting object to measure its motion characteristics and position in 3D space. For example, when the motion sensor is attached to a bicycle frame, the motion sensor can acquire one or more of the following activity-specific data: a cycling cadence, a cadence crank revolution angle, and a crank revolution direction. When the motion sensor is attached to a boat or any other water floating object the motion sensor can acquire one or more of the following activity-specific data: a lateral and left-to-right rolling cadence. When the motion sensor is attached to a downhill ski, snowboard, or a sled, the motion sensor can acquire one or more of the following activity-specific data: a turns count and a turn angle.

[0038] The motion sensor or motion sensors attached to a human body or a pivoting object can be also used with a host device, such as a mobile phone, smart watch, or another computing device for displaying and storing data acquired and processed by the motion sensor or sensors.

[0039] In some embodiments, the system may comprise at least one motion sensor wirelessly connected to a host device. The motion sensor generates Earth's magnetic field data indicative of movement of the motion sensor attached to a human body or a pivoting object with respect to a magnetic pole of the Earth, transforms the Earth's magnetic field data according to a purpose of the motion sensor to pivotal motion data characterizing the pivotal motion of at least one part of the human body or the pivoting object, and transmits the pivotal motion data to the host device. For example, if the motion sensor is mounted on a foot of human and the purpose of the motion sensor is to measure running characteristics, then the pivotal motion data is calculated according to a particular algorithm designed for detecting running characteristics only. The pivotal motion data, such as running cadence, running speed, step distance, and step type, is then transmitted to the host device.

[0040] In other embodiments, the motion sensor can be attached to a rowing oar to measure rowing characteristics. The Earth's magnetic field data is processed by the motion sensor according to a certain algorithm designed for detecting rowing

characteristics. The resulting pivotal motion data, such as a stroke count, rowing cadence, stroke drive duration, oar pivot angle, oar swing distance, and pull duration, is transmitted to the host device.

[0041] In yet another embodiment, the system may comprise a first motion sensor attached to an arm near a wrist, a second motion sensor attached to an arm near an elbow, a third motion sensor attached to an arm near a shoulder, and a fourth motion sensor attached to a foot. All mention sensors are wirelessly connected to a host device, which can be configured to receive the pivotal motion data from each of this plurality of motion sensors, correlate the pivotal motion data from each of the plurality of motion 16 000848 sensors with one another, and process the pivotal motion data from each of the plurality of motion sensors to produce a common (unified) visual or audio representation of one or more pivotal motion. In other words, the host device can receive data from all four sensors, processes it, display the results, and record the data into a data storage.

[0042] In additional embodiments, the motion sensor or the system may be used to calculate values associated with operational characteristics, such as an angle of a joint movement of an individual or an angle of a pivotal motion of a pivoting object.

System Architecture

[0043] Referring now to the drawings, exemplary embodiments are described.

The drawings are schematic illustrations of idealized example embodiments. Thus, the example embodiments discussed herein should not be construed as limited to the particular illustrations presented herein, rather these example embodiments can include deviations and differ from the illustrations presented herein.

[0044] FIG. 1 shows a block diagram of an example system 100 for pivotal motion sensing according to one embodiment. System 100 can be adapted to measure and record motion characteristics of an individual or a pivoting object.

[0045] As shown in FIG. 1, system 100 can include one or more motion sensors

105 and at least one host device 110. Each motion sensor 105 is enclosed into an enclosure 115, such as a housing. Further, each motion sensor 105 comprises a magnetic field sensor 120 (i.e., a magnetometer 120), a data storage 125 (or memory), a data processor 130 (such as a microcontroller (MCU)), a wireless transmitter 135, and a power source such as a battery 140. Motion sensor 105 is wirelessly connected to host device 110 via a built-in wireless transmitter 145 of host device 110. A wireless transmitter can use any existing wireless technologies for data transfer between motion sensor 105 and host device 110 such as Bluetooth radio, Ethernet network, an IEEE 802.11 -based radio frequency network, a Frame Relay network, Internet Protocol (IP) communications network, ANT+, Zigbee, or any other wireless technologies. While only one motion sensor 105 is shown as associated with host device 110 it is possible to have two or more motion sensors 105 connected to the same host device 110 simultaneously. Each of motion sensors 105 connected to host device 110 can measure different motion characteristics such as pivotal motions of different body parts of individual or different pivotal motions of pivoting object. Host device 110 can be also configured to receive pivotal motion data from each of motion sensors 105, correlate the pivotal motion data from each of the plurality of motion sensors 105 with one another, and process the pivotal motion data from each of the plurality of motion sensors 105 to produce a common visual or audio representation including the at least one motion of the at least one part of the individual or the pivoting object.

[0046] Magnetometer 120, which is also referred to as a magnetic field sensor, can be a MEMS device. Magnetometer 120 can be manufactured as a single purpose integrated circuit (IC) commonly called a microchip. It can also be combined with other MEMS devices as a multipurpose IC or in more complex systems as an integrated compass within an IC system. Regardless of the integration, the object of using magnetometer 120 is to measure Earth's magnetic field data relative to a magnetic pole of the Earth. Notably, magnetometer 120 is the only sensor used in motion sensor 105 and motion data is produced without information obtained from an accelerometer or a gyroscope.

[0047] Motion sensor 105 may have several logical modules or algorithms for different purpose such as for running, jumping, swimming, rowing, or kayaking. The logic can be programmed wirelessly from host device 110. It is also possible to have a multi-purpose logic programmed into motion sensor 105. For example, one motion sensor 105 attached to a wrist can measure swimming characteristics, while the same motion sensor 105 can measure running characteristics. The logic can recognize a type of activity, such as running or swimming, based on a certain pattern and transmit pivotal motion data according to the detected activity type for swimming or running.

[0048] The aspects of this technology also include a pivotal motion sensing method for use inside host device 110 comprising a magnetic field sensor inside host device 110 such as a smart watch, a smartphone, or a dedicated vehicle computer. Host device 110 can be mounted on a wrist of a human or attached to an object like a boat or a car. Host device 110 may contain at least one magnetometer and other components as outlined in mot ion sensor 105.

[0049] FIG. 2 shows an individual 200 and locations where a motion sensor can be attached to produce pivotal motion data according to one embodiment. As shown in FIG. 2, one or more motion sensors 105 can be attached, mounted, fixed, connected, retained, or placed on a human body near joints to measure various motion

characteristics. The front view of human body 201 shows a possible placement of motion sensors 105 near joints such as a shoulder 203, a wrist 205, or a foot 213. The rear view of human 202 shows a possible placement of motion sensors 105 near joints such as an elbow 207, a hip 209, or a knee 211.

[0050] Motion sensor 105 positioned near wrist 205 can measure an angle of arm movement in 3D and frequency of an arm movement. This angle can then be used to calculate additional characteristics based on desired activity or purpose of measurement. The frequency of arm movements can also be interpreted according to the type of activity. For example, in running activity (which can be automatically determined by motion sensor 105 or host device 110), it could determine stride time and strides per minute; in swimming activity, it can determine stroke time and strokes per minute.

[0051] Motion sensor 105 positioned near elbow 207 can measure an angle of elbow movement in 3D, a frequency of elbow motions, and the entire arm movement. This data can then be used to calculate additional characteristics based on desired activity or purpose of measurement. For example, in ping-pong or badminton activity, motion sensor 105 can measure an elbow swing angle and an angular speed of racquet motion.

[0052] Motion sensor 105 positioned near shoulder 203 can measure an angle of shoulder movement in 3D and a frequency of shoulder movement. This data can then be used to calculate additional characteristics based on desired activity or purpose of measurement. For example, in volleyball and basketball activities, motion sensor 105 can measure a shoulder swing angle and a speed of swing.

[0053] Motion sensor 105 positioned on foot 213 can measure an angle of an ankle movement in 3D, an angle of a foot movement in 3D, and a frequency of a foot movement. This data can then be used to calculate additional characteristics based on desired activity or purpose of measurement. For example, motion sensor 105 can determine how the foot is placed on the ground when the individual is running: heel first, flat, or toe first. The frequency of foot movement can also be interpreted according to the activity. For example, in running, it could determine step time and steps per minute. In swimming, it can determine a foot stroke or kick time, and strokes or licks per minute, In addition, in swimming, motion sensor 105 can also determine a type of swimming:

freestyle, breaststrokes or butterfly.

[0054] Motion sensor 105 positioned near knee 211 can measure an angle of knee movement in 3D and a frequency of knee movement. This angle can then be used to calculate additional characteristics based on desired activity or purpose of measurement. For example, motion sensor 105 can determine a speed of hitting a ball in soccer. The frequency of knee movement can also be interpreted by motion sensor 105 according to the activity. For example, in running, motion sensor 105 could determine step time and steps per minute.

[0055] Motion sensor 105 positioned near hip 209 can measure an angle of hip movement in 3D and f equency of hip movement. This angle can then be used to calculate additional characteristics by motion sensor 105 based on desired activity or purpose of measurement. For example, motion sensor 105 can determine a speed of hitting a ball in soccer. The frequency of hip movement can also be interpreted by motion sensor 105 according to the activity. For example, in running, motion sensor 105 could determine step time and steps per minute.

Periodic Motion Detection

[0056] The system for pivotal motion sensing of this disclosure can detect a periodic motion and interpret this motion based on installed sensor purpose. For example, if the sensor is installed on a leg of human at locations 209, 211, or 213as shown in FIG. 2 and the purpose of motion sensor 105 is to measure walking or running cadence, motion sensor 105 can interpret the detected periodic motion as steps with a time between each step Ts of the foot where the sensor is installed. Furthermore, motion sensor 105 can convert the time between steps of the foot Ts to the industry acceptable measurement units like steps per minute. This value can be referred to a running or walking cadence C and can be calculated using the following equation:

C = (7 1000) * 60 where C is steps per minute and Ts is a time between each step of one foot in milliseconds.

[0057] FIG. 3 A shows a data chart acquired by motion sensor 105 attached to an individual involved in a running activity. FIG. 3B shows an actual angular motion chart corresponding to FIG. 3 A for motion sensor 105 attached to an individual involved in a running activity.

[0058] As shown in FIG. 3 A, the motion for running can be detected based on a measurement of the magnetic field of the Earth in three orthogonal axis X, Y, and Z. In this example, motion sensor 105 was attached to a right foot. The right foot heel lift 301 and 311 is indicated on the chart of FIG. 3A. The time between 301 and 311 is Ts.

Further, since an individual usually moves booth feet at the same frequency when walking or running, the total cadence of both feet can be calculated by doubling the cadence of a single foot where the sensor is installed. It is also possible to have two motion sensors 105 on each foot measuring time Ts of each foot more precisely.

[0059] In another example, motion sensor 105 can be installed on an arm such as at locations 203, 205 or 207 as shown in FIG. 2, and the purpose is to measure hand movement in swimming, running, or cross country skiing. The sensor can interpret the detected periodic motion as arm strokes with a time between each stroke Ts.

Furthermore, motion sensor 105 can convert the time between hand strokes Ts to the industry acceptable measurement units like strokes per minute (Stpm) which can be calculated using the following equation: Stpm = (Ts 1 1000) * 60 where Stpm is strokes per minute and Ts is a time between each stroke of one hand in milliseconds.

[0060] Yet in another example, motion sensor 105 can be installed on a leg of individual such as in locations 209, 211 or 213 as shown in FIG. 2, and the purpose is to measure a leg movement in swimming. Motion sensor 105 can interpret the detected periodic motion of a foot as kicks with a time between each kick Ts. Furthermore, the sensor can convert the time between kicks Ts to the industry acceptable measurement units like kicks per minute Kpm using the following equation:

where Kpm is kicks per minute and Ts is a time between kick a foot in milliseconds.

[0061] The sensor can also be installed on an object to measure its periodic motion. For example, if motion sensor 105 is installed on a rowing oar 509 or 511 as shown in FIG. 5, and the purpose is to measure rowing oar motion, motion sensor 105 can interpret the detected periodic motion of oar as drive time Td and recovery time Tr.

[0062] FIG. 4A shows a data chart acquired by motion sensor 105 attached to an rowing oar. FIG. 4B shows an actual angular motion chart corresponding to FIG. 4A for motion sensor 105 attached to rowing oar.

[0063] As shown in FIG. 4A and 4B, the motion for rowing can be detected based on three orthogonal axis measurement X, Y and Z of the magnetic field of the Earth. According to these charts, the time between 405 and 407, or 411 and 413 is a drive time Td. The time between 407 and 409 and 413 and 415 is a recovery time. The events 405, 409, 411, and 415 indicate when the rowing oar blade is square or perpendicular to the water surface. The events 407 and 413 indicate when the rowing oar blade is feathering or parallel to the water surface. In order for motion sensor 105 to understand its orientation in space an initial calibration is required. This can be done by host device application sending a signal to motion sensor 105 when the oar is in feathering position and when the oar is in square position.

[0064] The sensor can also be installed on other pivoting objects to measure its motion characteristics. FIGs. 6A-6F illustrate various pivoting objects, such as athletic equipment, on which one or more motion sensors 105 can be attached or built-in. For example, as shown in FIG. 6A, a motion sensor 603 (which is analogous to motion sensor 105 described above) can be installed on a kayaking paddle 601 to measure drive time on each of the paddle blades. The motion is similar to rowing motion when one blade is squared and in the water and the other is in the air. The motion sensor can also detect when the right or left blade is in the water driving the boat. In order for the motion sensor to understand its orientation in space an initial calibration is required. This can be done by a host device application sending a signal to the sensor when the left blade is in the water and then when the right blade is in the water. The motion sensor remembers the X, Y and Z coordinates for both positions and uses this data to measure left and right blade strokes.

[0065] As shown in FIG. 6B, when a motion sensor 605 (which is analogous to motion sensor 105 described above) is installed on a canoe, outrigger, or standup boarding paddle 607, motion sensor 605 can measure drive time and recovery time using similar approach as for the kayaking paddle. As shown in FIG. 6C, when a motion sensor 609 (which is analogous to motion sensor 105 described above) is installed on a baseball or cricket bat 611, it can measure time of a swing. As shown in FIG. 6D, when a motion sensor 615 (which is analogous to motion sensor 105 described above) is installed on a hockey stick 613, it can measure a hockey stick swing time. As shown in FIG. 6E, when a motion sensor 619 (which is analogous to motion sensor 105 described above) is installed on a golf club 617, it can measure a golf swing time. As shown in FIG. 6F, when a motion sensor 623 (which is analogous to motion sensor 105 described above) is installed on a tennis, squash, or any other racket 621, it can measure a swing time.

Angle Detection

[0066] The motion sensor of this disclosure can detect an angle of a motion in

3D. When the motion sensor is used on an individual or a pivoting object it can be abstracted in a motion diagram as shown in FIG. 7.

[0067] Particularly, FIG. 7 shows a model 700 of pivoting motion according to one example embodiment. An element 701 indicates a center of pivotal motion, an element 703 is a position of a swinging arm in the beginning of a measurement, and element 705 is a position of the same arm at the end of measured motion. An element 707 is an angle of the pivotal motion.

[0068] In one example embodiment, motion sensor 105 is attached just slightly below elbow joint 207 as shown in FIG. 2, and the purpose is to measure an angle of the elbow motion, then the model 700 shown in FIG. 7 can be interpreted as follows:

element 701 is an elbow joint, element 703 is a position of motion sensor in the beginning of measurement, and element 705 is the position of motion sensor at the end of measurement. Angle 707 is an angle of the arm motion relative to the elbow joint.

[0069]

[0070] Yet in another example embodiment, motion sensor 105 can be attached just slightly below a shoulder joint 203 as shown in FIG. 2, and the purpose is to measure angle of a shoulder motion. In this case, the model shown in FIG. 7 can be interpreted as follows: element 701 is a shoulder joint, element 703 is a position of the sensor in the beginning of measurement, and element 705 is the position of the sensor at the end of measurement. Angle 707 is the angle of arm motion relative to the shoulder joint.

[0071] Yet in another example embodiment, motion sensor 105 is attached just slightly below a hip joint 209 as shown in FIG. 2 and the purpose is to measure angle of the hip motion. In this scenario, the model shown in FIG. 7 can be interpreted as follows; element 701 is a hip joint, element 703 is a position of the sensor in the beginning of measurement, and element 705 is the position of the sensor at the end of measurement. Angle 707 is the angle of leg motion relative to the hip joint. [0072] Yet in another example embodiment, motion sensor 105 is attached just slightly below a knee joint 211 as shown in FIG. 2 and the purpose is to measure angle of a knee motion. In this case, the model shown in FIG. 7 can be interpreted as follows: element 701 is a knee joint, element 703 is a position of the sensor in the beginning of measurement, and element 705 is the position of the sensor at the end of measurement. Angle 707 is the angle of a leg motion relative to the knee joint.

[0073] Referring back to FIGs. 3 A and 3B, the motion for jogging can be detected based on three orthogonal axis measurement X, Y and Z of the magnetic field of the Earth. For example, when motion sensor 105 is attached to a right foot, the sensor can detect right foot heel lift 301, toes lift 303, heel strike 307, toes down 309, and heel lift 311 again.

[0074] It can be more practical to select an axis with highest amplitude of motion for detecting the above stated action. However, for reliability all three axes shall be used for calculation and correction. For the sake of demonstration only, motion sensor 105 used three sets of two-dimensional (2D) models for each pair of axis: X and Y, Y and Z, X and Z. The actual angular motion of motion sensor 105, when it is attached to a foot is shown in FIG. 3B. A point 323 is a projection of point 301 on a two-axis chart X and Z. This is when the individual lifts a foot heel to make a step and it corresponds to point 703 in FIG. 7. A point 325 is a position of a point 307 on two-axis chart X and Z. This is the end of the step when the foot is placed on a ground, and it corresponds to point 705 of motion model 700. A point 321 is a center of pivotal motion. In running, the main leg motion is around the hip joint. Therefore, point 321 or 701 in this example are representing the hip joint. A point 327 is the same as 305 and is representing

measurement taken when the foot was in the air lifted from the ground.

[0075] Motion sensor 105 can also measure a motion angle of a pivoting object.

For example, FIG. 5 shows a rowing boat 500 where one or more motion sensors 105 can be attached to measure certain motion angles. In example shown in FIG. 5, a motion sensor 509 (which is analogous to motion sensor 105 described above) is attached to one of rowing oars 505 just slightly above an oar pivotal point in an oar lock 507. The purpose of this sensor installation is to measure rowing characteristics. The model 700 shown in FIG. 7 can be interpreted as follows: element 701 is the same as a pivotal point in the oar lock 507, element 703 is a position of the sensor in the beginning of the measurement, and element 705 is the position of the sensor at the end of the measurement. An angle 707 is the angle of an oar 505 motion.

[0076] As shown in FIGs. 4A and 4B, the rowing motion can be detected based on three orthogonal axis measurements X, Y and Z of the magnetic field of the Earth. For example, when motion sensor 509 is attached to the rowing oar, the sensor can detect an oar blade in square positions 405, 409, 411, and 415, or an oar blade in feather positions 407 and 413.

[0077] In some embodiments, it can be more practical to select an axis with highest amplitude of motion for detecting the above stated action, i.e., amplitudes 401 and 403 in the beginning of the chart and amplitudes 417 and 419 at the second part of the chart. However, for reliability all three axes shall be used for calculation and correction. For the sake of demonstration only, it is used three sets of 2D models for each pair of axis: X and Y, Y and Z, X and Z.

[0078] The actual angular motion of the sensor when attached to the rowing oar is shown in FIG. 4B. Point 425 is a position of a point 411 on two-axis chart X and Z. This is the beginning of the drive of oar and corresponds to point 703 of motion model 700. Point 423 is a projection of point 413 on two-axis chart: X and Z. This is when the drive of an oar ends and corresponds to point 705 in FIG. 7. The point 421 is a center of pivotal motion. In rowing, this point is at the oar lock 507. Therefore, points 421 and 701 are representing the oar lock point 507. Point 427 is a single measurement taken when the oar was in the middle of a drive and corresponds to a point 706.

Angular Motion Calibration

[0079] To measure a pivotal angle of motion properly, the sensor needs to be calibrated. This can be done by sending a signal from the host device to the sensor indicating that it should be entering a calibration mode. In the calibration mode, the sensor shall be moved in the same manner as it is intended for usage. For example, if the sensor is used for running and installed on shoe 213, then in calibration mode a person shall run for a few steps. If the sensor is installed on arm 203 for measuring swimming, then the individual shall swing arms in swimming like motion. If the sensor is installed on rowing oar 509, then the sensor needs to be moved in rowing motion with a maximum possible amplitude.

[0080] In calibration mode, the sensor records all X, Y and Z coordinates during calibration motion at a predefined sampling frequency and recreates in memory the diagram shown in FIG. 7. Since the diagram of FIG. 7 displays only 2D picture, the sensor creates three diagrams in memory for each pair of 2D coordinates: X and Y, Y and Z, X and Z. Then it detects points 703 as a starting point of a calibration motion and point 705 as an ending point of a calibration motion. Then the host device application shall notify the sensor what is the actual angle 707 occurred during this calibration process. It is advisable to use a maximum possible swing during calibration to determine maximum possible angle 707.

[0081] During calibration, motion coordinates for other points besides 703 and

705 are also recorded in memory or data storage 103. One of many such points 706 is show on the diagram of FIG. 7. The coordinates for points 703, 705, and 706 will be used to calculate coordinates for the center of pivotal motion point 701. Point 706 is selected from a number of points stored in memory somewhere in the middle of the circular motion between points 703 and 705. This will give a better precision for calculating the center point 701.

[0082] Each of the three points will have three coordinates: element 703 has coordinates (X3, Y3, Z3), element 705 has coordinates (X5, Y5, Z5), and element 706 has coordinates (X6, Y6, Z6), and the center of pivotal motion 701 has coordinates (XI, Yl, Zl). Since we use a 2D model in the description of the principal for the sensor as shown in FIG. 7 to describe a 3D model of actual motion, then the calculation of a center point of pivotal motion will be based on three pairs of co-ordinates for X and Y, Y and Z, X and Z. To calculate a center point of pivotal motion for element 701 having coordinates (XI, Yl, Zl), the sensor's logic can use the following equations in the form of a mathematical determinant:

(X₃ ^A2+Y₃ ^A2) Y₃ 1 X₃ (Χ₃ ^Λ2+Υ₃ ^Λ2) 1 (Χ₅ ^Λ2+Υ₅ ^Α2) Y₅ 1 X₅ (Χ₅ ^Λ2+Υ₅ ^Λ2) 1 I (Χ6^Λ2+Υ₆ ^Λ2) Y₆ 1 ¾ (Χ₆ ^Λ2+Υ₆ ^Λ2) 1 I

Xla- Y =

| X₃ Y₃ 1 | X₃ Y₃ 1

I Xs Y₅ 1 2 * | Xs Y₅ l

| X6 Ye 1 I X₆ Ye 1 I (Υ₃ ^Λ2+Ζ₃ ^Λ2) Z₃ 1 I I Y₃ (Υ₃ ^Λ2+Ζ₃ ^Λ2) 1 I I (Υ₅ ^Λ2+Ζ₅ ^Λ2) Z₅ 1 I I Y₅ (Υ₅ ^Λ2+Ζ₅ ^Λ2) 1 I I (Υ₆ ^Α2+Ζ₆ ^Λ2) Z₆ 1 I I Y₆ (Υ₆ ^Λ2+Ζ₆ ^Λ2) 1 1

Yl b= z_la-

Y₃ Z₃ 1 Y₃ ¾ 1

Y₅ Z₅ 1 2 * Ys Z₅ 1

Y₆ Z₆ 1 Y₆ Z₆ 1

I (Χ₃ ^Λ2+Ζ₃ ^Λ2) Z₃ 1 1 I X₃ (X₃ ^A2+Z₃ ^A2) 1

I (Χ₅ ^Λ2+Ζ₅ ^Λ2) Z₅ 1 I I Xs (Χ₅ ^Λ2+Ζ₅ ^Λ2) 1 |

I (Χ₆ ^Λ2+Ζ₆ ^Λ2) Z₆ 1 I I ¾ (Χ6^Λ2+Ζ₆ ^Λ2) 1 |

X_{l b}= Zi_b=

| X₃ Z₃ 1 | I X3 Z3 I I

2 * I X5 Z5 I I 2 * I X5 Z5 I I

I Χ_ό z₆ 1 1 I ¾ ¾ 1 I

[0083] Then the sensor uses these equations to calculate XI, Yl, Zl as follows:

Zi = (Zi.+ Zib) / 2;

[0084] The above method is one way of finding a center of pivotal motion. Like in any real systems certain element of error is present. To reduce an error even more, three points can be used for calibration of the center of pivotal motion 701.

[0085] For practical reasons, the angle for calibration can be 90, 180, 270, or 360 degrees. These angles are easier to reproduce using existing objects. For example, calibrating an elbow movement can be done when leaning elbow, shoulder and the wrist against a straight wall then lifting an elbow at 90 degrees. The 90-degree angle can be created by any square objects like a book leaned against a wall. An elbow can rest on the top side of a book while the other side of the book is pressed against a wall. Angle of 360 degrees can be used for calibrating a full arm swing like in freestyle swimming motion.

[0086] Angle of 180 degrees can also be achieved by swinging a pivoting object, like a rowing oar in the opposite position. In this case the starting point for calibration is element 703 with coordinates (X3 Y3 Z3) and the ending point is element 704 with coordinates (X4 Y4 Z4). The center of pivotal motion is element 701 having coordinates (XI, Yl, Zl), which can be calculated as follows:

Xi = (X₃ + X4) / 2;

Y! = (Y₃ + Y4) / 2;

Z₁ = (Z₃ + Z₄) / 2; [0087] The algorithm of the motion sensor can also detect forward and reverse motions in any 2D plane: X and Y, Y and Z, X and Z. For these ends, the host device shall notify the motion sensor during the calibration which direction of motion is considered forward and which is reverse. Angular Motion Measurement

[0088] The motion sensor logic divides the circle with a center point 701 into a number of sectors starting from any point, for example, 703. As shown in FIG. 7, in one example, there are 40 sectors of equal angle numbered 711 as the first sector, 712 is the second, 713 is the third, and so forth. Each sector creates an angle of 9 degrees. This gives a precision of measurement of 9 degrees. If a point 705 falls in the 6th sector 716, then the angle 707 between point 703 and 705 can be calculated as follows:

Angle = (Sector Number - 1) x 360 / (Total number of sectors); [0089] In the above example:

Angle = ( 6th sector - 1 ) x 360 / 40 = 45 degrees.

[0090] During measurement, point 705 can be shifted off the ideal circle due to an error in measurement. However, if it falls anywhere within the sector 716 it is still considered 45 degrees apart from the starting point 703. The number of sectors can be selected based on a quality of the signal and measurement noise in still position. The number of sectors and therefore a precision can be changed dynamically based on certain conditions. For example, the motion sensor can start with 360 sectors, 1 degree per sector, and then decrease number of sectors to 180 with 2 degrees per sector, 120 with 3 degrees per sector and so on. If the signal becomes noisy the number of sectors can be decreased hence reducing precision but increasing stability of the measurement.

[0091] It is apparent to a person of ordinary skills that the similar logic of angular calculation can be adopted to other objects which can pivot or rotate around certain point in space. Such objects have been listed above. The algorithm may be slightly adjusted to accommodate certain differences in motion but the principal remains the same using Earth's magnetic pole as a point of reference for motion characteristics measurement.

Example Method for Pivotal Motion Sensing

[0092] FIG. 8 is a process flow diagram showing a method 800 for pivotal motion sensing according to an example embodiment. Method 800 may be performed by processing logic that may comprise hardware (e.g., decision-making logic, dedicated logic, programmable logic, application-specific integrated circuit), software, or a combination of both. In one example embodiment, the processing logic refers to motion sensor 105 and/or host device 110. Below recited operations of method 800 may be implemented in an order different than described and shown in the figure. Moreover, method 800 may have additional operations not shown herein, but which can be evident for those skilled in the art from the present disclosure. Method 800 may also have fewer operations than outlined below and shown in FIG. 8.

[0093] Method 800 commences at operation 805 when at least one magnetic field sensor (magnetometer) 120 repeatedly measures Earth's magnetic field relative to a magnetic pole of the Earth and generates Earth's magnetic field data which represents a pivotal motion of at least one part of the individual or the pivoting object. At operation 810, data processor 130 processes the Earth's magnetic field data to produce pivotal motion data characterizing the at least one pivotal motion of the at least one part of the individual or the pivoting object. At operation 815, data storage 125 stores, at least temporary, the pivotal motion data. Further, at operation 820, transmitter 135 transmits the pivotal motion data from data storage 125 to host device 110 at predetermined times.

[0094] Method 800 may require prior calibration of motion sensor 105, which is performed as follows. First, motion sensor 105 receives a calibration instruction from host device 110. In response to the calibration instruction, magnetic field sensor (magnetometer) measures the Earth's magnetic field and generates Earth's magnetic field data. Further, data processor 130 produces activity-specific motion data from the Earth's magnetic field data at a sampling frequency, where the activity-specific motion data includes a rotational motion in each plane of a Cartesian coordinate space. For example, the activity-specific motion data can be produced when an individual should make certain predetermined motions. Further, transmitter 135 transmits an actual rotational or pivotal data to host device 110 and host device 110 or data processor 130 associates the actual rotational or pivotal data with the activity-specific motion data.

[0095] Method 800 may further include a calibration process as follows. First, motion sensor 105, host device 110, or both motion sensor 105 and host device 110 perform calibration of a center of a pivotal motion based on at least three samples of the Earth's magnetic field data related to the at least one pivotal motion of the at least one part of the individual or the pivoting object. Further, motion sensor 105, host device 110, or both motion sensor 105 and host device 110 perform calibration of an angle of the pivotal motion of the at least one part of the individual or the pivoting object based on pivoting the at least one part of the individual or the pivoting object on a predetermined angle.

[0096] Method 800 may further include another calibration process as follows.

First, motion sensor 105, host device 110, or both motion sensor 105 and host device 110 perform calculation of a center of the pivotal motion based on at least three samples of the Earth's magnetic field data related to the at least one pivotal motion of the at least one part of the individual or the pivoting object. Further, a circle with the center of the pivotal motion is divided into a predetermined number of segments using three sets of a two- dimensional Cartesian coordinate system: X and Y, Y and Z, X and Z. Then, motion sensor 105, host device 110, or both motion sensor 105 and host device 110 perform calculation of the angle of the at least one pivotal motion of the at least one part of the individual or the pivoting object using a matching number of the segments between two points of the pivotal motion.

[0097] FIG. 9 is a high-level block diagram illustrating a computing device 900 suitable for implementing the methods described herein. In particular, computing device 900 may be used for implementing the methods for pivotal motion sensing as described herein. Computing device 900 may include, be, or be an integral part of one or more of a variety of types of devices, such as a general-purpose computer, desktop computer, laptop computer, tablet computer, server, netbook, mobile phone, smartphone, infotainment system, smart television device, among others. In some embodiments, computing device 900 can be regarded as an instance of host device 110 and optionally motion sensor 105.

[0098] As shown in FIG. 9, computing device 900 includes one or more processors 910, memory 920, one or more mass storage devices 930, one or more output devices 950, one or more input devices 960, network interface 970, one or more optional peripheral devices 980, and a communication bus 990 for operatively interconnecting the above-listed elements. Processors 910 can be configured to implement functionality and/or process instructions for execution within computing device 900. For example, processors 910 may process instructions stored in memory 920 or instructions stored on storage devices 930. Such instructions may include components of an operating system or software applications.

[0099] Memory 920, according to one example, is configured to store

information within computing device 900 during operation. For example, memory 920 can store pivotal or rotational motion data. Memory 920 can be an instance of data storage 125. Further, memory 920, in some example embodiments, may refer to a non- transitory computer-readable storage medium or a computer-readable storage device. In some examples, memory 920 is a temporary memory, meaning that a primary purpose of memory 920 may not be long-term storage. Memory 920 may also refer to a volatile memory, meaning that memory 920 does not maintain stored contents when memory 920 is not receiving power. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art. In some examples, memory 920 is used to store program instructions for execution by processors 910.

Memory 920, in one example, is used by software applications. Generally, software applications refer to software applications suitable for implementing at least some functionality as described herein.

[00100] Mass storage devices 930 can also include one or more transitory or non- transitory computer-readable storage media or computer-readable storage devices. In some embodiments, mass storage devices 930 may be configured to store greater amounts of information than memory 920. Mass storage devices 930 may be also configured for long-term storage of information. In some examples, mass storage devices 930 include non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, solid-state discs, flash memories, forms of electrically programmable memories (EPROM) or electrically erasable and programmable memories, and other forms of non- volatile memories known in the art.

[00101] Still referencing to FIG. 9, computing device 900 may also include one or more input devices 960. Input devices 960 may be configured to receive input from a user through tactile, audio, video, or biometric channels. Examples of input devices 960 may include a keyboard, keypad, mouse, trackball, touchscreen, touchpad, microphone, video camera, image sensor, fingerprint sensor, or any other device capable of detecting an input from a user or other source, and relaying the input to computing device 900 or components thereof. Output devices 950 may be configured to provide output to a user through visual or auditory channels. Output devices 950 may include a video graphics adapter card, display, such as liquid crystal display (LCD) monitor, light emitting diode (LED) monitor, or organic LED monitor, sound card, speaker, lighting device, projector, or any other device capable of generating output that may be intelligible to a user. Output devices 1650 may also include a touchscreen, presence-sensitive display, or other input/output capable displays known in the art.

[00102] Computing device 900 can also include network interface 970. Network interface 970 can be utilized to communicate with external devices via one or more networks such as one or more wired, wireless, or optical networks including, for example, the Internet, intranet, local area network, wide area network, cellular phone networks (e.g., Global System for Mobile communications network, Long-Term

Evolution communications network, packet switching communications network, circuit switching communications network), Bluetooth radio, ANT+, and an IEEE 802.11 -based radio frequency network, among others. Network interface 970 may be a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information.

[00103] An operating system of computing device 900 may control one or more functionalities of computing device 900 or components thereof. For example, the operating system may interact with the software applications and may facilitate one or more interactions between the software applications and processors 910, memory 920, storage devices 930, input devices 960, output devices 950, and network interface 970. The operating system may interact with or be otherwise coupled to software applications or components thereof. In some embodiments, software applications may be included in operating system.

[00104] Thus, a method and system for pivotal motion sensing have been described. Although embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes can be made to these example embodiments without departing from the broader spirit and scope of the present application. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.

Claims

A motion sensor for tracking a pivotal motion of an individual or a pivoting object, the motion sensor comprising:

at least one magnetic field sensor configured to repeatedly generate Earth's magnetic field data relative to a magnetic pole of the Earth, wherein the Earth's magnetic field data represents at least one pivotal motion of at least one part of the individual or the pivoting object;

a data processor operatively connected to the at least one magnetic field sensor and configured to process the Earth's magnetic field data to produce pivotal motion data characterizing the at least one pivotal motion of the at least one part of the individual or the pivoting object; and

a transmitter operatively connected to the data processor and configured to repeatedly transmit the pivotal motion data to a host device.

The sensor of claim 1, wherein the motion data includes angular data characterizing a rotational motion of the at least one part of the individual or the pivoting object.

The sensor of claim 1, wherein the data processor is further configured to:

recognize a type of activity associated with the individual or the pivoting object based on the Earth's magnetic field data; and

produce activity-specific motion data depending on the type of activity and based on the Earth's magnetic field data.

The sensor of claim 3, wherein the activity-specific motion data includes one or more of the following: a cadence, a step distance, a step type, and a foot motion pivotal angle, the type of activity including walking, sport walking, or running.

The sensor of claim 3, wherein the activity-specific motion data includes a step count prior to a jump, an individual step distance, a jump height, and a jump air time, the type of activity being high jumping, length jumping, or basketball.

The sensor of claim 3, wherein the activity-specific motion data includes one or more of the following: a step count, an individual step distance, a foot swing distance, and a foot swing speed, the type of activity being soccer or football.

The sensor of claim 3, wherein the activity-specific motion data includes one or more of the following: a cadence, a stride distance, and a stride type, the type of activity being roller blading, skating or cross-country skiing.

The sensor of claim 3, wherein the activity-specific motion data includes one or more of the following: a rotation angle, a rotation count, a rotation direction selected from a forward direction and a reverse direction, and a spinning speed, the type of activity being cycling, spinning, or rotation-like activity.

9. The sensor of claim 3, wherein the activity-specific motion data includes one or more of the following: an arm swing and pivotal angle relative to a shoulder, an elbow or a wrist joint of the individual, an arm swing distance, an arm swing speed, and an arm swing type when the type of activity is tennis, squash, badminton, racket sport, golf, hockey, baseball, cricket, volleyball, and handball.

10. The sensor of claim 3, wherein the activity-specific motion data includes an arm movement cadence or a wrist twist angle, the type of activity being swimming.

11. The sensor of claim 3, wherein the activity-specific motion data includes an arm movement cadence or a stride count, the type of activity being walking, sport walking, running, skating, or cross-country skiing.

12. The sensor of claim 3, wherein the activity-specific motion data includes parameters indicating one or more of the following: sleeping, rolling from side to side, getting up, laying down, falling, walking, and sitting, the type of activity being a general activity.

13. The sensor of claim 3, wherein the activity-specific motion data includes one or more of the following: a cadence, a stroke duration, an oar swing pivotal angle, an oar blade rotation angle, a pull duration, and a recovery duration, the type of activity being rowing.

14. The sensor of claim 3, wherein the activity-specific motion data includes one or more of the following: a paddling cadence, a stroke duration, a paddle swing angle, and a paddle blade rotation angle, the type of activity being paddling with a paddle having two blades.

15. The sensor of claim 3, wherein the activity-specific motion data includes one or more of the following: a paddling cadence, a stroke duration, a paddle blade pivot angle, and a paddle swing angle, the type of activity being paddling with a paddle having one blade.

16. The sensor of claim 3, wherein the activity-specific motion data includes a cycling cadence, cadence crank revolution angle, and a crank revolution direction, the type of activity being cycling.

17. The sensor of claim 3, wherein the activity-specific motion data includes a turns count or a turn angle, the type of activity being downhill skiing, snowboarding or a sled.

18. The sensor of claim 1, wherein the data processor is further configured to:

receive a calibration instruction from the host device;

in response to the calibration instruction, obtain the Earth's magnetic field data from the at least one magnetic field sensor;

produce activity-specific motion data from the Earth's magnetic field data at a sampling frequency, wherein the activity-specific motion data includes a rotational motion in each plane of a Cartesian coordinate space;

transmit an actual rotational or pivotal data to the host device; and associate the actual rotational or pivotal data with the activity-specific motion data.

19. The sensor of claim 1, wherein the at least one magnetic field sensor consists of a microelectromechanical magnetometer, wherein the pivotal motion data is produced without information obtained from an accelerometer or a gyroscope.

20. A system for pivotal motion sensing, the system comprising:

a plurality of motion sensors for tracking pivotal motion of at least a part of an individual or a pivoting object, each of the motion sensors comprising:

at least one magnetic field sensor configured to repeatedly generate Earth's magnetic field data relative to a magnetic pole of the Earth, wherein the Earth's magnetic field data represents at least one pivotal motion of the at least one part of the individual or the pivoting object;

a transmitter operatively connected to the data processor and configured to repeatedly transmit the pivotal motion data to a host device;

wherein the host device is configured to receive the pivotal motion data from each of the plurality of motion sensors, correlate the pivotal motion data from each of the plurality of motion sensors with one another, and process the pivotal motion data from each of the plurality of motion sensors to produce a common visual or audio representation including the at least one motion of the at least one part of the individual or the pivoting object.

21. A method for pivotal motion sensing, the method comprising:

repeatedly generating, by at least one magnetic field sensor, Earth's magnetic field data relative to a magnetic pole of the Earth, wherein the Earth's magnetic field data represents a pivotal motion of at least one part of the individual or the pivoting object; processing, by a data processor operatively connected to the at least one magnetic field sensor, the Earth's magnetic field data to produce pivotal motion data characterizing the at least one pivotal motion of the at least one part of the individual or the pivoting object; and

repeatedly transmitting, by a transmitter operatively connected to the data processor, the pivotal motion data to a host device.

22. The method of claim 21, further comprising:

calibrating a center of the pivotal motion based on at least three samples of the Earth's magnetic field data related to the at least one pivotal motion of the at least one part of the individual or the pivoting object; and

calibrating an angle of the pivotal motion of the at least one part of the individual or the pivoting object based on pivoting the at least one part of the individual or the pivoting object on a predetermined angle.

23. The method of claim 21, further comprising:

calculating a center of the pivotal motion based on at least three samples of the Earth's magnetic field data related to the at least one pivotal motion of the at least one part of the individual or the pivoting object;

dividing a circle with the center of the pivotal motion into a predetermined number of segments using three sets of a two- dimensional Cartesian coordinate system: X and Y, Y and Z, X and Z; and

calculating the angle of the at least one pivotal motion of the at least one part of the individual or the pivoting object using a matching number of the segments between two points of the pivotal motion.