WIRELESS IN-SHOE PHYSICAL ACTIVITY MONITORING

• US 20180049703A1
• Filed: 08/17/2017
• Published: 02/22/2018
• Est. Priority Date: 08/18/2016
• Status: Active Grant

• Current Assignee: SigmaSense, LLC
• Original Assignee: SigmaSense, LLC
• Inventors: Markison, Timothy W., Kiaei, Sayfe, McCoy, Gary
• Granted Patent: US 11,129,547 B2
• Time in Patent Office: Days
• CPC Class Codes: A43B 3/34 with electrical or electronic arrangements
• CPC Class Codes: A61B 2560/0214 of power generation or supply
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1036 Measuring load distribution, e.g. podologic studies
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A63B 24/0062 Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: A63B 71/06 Indicating or scoring devices for games or players , or for other sports activities
• CPC Class Codes: G01S 13/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S13/46
• CPC Class Codes: G01S 13/46 Indirect determination of position data
• CPC Class Codes: G01S 13/58 Velocity or trajectory determination systems; Sense-of-movement determination systems
• CPC Class Codes: G01S 13/726 Multiple target tracking
• CPC Class Codes: G01S 13/88 Radar or analogous systems specially adapted for specific applications electromagnetic prospecting or detecting of objects, e.g. near-field detection, G01V3/00
• CPC Class Codes: G01S 2013/468 : by Triangulation, i.e. two antennas or two sensors determine separately the bearing, direction or angle to a target, whereby with the knowledge of the baseline length, the position data of the target is determined
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: H04Q 2209/40 : using a wireless architecture
• CPC Class Codes: H04Q 2209/84 : Measuring functions
• CPC Class Codes: H04Q 2209/845 : where the measuring is synchronized between sensing devices
• CPC Class Codes: H04Q 9/00 : Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom

Abstract

A method includes obtaining foot force data that is created by sampling, in accordance with a sampling signal, data from pressure sensing elements of a shoe sensor system. The method further includes obtaining three-dimensional foot data that is created by sampling, in accordance with the sampling signal, data from an accelerometer of the shoe sensor system. The method further includes correlating the foot force data and the three-dimensional foot data in accordance with the sampling signal to produce correlated foot data. The method further includes processing the correlated foot data to determine one or more of: distance traveled during a time period, stride length data, time duration, fatigue indication, injury prevention indicators, elevation tracking for the time period, running optimization, and rotational sport optimization.

Claims

1. A method for execution by a processing module, the method comprises:

obtaining foot force data that is created by sampling, in accordance with a sampling signal, data from a plurality of pressure sensing elements of a shoe sensor system, wherein the plurality of sensing elements that are distributed in a pattern having a shape corresponding to an outline a human foot;

obtaining three-dimensional foot data that is created by sampling, in accordance with the sampling signal, data from a first accelerometer of the shoe sensor system, wherein the first accelerometer is positioned within the pattern;

correlating the foot force data and the three-dimensional foot data in accordance with the sampling signal to produce correlated foot data; and

processing the correlated foot data to determine one or more of;

distance traveled during a time period, stride length data, time duration, fatigue indication, injury prevention indicators, elevation tracking for the time period, running optimization, and rotational sport optimization.

2. The method of claim 1 further comprises:

the obtaining the foot force data includes receiving, by the processing module of the shoe sensor system, the foot force data from the plurality of pressure sensing elements; and

the obtaining the three-dimensional foot data includes receiving, by the processing module of the shoe sensor system, the three-dimensional foot data from the first accelerometer.

3. The method of claim 1 further comprises:

the obtaining the foot force data includes receiving, by the processing module of a computing device, the foot force data from a transmitter of the shoe sensor system; and

the obtaining the three-dimensional foot data includes receiving, by the processing module of the computing device, the three-dimensional data from the transmitter of the shoe sensor system.

4. The method of claim 1, wherein the foot force data comprises one or more of:

from a first pressure sensing element of the plurality of pressure sensing elements;

a first pressure sensor indicator;

a first plurality of pressure sensed values;

a first plurality of representations of sensed pressure levels; and

a first plurality of pressure measurements; and

from a second pressure sensing element of the plurality of pressure sensing elements;

a second pressure sensor indicator;

a second plurality of pressure sensed values;

a first plurality of representations of sensed pressure levels; and

a first plurality of pressure measurements.

5. The method of claim 1, wherein the three-dimensional foot data comprises one or more of:

an accelerometer identifier;

an x-y-z origin coordinate;

a plurality of x-y-z coordinate values;

a first plurality of representations of x-y-z coordinate values; and

a first plurality of x-y-z coordinates.

6. The method of claim 1, wherein the correlating the foot force data and the three-dimensional foot data comprises:

for a first sampling interval of the sampling signal;

identifying a first foot force data point of the foot force data and a first three-dimensional foot data point of the three-dimensional foot data; and

linking the first foot force data point with the first three-dimensional foot data point and with the first sampling interval; and

for a second sampling interval of the sampling signal;

identifying a second foot force data point of the foot force data and a second three-dimensional foot data point of the three-dimensional foot data; and

linking the second foot force data point with the second three-dimensional foot data point and with the second sampling interval.

7. The method of claim 1, wherein the processing the correlated foot data to determine the distance traveled during the time period comprises:

determining a first x-y coordinate from a first three-dimensional foot data point that corresponds to the beginning of the time period;

determining a next x-y coordinate from a next three-dimensional foot data point that corresponds to a next sampling point of the sampling clock within the time period;

determining a next delta distance based on a difference between the first x-y coordinate and the next x-y coordinate;

adding the next delta distance to an accumulation of previous delta distances to produce an updated accumulation of delta distances;

when the next sampling point corresponds to the end of the time period, provided the updated accumulation of delta distances as the distance traveled during the time period; and

when the next sampling point does not correspond to the end of the time period, repeating all but the first step for another next three-dimensional foot data point until the next sampling point corresponds to the end of the time period.

8. The method of claim 1, wherein the processing the correlated foot data to determine the stride length data comprises:

determining a first left x-y coordinate from a first left three-dimensional foot data point that corresponds to a left foot being in contact with a surface;

determining a first right x-y coordinate from a first right three-dimensional foot data point that corresponds to a right foot being in contact with the surface; and

determining a left foot to right foot stride length based on the first left x-y coordinate and the first right x-y coordinate.

9. The method of claim 1, wherein the processing the correlated foot data to determine the time duration comprises:

counting clock cycle of the clock signal from a beginning of the time duration until an end of the time duration to produce a clock cycle count; and

interpreting the clock cycle count in light of a clock rate of the clock signal to determine the time duration.

10. The method of claim 1, wherein the processing the correlated foot data to determine the fatigue indication comprises:

detecting, based on changes in the correlated foot data over time within the time period, one or more of a shortening of stride length, a slowing of pace, and a change in foot force; and

interpreting the one or more of a shortening of stride length, a slowing of pace, and a change in foot force to determine a fatigue level.

11. The method of claim 1, wherein the processing the correlated foot data to determine the injury prevention indicators comprises:

detecting, based on changes in the correlated foot data over time within the time period, one or more of an imbalance in foot force between the feet, a change in foot forces of one or both feet, an imbalance in stride lengths, an imbalance in stride height between the feet, and foot movement outside of a movement deviation range,

interpreting the one or more of the imbalance in foot force between the feet, the change in foot forces of one or both feet, the imbalance in stride lengths, the imbalance in stride height between the feet, and the foot movement outside of the movement deviation range to identify a potential injury; and

determining a preventive measure based on the potential injury and the one or more of the imbalance in foot force between the feet, the change in foot forces of one or both feet, the imbalance in stride lengths, the imbalance in stride height between the feet, and the foot movement outside of the movement deviation range.

12. The method of claim 1, wherein the processing the correlated foot data to determine the elevation tracking for the time period comprises:

determining a first z coordinate from a first three-dimensional foot data point that corresponds to the beginning of the time period;

determining a next z coordinate from a next three-dimensional foot data point that corresponds to a next sampling point of the sampling clock within the time period;

determining a next delta distance based on a difference between the first z coordinate and the next z coordinate;

when the next delta distance is greater than or equal to zero, adding the next delta distance to an accumulation of previous delta distances to produce an updated accumulation of delta distances;

when the next sampling point corresponds to the end of the time period, provided the updated accumulation of delta distances as the elevation change that occurred during the time period; and

when the next sampling point does not correspond to the end of the time period, repeating all but the first step for another next three-dimensional foot data point until the next sampling point corresponds to the end of the time period.

13. The method of claim 1, wherein the processing the correlated foot data to determine the running optimization comprises one or more of:

interpreting the correlated foot data to determine that foot positioning while running is offset by at least a foot positioning threshold from an optimal foot positioning and providing a corrective measure to reduce the offset;

interpreting the correlated foot data to determine that, while running, weight distribution between the left and right feet is imbalanced and providing a correct measure to reduce the weight distribution imbalance;

interpreting the correlated foot data to determine that, while running, stride lengths are imbalanced between a left-to-right stride and a right-to-left stride and providing a correct measure to reduce the stride imbalance;

interpreting the correlated foot data to determine that, while running, ground reaction forces are below an optimal level and providing a correct measure to improve ground reaction force; and

interpreting the correlated foot data to determine that, while running, ground contact time is above an optimal level and providing a correct measure to reduce ground contact time.

14. The method of claim 1, wherein the processing the correlated foot data to determine the rotational sport optimization comprises:

determining ground reaction force during performance of an athletic movement of a rotational sport;

determining weight force vector distribution between medial and lateral side of foot and between forefoot and heel based on the ground reaction forces; and

when the weight force vector distribution is less than optimal, determining a corrective measure to optimize the weight force vector distribution during performance of the athletic movement.

15. The method of claim 1 further comprises:

obtaining biometric data from one or more biometric sensors of the shoe sensor system; and

interpreting the biometric data to provide information regarding one or more of temperature, moisture, heart rate, and respiration rate.

16. The method of claim 1, wherein a pressure sensing element of the plurality of pressure sensing elements comprises one or more of:

resistive pressure sensor, a piezoelectric pressure sensor, a capacitive pressure sensor, and an inductive pressure sensor.

17. The method of claim 1 further comprises:

obtaining second three-dimensional foot data that is created by sampling, in accordance with the sampling signal, data from a second accelerometer of the shoe sensor system, wherein the second accelerometer is positioned within the pattern at a distance from the first accelerometer;

correlating the foot force data, the three-dimensional foot data, and the second three-dimensional foot data in accordance with the sampling signal to produce the correlated foot data; and

processing the correlated foot data to determine various foot orientations during the time period.