SYSTEM AND METHOD FOR AUTOMATIC LOCATION DETECTION FOR WEARABLE SENSORS

1 . A method for activity monitoring comprising:

collecting kinematic data from at least one kinematic activity sensor coupled to a user;

generating a set of base kinematic metrics;

assessing a set of sensor state discriminators and identifying a kinematic monitoring mode; and

activating the kinematic monitoring mode at the at least one kinematic activity sensor.

2 . The method of claim 1 , wherein identifying a kinematic monitoring mode comprises determining position of an activity sensor, wherein the identified kinematic monitoring mode is associated to the determined position.

3 . The method of claim 2 , wherein assessing the set of sensor state discriminators comprises assessing at least a first regional discriminator to select one of a set of location candidates.

4 . The method of claim 2 , wherein assessing the set of sensor state discriminators further comprises assessing a secondary regional discriminator.

5 . The method of claim 2 , wherein assessing the set of sensor state discriminators further comprises assessing an activity discriminator for at least one of the location candidates.

6 . The method of claim 2 , wherein, if a first location candidate is selected, further assessing a right-left discriminator and identifying a right or left location-specific kinematic monitoring mode

7 . The method of claim 1 , wherein activating the kinematic monitoring mode at the at least one kinematic activity sensor comprises generating a set of biomechanical signals through processing modules customized to the identified kinematic monitoring mode.

8 . The method of claim 1 , wherein activating the kinematic monitoring mode at the at least one kinematic activity sensor comprises: for a first kinematic monitoring mode generating a first set of biomechanical signals; for a second kinematic monitoring mode generating a second set of biomechanical signals; wherein the first set of biomechanical signals is different from the second set of biomechanical signals.

9 . The method of claim 1 , wherein collecting kinematic data from at least one kinematic activity sensor coupled to a user further comprises collecting kinematic data from a plurality sensors positioned at distinct locations of the user; wherein generating the base kinematic metrics comprises generating at least a first set of relative metrics, where a relative metric compares metrics from at least two activity sensors; and wherein identifying a kinematic monitoring mode comprises identifying a kinematic monitoring mode for each of the plurality of sensors.

10 . The method of claim 9 , wherein identifying a kinematic monitoring mode for each of the plurality of sensors further comprises selectively activating a kinematic monitoring mode of a first activity sensor based in part on the kinematic monitoring mode of at least a second activity sensor.

11 . The method of claim 1 , wherein identifying a kinematic monitoring mode comprises selecting a kinematic monitoring mode selected from a set of kinematic monitoring modes that comprises at least a walking gait monitoring mode, a posture monitoring mode, and a running monitoring mode.

12 . The method of claim 11 , wherein the set of kinematic monitoring modes further comprises an exercise training monitoring mode and a neck posture monitoring mode.

13 . The method of claim 1 , wherein identifying a kinematic monitoring mode comprises selecting a kinematic monitoring mode selected from a set of kinematic monitoring modes that comprises at least a foot-positioned monitoring mode, a pelvic-positioned monitoring mode, and an upper-body-positioned monitoring mode.

14 . The method of claim 1 , wherein identifying a kinematic monitoring mode comprises selecting a kinematic monitoring mode selected from a set of kinematic monitoring modes that comprises at least a foot-positioned walking gait monitoring mode, a pelvic-positioned walking gait monitoring mode, a pelvic-positioned posture monitoring mode, and an upper-body-positioned posture monitoring mode.

15 . The method of claim 1 , wherein the set of base kinematic metrics includes step impact magnitude; wherein assessing a set of sensor state discriminators and identifying a kinematic monitoring mode comprises:

for a first regional discriminator, checking for step impact magnitude greater than 4 G's and determining a foot position if the condition is valid or a non-foot position if the value is not valid; and

identifying a foot-positioned monitoring mode if the first regional discriminator determines a foot position.

16 . The method of claim 15 , wherein the set of base kinematic metrics includes average peak rotation rate wherein assessing a set of sensor state discriminators and identifying a kinematic monitoring mode further comprises:

for a second regional discriminator assessed upon detecting the non-foot position, checking if the average peak rotation rate around a vertical axis is greater than an angular velocity threshold and determining a pelvis position if valid and a chest position if not valid;

identifying a pelvis-positioned monitoring mode if the second regional discriminator determines a pelvis position; and

identifying a chest-positioned monitoring mode if the second regional discriminator determines a chest position.

17 . The method of claim 1 , wherein at least one of the sensor state discriminators is a machine learning model.

18 . The method of claim 17 , wherein the machine learning model is trained on labeled data of the user.

19 . The method of claim 1 , further comprising detecting a change in the activity and updating the kinematic monitoring mode at the kinematic activity sensor.