Systems and methods for sensorimotor rehabilitation

• US 9,311,789 B1
• Filed: 04/09/2014
• Issued: 04/12/2016
• Est. Priority Date: 04/09/2013
• Status: Active Grant

• Current Assignee: BioSensics, LLC (Best Buy Co., Inc.)
• Original Assignee: BioSensics, LLC (Best Buy Co., Inc.)
• Inventors: Gwin, Joseph T.
• Primary Examiner(s): Previl, Daniel
• Application Number:
  US14/248,956
• Time in Patent Office: 734 Days
• Field of Search: 340/573.1, 340/539.1, 340/539.12, 340/539.23, 340/539.24, 340/539.25, 340/648, 340/691.6, 340/5.52, 340/5.53, 340/5.61, 340/5.64, 340/7.51
• US Class Current: 1/1
• CPC Class Codes: A61B 2505/09 Rehabilitation or training
• 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 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/74 Details of notification to user or communication with user or patient indicating measured values G01D7/00; user input means input or output arrangements for computers G06F3/00
• CPC Class Codes: G08C 19/00 Electric signal transmission systems G08C17/00 takes precedence

Abstract

A sensorimotor rehabilitation system can analyze body movement kinematics of a person, and in particular analyze the use of limbs (e.g., upper limbs) to provide feedback for sensorimotor rehabilitation. Parameters of body movement (e.g., quantity and type of body movement) can be assessed based on data recorded from an inertial sensor affixed or connected to, for example, an upper limb of a person or outer clothing such as a sleeve. Automated feedback can be provided to the user to improve sensorimotor rehabilitation.

Claims

1. A method for sensorimotor rehabilitation, the method comprising:

receiving one or more signals generated by at least one body-worn inertial sensor supported by an affected upper limb of a person to automatically detect movement of the affected limb during activities of daily living;

with processing electronics comprising digital logic circuitry, processing sensor data derived from the one or more signals to identify a characteristic associated with at least one of a quantity of upper limb movement of the person during the activities of daily living or a type of upper limb movement of the person during the activities of daily living; and

based at least in part on the identified characteristic, electronically generating information usable to present feedback usable in sensorimotor rehabilitation of the affected upper limb.

2. The method of claim 1, wherein the feedback is indicative of a corrective action for improving sensorimotor rehabilitation.

3. The method of claim 1, wherein the body-worn inertial sensor is attached to a wrist of the affected upper limb of the person.

4. The method of claim 1, wherein the one or more signals are generated by at least two body-worn inertial sensors, wherein a first body-worn inertial sensor of the at least two body-worn inertial sensors is supported by the affected upper limb of the person, and a second body-worn inertial sensor of the at least two body-worn inertial sensors is supported by an unaffected upper limb of the person, and the affected limb exhibits more limited motor control than the unaffected limb due to nervous system damage of the person.

5. The method of claim 4, wherein the characteristic is associated with the quantity of upper limb movement of the person, and wherein the characteristic comprises a ratio of affected upper limb movement to unaffected upper limb movement.

6. The method of claim 5, wherein the ratio of affected upper limb movement to unaffected upper limb movement is associated with a quantity of one or more types or groups of upper limb movements.

7. The method of claim 5, wherein a packaging containing at least one of the at least two body-worn inertial sensors comprises a graphical display configured to display the ratio of affected upper limb movement to unaffected upper limb movement.

8. The method of claim 5, wherein at least one of the at least two body-worn inertial sensors is configured to provide at least one of auditory positive feedback, visual positive feedback, or tactile positive feedback to a user when the ratio of affected upper limb movement to unaffected upper limb movement is equal to or greater than a predetermined ratio or configured to provide at least one of auditory negative feedback, visual negative feedback, or tactile negative feedback to the user when the ratio of affected upper limb movement to unaffected upper limb movement is less than the predetermined ratio.

9. The method of claim 5, wherein the ratio corresponds to a quantity of movement of the affected limb relative to a quantity of the unaffected limb, wherein the quantity of movement of the affected limb and the quantity of the unaffected limb are different.

10. The method of claim 1, wherein the characteristic is associated with the quantity of upper limb movement of the person, and wherein the quantity of upper limb movement of the person is associated with a quantity of one or more types or groups of upper limb movements.

11. The method of claim 10, further comprising, with the processing electronics, determining the one or more types or groups of upper limb movements by at least one of:

estimating upper limb kinematics from measured the sensor data and identifying the one or more types or groups based on the estimated upper limb kinematics;

or

applying classification rules to the measured the sensor data, the classification rules based at least in part on artificial intelligence classification methods or statistical classification methods.

12. The method of claim 10, further comprising displaying a sequence of images depicting a sensorimotor rehabilitation exercise to be performed by the person, the sensorimotor rehabilitation exercise comprising the one or more types or groups of upper limb movements.

13. The method of claim 12, wherein the sequence of images change to reflect changes in upper limb movement that are identified by the processing during the sensorimotor rehabilitation exercise.

14. The method of claim 1, further comprising wirelessly transmitting to a separate computer system the information usable to present the feedback.

15. The method of claim 1, further comprising logging the information over a predetermined period of time for later access of the feedback information, wherein the feedback comprises a usage log of the affected upper limb over the predetermined period of time based at least in part on the identified characteristic.

16. The method of claim 15, further comprising determining a performance score based on the logged information indicating a level of compliance of a patient with sensorimotor rehabilitation therapy.

17. The method of claim 1, wherein the affected upper limb is able to be moved by the person independently relative to movement of an unaffected upper limb of the person.

18. A sensorimotor rehabilitation system, the system comprising:

at least one body-worn inertial sensor configured to generate one or more signals; and

processing electronics in communication with the at least one body-worn inertial sensor and comprising digital logic circuitry, the processing electronics configured to;

process sensor data derived from the one or more signals according to a sensorimotor analysis algorithm, wherein the sensor data is collected while the body-worn inertial sensor is supported by a finger of a person, a hand of the person, a wrist of the person, a forearm of the person, or an upper arm of the person;

based at least in part on results of the processing of the sensor data, identify a characteristic associated with at least one of a quantity of limb movement of the person during activities of daily living or a type of limb movement of the person during the activities of daily living; and

based at least in part on the identified characteristic, generate information usable to present feedback.

19. The system of claim 18, wherein the one or more signals are generated by at least two body-worn inertial sensors, wherein a first body-worn inertial sensor of the at least two body-worn inertial sensors is supported by an affected upper limb of the person, and a second body-worn inertial sensor of the at least two body-worn inertial sensors is supported by an unaffected upper limb of the person, and the affected limb exhibits more limited motor control than the unaffected limb due to nervous system damage of the person.

20. The system of claim 19, wherein the characteristic is associated with the quantity of upper limb movement of the person, and wherein the characteristic comprises a ratio of affected upper limb movement to unaffected upper limb movement.

21. The system of claim 20, wherein the ratio corresponds to a quantity of movement of the affected limb relative to a quantity of the unaffected limb, wherein the quantity of movement of the affected limb and the quantity of the unaffected limb are different.

22. The system of claim 18, further comprising a wireless transmitter configured to send to a separate computer system the information usable to present the feedback.

23. The system of claim 18, wherein the feedback information is usable to generate visual feedback indicative of one or more of the identified characteristic and a suggested corrective action.

24. The system of claim 18, wherein the processing electronics are further configured to electronically store the feedback information over a predetermined period of time for later access of the feedback information, wherein the feedback information comprises a usage indication of limb movement over the predetermined period of time based at least in part on the identified characteristic.

25. Non-transitory computer storage that stores executable code that directs computer hardware to at least:

process sensor data derived from one or more signals generated by at least one body-worn inertial sensor according to a sensorimotor analysis algorithm, the sensor data obtained when the body-worn inertial sensor is supported by a finger of a person, a hand of the person, a wrist of the person, a forearm of the person, or an upper arm of the person;

based at least in part on results of the processing of the sensor data, identify a characteristic associated with at least one of a quantity of limb movement of the person during activities of daily living or a type of limb movement of the person during the activities of daily living; and

based on the identified characteristic, generating information usable to present feedback.