Smart garment and method for detection of body kinematics and physical state

• US 9,285,788 B2
• Filed: 08/20/2013
• Issued: 03/15/2016
• Est. Priority Date: 08/20/2013
• Status: Active Grant

• Current Assignee: Raytheon BBN Technlogies Corp. (Rtx Corporation)
• Original Assignee: Raytheon BBN Technlogies Corp. (Rtx Corporation)
• Inventors: Nicoletti, Michael John, Ritter, Scott Evan, Beal, Jacob Stuart Michael, Daily, Matthew Patrick, Holmes, Jason David, Park, Christopher Glenn
• Primary Examiner(s): Thangavelu, Kandasamy
• Application Number:
  US13/971,678
• Publication Number:
  US 20150057984A1
• Time in Patent Office: 938 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• 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 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4585 Evaluating the knee
• CPC Class Codes: A61B 5/4595 Evaluating the ankle
• CPC Class Codes: A61B 5/6804 Garments; Clothes
• 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: G05B 15/02 electric
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects

Abstract

A body garment including sensors distributed throughout the garment, each sensor senses body state information from a local surface area of a body; and sensor nodes in proximity to the plurality of sensors, each sensor node including a processor to receive sensing body state information from at least one of the plurality of sensors. Each processor is configured to receive body state information locally from sensors, to utilize the information to determine a local surface shape of the surface of a portion of the body part; and to exchange local surface shape information with neighboring sensor nodes. At least one processor of utilizes the local surface shape information received from the sensor nodes to generate one overall model of a surface shape of the entire surface of the body part covered by the garment.

Claims

1. A body garment for detecting body kinematics comprising:

a plurality of sensors distributed throughout the garment, each sensor being configured to sense body state information from a local surface area of a body part covered by the garment; and

a plurality of sensor nodes in proximity to the plurality of sensors respectively, each sensor node including a processor and configured to receive sensing body state information from at least one of the plurality of sensors;

wherein each processor is configured to receive body state information locally from one or more sensors in proximity to said each processor;

to utilize the information to determine a local surface shape of the surface of a portion of the body part covered by said one or more sensors; and

to exchange local surface shape information with neighboring sensor nodes, and

wherein at least one processor of at least one sensor node utilizes the local surface shape information received from the sensor nodes to generate one overall model of a surface shape of the entire surface of the body part covered by the garment.

2. The body garment of claim 1, wherein said at least one processor of at least one sensor node is further configured to generate body kinematics information from said overall model.

3. The body garment of claim 2, wherein at least one of the plurality of the processors is further configured to output said body kinematics information to control an actuator.

4. The body garment of claim 3, wherein said at least one of the plurality of the processors is further configured to adaptively control said actuator and respond to feedback provided by the plurality of sensors, sensor nodes or data streams coming from external devices.

5. The body garment of claim 2, wherein said at least one processor is configured to generate body kinematics information by taking snap shots of overall model at different times and compare the snap shots.

6. The body garment of claim 2, wherein said overall model includes a rate of change of the surface shape over time, and wherein said at least one processor is configured to generate body kinematics information using said rate of change of the surface shape.

7. The body garment of claim 2, wherein said body kinematics information include one or more of body pose, gait, and muscle activation of the subject wearing the garment.

8. The body garment of claim 2, wherein at least one of the plurality of the processors includes an interface for driving one or more of an actuator for mechanical assisting for walking, running or integration of a prosthetic limb, a haptic feedback device for delivering injury warnings, a biomechanical system for analyzing metabolic activity or athletic load endured by a subject wearing the garment, and a health monitoring system.

9. The body garment of claim 1, wherein said body state information include one or more of skin surface temperature, electrical nerve impulses, and whether a person has been at rest or is warmed up.

10. The body garment of claim 1, wherein the body garment comprises of a plurality of different portions, each portion configured to be coupled to and decoupled from the other portions.

11. The body garment of claim 1, wherein a first subset of the plurality of sensor nodes located on critical regions of the body part is configured to operate faster and denser than a second subset of the plurality of the sensor nodes located on stable regions of the body part.

12. The body garment of claim 1, wherein the plurality of sensors are configured in a plurality of manifolds, each manifold of group of sensors including a set of similar and related sensors in close proximity of each other that respond in an ordered distribution, wherein the body garment is capable of losing some fraction of sensors or displacement of some of the sensors on the garment and still generate said body state information.

13. The body garment of claim 1, wherein each processor is configured to perform adaptive processing to tune the local surface shape to exchange the tuned local surface shape information with neighboring sensor nodes to commend a plurality of actuators.

14. The body garment of claim 1, wherein each processor is configured to change degrees of freedom being measured by the garment according to the time of day, the temperature, and the health of the subject wearing the garment.

15. The body garment of claim 1, wherein said each processor is further configured to determine the local surface shape of the surface of a portion of the body part covered by said one or more sensors using geometric processing.

16. A method for detecting body kinematics comprising:

collecting body state information from a plurality of local surface areas of a body part covered by a garment, from a plurality of sensors distributed throughout the garment covering the body part;

locally and in real time processing the collected body state information of local surface areas to determine a local surface shape of the surface of a portion of the body part covered by respective local surface areas, by a respective one of a plurality of distributed processors;

exchanging local surface shape information with neighboring processors; and

generating one overall model of a surface shape of the entire surface of the body part covered by the garment from the distributed local surface shape information.

17. The method of claim 16, further comprising generating body kinematics information from said overall model.

18. The method of claim 17, further comprising receiving feedback from the plurality of distributed processors or from external devices;

and using said feedback and said body kinematics information to adaptively control an actuator.

19. The method of claim 16, wherein said overall model includes a rate of change of the surface shape over time, and further comprising generating body kinematics information using said rate of change of the surface shape.

20. The method of claim 16, further comprising changing degrees of freedom being measured by the plurality of processors according to the time of day, the temperature, and the health of the subject wearing the garment.