Vision Based Human Activity Recognition and Monitoring System for Guided Virtual Rehabilitation

• US 20110054870A1
• Filed: 09/01/2010
• Published: 03/03/2011
• Est. Priority Date: 09/02/2009
• Status: Abandoned Application

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Sakagami, Yoshiaki, Fujimura, Kikuo, Dariush, Behzad
• Application Number:
  US12/873,498
• Publication Number:
  US 20110054870A1
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: G06F 3/011 Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A system, method, and computer program product for providing a user with a virtual environment in which the user can perform guided activities and receive feedback are described. The user is provided with guidance to perform certain movements. The user'"'"'s movements are captured in an image stream. The image stream is analyzed to estimate the user'"'"'s movements, which is tracked by a user-specific human model. Biomechanical quantities such as center of pressure and muscle forces are calculated based on the tracked movements. Feedback such as the biomechanical quantities and differences between the guided movements and the captured actual movements are provided to the user.

Claims

1. A computer based method for providing a human user with a guided movement and feedback, the method comprising:

providing to the user an instruction to perform the guided movement;

capturing a movement performed by the user in response to the instruction;

estimating a movement of the user in a human model based on the captured movement performed by the user;

determining a biomechanical quantity of the user by analyzing the estimated movement in the human model; and

providing feedback to the user about the captured movement performed by the user based on the biomechanical quantity.

2. The method of claim 1, wherein capturing the movement comprises capturing the movement in a depth image stream using a depth camera, and wherein estimating the movement of the user comprises:

detecting features in the depth image stream and representing the detected features by position vectors;

filtering the position vectors to generate interpolated position vectors;

augmenting the interpolated position vectors with positions of features missing in the depth image stream; and

generating an estimated movement of the user based on the augmented position vectors.

3. The method of claim 2, wherein the features are detected by comparing Inner Distance Shape Context (IDSC) descriptors of sample contour points with IDSC descriptors of known feature points for similarity.

4. The method of claim 3, wherein the feature point comprises one of:

head top, left shoulder, right shoulder, left elbow, right elbow, left wrist, right wrist, left waist, right waist, groin, left knee, right knee, left ankle, and right ankle

5. The method of claim 1, wherein the human model is a human anatomical model that closely resembles the body of the user.

6. The method of claim 5, wherein the human model is configured based on a plurality of appropriate kinematic model parameters and appropriate dynamic model parameters of a plurality of body parts of the user.

7. The method of claim 6, wherein one or more of the plurality of appropriate kinematic model parameters are obtained from images of the user.

8. The method of claim 1, wherein the biomechanical quantity comprises a center of pressure (COP) and the COP is determined using a Recursive Newton-Euler Algorithm (RNEA).

9. The method of claim 1, wherein the biomechanical quantity comprises a muscle force, and the muscle force is determined by modeling muscle and tendon mechanics as active force-generating elements in series and parallel with elastic elements.

10. The method of claim 9, wherein the muscle force is determined using a generic musculo-tendon model that is scaled to individual muscles using the following muscle specific parameters:

a maximum isometric force capacity of muscle, an optimal muscle fiber length, a muscle fiber pennation angle at optimal fiber length, and a tendon slack length.

11. The method of claim 9, wherein determining the muscle force comprises iteratively updating the fiber length and recomputing a percentage force error until the percentage force error is less than a predetermined value.

12. The method of claim 1, wherein providing the feedback comprises:

displaying a human model tracking the estimated movement of the user along with the guided movement.

13. The method of claim 11, wherein providing the feedback further comprises:

amplifying the differences between the estimated movement and the guided movement.

14. The method of claim 1, further comprising:

transmitting the biomechanical quantity to a human expert, wherein the feedback comprises feedback provided by the human expert in response to the biomechanical quantity.

15. The method of claim 1, wherein the instruction to perform the guided movement comprises one of a voice command and a motion command graphically displayed to the user by means of the human model.

16. The method of claim 1, wherein the feedback comprises a physical robot that replicates the subject'"'"'s movements.

17. The method of claim 16, wherein the physical robot is further configured to provide at least one of the following:

physical interaction, physical assistance, and resistive training

18. The method of claim 1, wherein the guided movement comprises one of the following:

mirror therapy, balance &

stability based on regulation of the center of pressure (COP) and the center of gravity (COG), balance &

stability based on pose regulation, motion sequence recall, voice and posture, posture and hand shape, and listening to words and gesture.

19. A computer program product for providing a human user with a guided movement and feedback, the computer program product comprising a computer-readable storage medium containing executable computer program code for performing a method comprising:

providing to the user an instruction to perform the guided movement;

capturing a movement performed by the user in response to the instruction;

estimating a movement of the user in a human model based on the captured movement performed by the user;

determining a biomechanical quantity of the user by analyzing the estimated movement in the human model; and

providing feedback to the user about the captured movement performed by the user based on the biomechanical quantity.

20. A system for providing a human user with a guided movement and feedback, the system comprising:

a computer processor for executing executable computer program code;

a computer-readable storage medium containing the executable computer program code for performing a method comprising;

providing to the user an instruction to perform the guided movement;

capturing a movement performed by the user in response to the instruction;

estimating a movement of the user in a human model based on the captured movement performed by the user;

determining a biomechanical quantity of the user by analyzing the estimated movement in the human model; and

providing feedback to the user about the captured movement performed by the user based on the biomechanical quantity.