REALTIME BIOFEEDBACK MECHANISM AND DATA PRESENTATION FOR KNEE INJURY REHABILITATION MONITORING AND A SOFT REAL TIME INTELLIGENT SYSTEM THEREOF

• US 20170027501A1
• Filed: 03/02/2015
• Published: 02/02/2017
• Est. Priority Date: 04/03/2014
• Status: Active Grant

• Current Assignee: Universiti Brunei Darussalam
• Original Assignee: Universiti Brunei Darussalam
• Inventors: SENANAYAKE, S. M. N Arosha, MALIK, Owais Ahmed
• Granted Patent: US 10,004,455 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2505/09 Rehabilitation or training
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• 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 5/1122 of movement trajectories
• 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 5/1124 Determining motor skills
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4519 Muscles A61B5/389, A61B5/224 take precedence
• CPC Class Codes: A61B 5/4533 Ligaments
• CPC Class Codes: A61B 5/4585 Evaluating the knee
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00

Abstract

A real time visualization method and system for evaluating and monitoring recovery progress of Anterior Cruciate Ligament reconstructed/injured subject is provided. The method includes receiving multiple super-imposed bio-signals from an eletromyogram (EMG) for muscles of limbs of the patient and signals pertaining to three dimensional kinematics measurement from a motion sensor set, processing the signals to extract wavelet features and evaluating the wavelet features to determine the extent of rehabilitation and a recovery classification of the patient'"'"'s limb from ACL injury. The system includes an input interface circuit, a processing module, a comparator module and an output interface circuit for providing an estimate of the extent of rehabilitation the patient'"'"'s limb from ACL injury.

Claims

1. A method implemented by a real time visualization system for determining extent of rehabilitation and recovery classification of a knee joint of a patient'"'"'s limb from Anterior Cruciate Ligament (ACL) injury, the method comprising the steps of:

receiving data pertaining to an activity from an activity set, the activity being performed by a patient, the data comprising;

multiple super-imposed bio-signals from electromyography (EMG) sensors placed on one or more muscles of one or both limbs of the patient, the one or more muscles including vastusmedialis, vastuslateralis, semitendinosus, biceps femoris and gastrocnemius; and

motion-signals pertaining to three dimensional kinematics measurements obtained from a motion sensor set;

processing the bio-signals from the EMG sensors and the motion-signals pertaining to three dimensional kinematics measurement to extract wavelet features; and

evaluating the extracted wavelet features to determine an initial estimate of the extent of rehabilitation and the recovery classification of the patient'"'"'s limb from ACL injury.

2. The method of claim 1, wherein the motion-signal pertaining to the kinematics measurement comprises signals pertaining to three dimensional angular rates and three dimensional linear acceleration obtained during the activity.

3. The method of claim 1, wherein the three dimensional kinematics measurement comprises three dimensional joint orientation measurements.

4. The method of claim 3, wherein the three dimensional joint orientation measurements include one or more of:

a. joint flexion or joint extension;

b. joint abduction or joint adduction;

c. joint rotation; and

d. actuation timings of different muscles for the activity.

5. The method of claim 1, wherein the bio-signals are full wave rectified and low pass filtered to generate envelope data comprising wavelet features.

6. The method of claim 1, further comprising obtaining, by the real time visualization system, linear acceleration signals and calculating gait cycles from the linear acceleration signals.

7. The method of claim 1, wherein the step of evaluating comprises comparing the extracted wavelet features with data or signals stored in a database, wherein the data or signal stored in the database corresponds to:

a. data or signal obtained from the activity being performed by one or more healthy subjects;

b. data or signal for the activity obtained for a rehabilitating joint of at least one other patient;

c. data or signal for the activity obtained for an intact joint of at least one other patient;

d. data or signal for the activity obtained for a rehabilitating joint of the same patient, taken in the past;

e. data or signal for the activity obtained for an intact joint of the same patient taken in the past; and

f. data or signal for the activity obtained for an intact joint of the same patient taken during the current activity.

8. The method of claim 7, wherein the step of comparing comprises a step of generating a superimposed bio signal, if the extracted wavelet features are compared with one or more of:

a. data or signal for the activity obtained for a rehabilitating joint of the same patient, taken in the past;

b. data or signal for the activity obtained for an intact joint of the same patient taken in the past; and

c. data or signal for the activity obtained for an intact joint of the same patient taken during the current activity.

9. The method of claim 7, wherein the step of comparing comprises comparing the extracted wavelet features with clusters of data or signals stored in a database, the clusters having been formed using a fuzzy clustering method and the comparison with the clusters of data or signals providing an initial estimate of the extent of rehabilitation and a recovery classification of the patient'"'"'s limb from ACL injury.

10. The method of claim 9, wherein the step of comparing implements an adaptive neuro-fuzzy interface system for comparing the extracted wavelet features with clusters of data or signals stored in a database, to arrive at the initial estimate of the extent of rehabilitation and the recovery classification of the patient'"'"'s limb from ACL injury.

11. The method of claim 1, further comprising obtaining, by the real time visualization system, video signals from a video camera set, including front view video signals and side view video signals of the joint of the patient'"'"'s limb.

12. The method of claim 1, further comprising depicting, by the real time visualization system, the initial estimate of the extent of rehabilitation of the joint of the patient'"'"'s limb from ACL injury along with the video signals to enable acceptance or modification of the initial estimate.

13. The method of claim 1, further comprising depicting, by the real time visualization system, the initial estimate of the recovery classification of the knee joint of the patient'"'"'s limb from ACL injury along with the superimposed bio signal to enable acceptance or modification of the initial estimate.

14. The method of claim 1, further comprises repeating the steps for a plurality of patient activities.

15. A real time visualization system for determining a recovery progress and class of recovery of a knee joint of a patient'"'"'s limb from Anterior Cruciate Ligament (ACL) injury, the system comprising:

an input interface circuit, for receiving data pertaining to activity set, the activity being performed by patient, the data comprising;

bio-signals from an electromyography(EMG) sensors placed on one or more muscles of one or both limbs of the patient, the one or more muscles including vastusmedialis, vastuslateralis, semitendinosus, biceps femoris and gastrocnemius; and

motion-signals pertaining to three dimensional kinematics measurements as obtained from a motion sensor set;

a processing module forming part of a processor and being in operational interconnection with the input interface circuit, the processing module being configured to process the bio-signals and the motion-signals to extract wavelet features;

a comparator module forming part of the processor for comparing the extracted wavelet features with data or signals stored in a database to determine an initial estimate of the recovery progress and the class of recovery of the knee joint of the patient'"'"'s limb from ACL injury; and

an output interface circuit being in operational interconnection with the processor for providing as an output the initial estimate of the recovery progress and the class of recovery of the knee joint of the patient'"'"'s limb from ACL injury.

16. The system of claim 15, wherein the input interface circuit further receives video signals from a video camera set, including front view video signals and side view video signals and provides the video signals to the processing module of the processor.

17. The system of claim 16, wherein the output interface circuit depicts the initial estimate of the recovery progress and the class of recovery of the knee joint of the patient'"'"'s limb from ACL injury along with the video signals.