SMART GAIT REHABILITATION SYSTEM FOR AUTOMATED DIAGNOSIS AND THERAPY OF NEUROLOGIC IMPAIRMENT

• US 20100312152A1
• Filed: 05/28/2010
• Published: 12/09/2010
• Est. Priority Date: 06/03/2009
• Status: Abandoned Application

• Current Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Original Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Inventors: Trejo, Adrian, Sarkodie-Gyan, Thompson
• Application Number:
  US12/790,061
• Publication Number:
  US 20100312152A1
• Time in Patent Office: Days
• US Class Current: 601/34
• CPC Class Codes: A61B 2505/09 Rehabilitation or training
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/6828 Leg
• CPC Class Codes: A61H 1/0237 for the lower limbs
• CPC Class Codes: A61H 1/0255 Both knee and hip of a patient, e.g. in supine or sitting position, the feet being moved in a plane substantially parallel to the body-symmetrical-plane A61H1/0214 takes precedence
• CPC Class Codes: A61H 2201/0192 Specific means for adjusting dimensions
• CPC Class Codes: A61H 2201/1215 Rotary drive
• CPC Class Codes: A61H 2201/123 Linear drive
• CPC Class Codes: A61H 2201/165 Wearable interfaces
• CPC Class Codes: A61H 2201/1671 rotational
• CPC Class Codes: A61H 2201/1676 Pivoting
• CPC Class Codes: A61H 2201/5007 computer controlled
• CPC Class Codes: A61H 2201/5058 Sensors or detectors
• CPC Class Codes: A61H 2201/5061 Force sensors
• CPC Class Codes: A61H 2201/5069 Angle sensors
• CPC Class Codes: A61H 2201/5084 Acceleration sensors
• CPC Class Codes: A61H 2230/60 Muscle strain, i.e. measured on the user, e.g. Electromyography [EMG]
• CPC Class Codes: A61H 3/00 Appliances for aiding patients or disabled persons to walk about apparatus for helping babies to walk A47D13/04; orthopaedic devices for correcting deformities of, or supporting, limbs A61F5/0102; exercising apparatus for the feet or toes A63B23/10; stairways or ramps E04F11/00
• CPC Class Codes: A61H 3/008 Using suspension devices for supporting the body in an upright walking or standing position, e.g. harnesses drawing-out devices A61H1/0218
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising

Abstract

The present invention describes a Smart Gait Rehabilitation System (SGRS). The present invention is capable of performing a quantitative analysis of human movements based on the simultaneous measurement of within-subject stride-to-stride changes in gait using accelerometers, gyroscopes, goniometers, and electromyography (EMG). The system described in the present invention is based on step-training that incorporates sensory feedback, provide feedback about kinematics and torques, and proceeds at walking speeds typical of overground ambulation.

Claims

1. A mechanical multi-axis robotic device (10) for re-educating and/or training one or more lower limbs of a subject having an impairment of the central nervous system, comprising:

at least two powered lower limb structures; and

one or more support structures or plates;

wherein the two powered lower limb structures are secured to the one or more support structures or plated using bolts attached to a linear actuator.

2. The device of claim 1, wherein the powered lower limb structures comprise:

a height adjuster assembly (12);

a hip movement assembly (14);

wherein the height adjuster assembly (12) is attached to the hip movement assembly (14) through a bearing connected to the one or more support structures or plates (20);

a thigh movement assembly (16);

wherein the hip movement assembly (14) is attached to the thigh movement assembly (16) by a bolt protruding through the upper end of the linear actuator and through the hip movement assembly; and

a calf movement assembly (18);

wherein the thigh movement assembly (16) is attached to the calf movement assembly (18) through the bearing (24) connected by support structures or plates (20).

3. The device of claim 2, wherein the one or more holes in the support structures or plates and hip movement assembly are fitted with bearings (24) to allow rotation between hip movement assembly and thigh movement assembly.

4. The device of claim 1, wherein the central nervous system impairment comprises a hemiplegic stroke, a paraparesis from spinal cord injuries, an upper motor neuron syndrome, a serious mobility-related disability or any combinations thereof.

5. The device of claim 1, wherein the device further comprises an imbedded knowledge-based control system, an intelligent sensing and a data acquisition, wherein the control system controls at least one of the height adjuster assembly (12);

the hip movement assembly (14);

the thigh movement assembly (16); and

the calf movement assembly (18).

6. The device of claim 5, wherein the imbedded knowledge-based control system further comprises:

a human locomotor system (30);

one or more measurement systems for measuring stride-to-stride changes in gait; and

a quantitative system for movement analysis based on stride-to-stride changes in gait.

7. The device of claim 6, wherein the one or more measurement systems are selected from a group comprising accelerometers, gyroscopes, goniometers, and electromyography (EMG).

8. The device of claim 5, wherein the intelligent sensing and a data acquisition and control system further comprises:

a database module (40),

a decision/inference module (42),

a knowledge base module (36),

one or more modules for identification of a problem (38) and for connecting to the human locomotor system (30) and control systems (50); and

a bio-cognitive monitor module (46) that provides feedback to a patient.

9. A system for a priori prediction of the outcome of a physical therapy regimen or recovery in a patient following an impairment of the central nervous system, comprising the steps of:

identifying a patient having an impairment of the central nervous system;

attaching a mechanical multi-axis robotic device to the patient (10);

wherein the multi-axis robotic device comprises two or more powered lower limb structures connected via one or more support structures or plates, an imbedded knowledge-based control system and an intelligent sensing and a data acquisition and control system connected to one or more sensors;

measuring within-subject stride-to-stride changes using the one or more sensors;

analyzing the movements quantitatively based on the measurements of the within-subject stride-to-stride changes; and

predicting the outcome of a physical therapy regimen or recovery in the patient based on the quantitative results of the measurements of the within-subject stride-to-stride changes.

10. The system of claim 9, wherein the central nervous system impairment comprises hemiplegic stroke, paraparesis from spinal cord injuries, and other upper motor neuron syndromes, serious mobility-related disabilities or any combinations thereof.

11. The system of claim 9, wherein the one or more measurement systems are selected from a group comprising accelerometers, gyroscopes, goniometers, and electromyography (EMG).

12. The system of claim 9, wherein the powered lower limb structures comprise:

a height adjuster assembly (12);

a hip movement assembly (14);

wherein the height adjuster assembly (12) is attached to the hip movement assembly (14) through a bearing connected to the one or more support structures or plates (20);

a thigh movement assembly (16);

wherein the hip movement assembly (14) is attached to the thigh movement assembly (16) by a bolt protruding through the upper end of the linear actuator and through the hip movement assembly; and

a calf movement assembly (18);

wherein the thigh movement assembly (16) is attached to the calf movement assembly (18) through the bearing (24) connected by support structures or plates (20).

13. The system of claim 12, wherein the one or more holes in the support structures or plates and hip movement assembly are fitted with bearings (24) to allow rotation between hip movement assembly and thigh movement assembly.

14. A method for designing a passive gait or locomotor training regimen, or diagnosing gait for a subject the method comprising the steps of:

attaching a mechanical multi-axis robotic device (10) to the subject;

wherein the multi-axis robotic device comprises two powered lower limb structures, one or more support structures or plates, an imbedded knowledge-based control system, an intelligent sensing and a data acquisition and control system;

measuring within-subject stride-to-stride changes using one or more measurement systems;

analyzing the movements quantitatively based on the measurements of the within-subject stride-to-stride changes; and

diagnosing gait or designing a gait or locomotor training regimen based on the quantitative results of the measurements of the within-subject stride-to-stride changes.

15. The method of claim 14, wherein the one or more measurement systems are selected from a group comprising accelerometers, gyroscopes, goniometers, and electromyography (EMG).

16. The method of claim 14, wherein the central nervous system impairment comprises hemiplegic stroke, paraparesis from spinal cord injuries, and other upper motor neuron syndromes, serious mobility-related disabilities or any combinations thereof.

17. The method of claim 14, wherein the one or more measurement systems are selected from a group comprising accelerometers, gyroscopes, goniometers, and electromyography (EMG).

18. The method of claim 14, wherein the powered lower limb structures comprise:

a height adjuster assembly (12);

a hip movement assembly (14);

wherein the height adjuster assembly (12) is attached to the hip movement assembly (14) through a bearing connected to the one or more support structures or plates (20);

a thigh movement assembly (16);

wherein the hip movement assembly (14) is attached to the thigh movement assembly (16) by a bolt protruding through the upper end of the linear actuator and through the hip movement assembly; and

a calf movement assembly (18);

wherein the thigh movement assembly (16) is attached to the calf movement assembly (18) through the bearing (24) connected by support structures or plates (20).

19. The method of claim 14, wherein the one or more holes in the support structures or plates and hip movement assembly are fitted with bearings (24) to allow rotation between hip movement assembly and thigh movement assembly.

20. The method of claim 14, wherein the imbedded knowledge-based control system further comprises:

a human locomotor system (30);

one or more measurement systems for measuring stride-to-stride changes in gait; and

a quantitative system for movement analysis based on stride-to-stride changes in gait.

21. The method of claim 20, wherein the one or more measurement systems are selected from a group comprising accelerometers, gyroscopes, goniometers, and electromyography (EMG).

22. The method of claim 14, wherein the intelligent sensing and a data acquisition and control system further comprises:

a database module (40),

a decision/inference module (42),

a knowledge base module (36),

one or more modules for identification of a problem (38) and for connecting to the human locomotor system (30) and control systems (50); and

a bio-cognitive monitor module (46) that provides feedback to a patient.

23. The method of claim 14, wherein the device is further defined as comprising one or more sensors are attached to each of the height adjuster assembly (12);

the hip movement assembly (14);

the thigh movement assembly (16);

the calf movement assembly (18) or combinations thereof.

24. The method of claim 23, wherein the imbedded knowledge-based control system receives input from the one or more sensors that comprises:

a localization module that establishes which sensor has failed);

an identification module that determines the type of failure); and

an estimation modules that calculates the effect and extent of the failure.

25. The method of claim 23, wherein the imbedded knowledge-based control system receives input from the one or more sensors and data from each of the sensors is integrated by a fuzzy rule-based algorithm.

26. The method of claim 23, wherein the imbedded knowledge-based control system integrates input from the one or more sensors;

organizes the distributed sensing systems;

integrates the sensors'"'"' diverse observations (inputs and outputs);

coordinates and guides the decisions made by each sensor; and

controls devices with the goal of improving sensor system performance.

27. The method of claim 23, wherein the method allows for training a subject in a passive, an active mode, or both depending on the therapeutic needs of the subject.