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Research Papers

A Mobile Gait Monitoring System for Abnormal Gait Diagnosis and Rehabilitation: A Pilot Study for Parkinson Disease Patients

[+] Author and Article Information
Joonbum Bae

Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720jbbae@me.berkeley.edu

Kyoungchul Kong

Department of Mechanical Engineering, Sogang University, Seoul 121-742, Koreakckong@sogang.ac.kr

Nancy Byl

Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, San Francisco, CA 94122byln@ptrehab.ucsf.edu

Masayoshi Tomizuka

Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720tomizuka@me.berkeley.edu

J Biomech Eng 133(4), 041005 (Mar 08, 2011) (11 pages) doi:10.1115/1.4003525 History: Received June 07, 2010; Revised January 20, 2011; Posted January 28, 2011; Published March 08, 2011; Online March 08, 2011

Conventional gait rehabilitation treatment does not provide quantitative information on abnormal gait kinematics, and the match of the intervention strategy to the underlying clinical presentation may be limited by clinical expertise and experience. Also the effect of rehabilitation treatment may be reduced as the rehabilitation treatment is achieved only in a clinical setting. In this paper, a mobile gait monitoring system (MGMS) is proposed for the diagnosis of abnormal gait and rehabilitation. The proposed MGMS consists of Smart Shoes and a microsignal processor with a touch screen display. It monitors patients’ gait by observing the ground reaction force (GRF) and the center of GRF, and analyzes the gait abnormality. Since visual feedback about patients’ GRFs and normal GRF patterns are provided by the MGMS, patients can practice the rehabilitation treatment by trying to follow the normal GRF patterns without restriction of time and place. The gait abnormality proposed in this paper is defined by the deviation between the patient’s GRFs and normal GRF patterns, which are constructed as GRF bands. The effectiveness of the proposed gait analysis methods with the MGMS has been verified by preliminary trials with patients suffering from gait disorders.

Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Gait phases and corresponding foot pressure patterns in normal gait (23)

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Figure 3

Schematic sketch of air bladder sensor (each variable used in figure represents the following: F is the force exerted by a foot, P is the air pressure in an air bladder, A is the cross-sectional area of the air bladder, c is the conversion constant of an air pressure sensor, and Vo is the voltage output of the air pressure sensor) (23)

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Figure 4

Experimental setup for performance test of the air bladder sensor

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Figure 5

Performance of air bladder sensor

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Figure 6

Concept of a MGMS

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Figure 7

Implementation of a MGMS

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Figure 8

Raw GRF signals in normal gait (bodyweight: 68 kg)

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Figure 9

CoGRF in normal gait

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Figure 10

CoGRF verification with a VICON system

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Figure 11

Normal GRF bands (solid lines: mean, dashed lines: ±2.03 SD)

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Figure 12

An example of GRF deviations from normal GRF bands

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Figure 13

GRF data (patient A)

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Figure 14

GRF with normal GRF bands (patient A)

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Figure 15

CoGRF (patient A)

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Figure 16

GRF data (patient B)

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Figure 17

GRF with normal GRF bands (patient B)

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Figure 18

CoGRF (patient B)

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Figure 19

GRF data (patient C)

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Figure 20

GRF with normal GRF bands (patient C)

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Figure 21

CoGRF (patient C)

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