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Research Papers

A Wireless Human Motion Monitoring System for Smart Rehabilitation

[+] Author and Article Information
Wenlong Zhang

The Polytechnic School,
Ira A. Fulton Schools of Engineering,
Arizona State University,
Mesa, AZ 85212
e-mail: Wenlong.Zhang@asu.edu

Masayoshi Tomizuka

Department of Mechanical Engineering,
College of Engineering,
University of California, Berkeley,
Berkeley, CA 94720
e-mail: tomizuka@me.berkeley.edu

Nancy Byl

Department of Physical Therapy and
Rehabilitation Science,
School of Medicine,
University of California, San Francisco,
San Francisco, CA 94158
e-mail: BylN@ptrehab.ucsf.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 29, 2015; final manuscript received June 7, 2016; published online July 13, 2016. Assoc. Editor: Xiaopeng Zhao.

J. Dyn. Sys., Meas., Control 138(11), 111004 (Jul 13, 2016) (9 pages) Paper No: DS-15-1467; doi: 10.1115/1.4033949 History: Received September 29, 2015; Revised June 07, 2016

In this paper, a wireless human motion monitoring system is presented for gait analysis and visual feedback in rehabilitation training. The system consists of several inertial sensors and a pair of smart shoes with pressure sensors. The inertial sensors can capture lower-extremity joint rotations in three dimensions and the smart shoes can measure the force distributions on the two feet during walking. Based on the raw measurement data, gait phases, step lengths, and center of pressure (CoP) are calculated to evaluate the abnormal walking behaviors. User interfaces are developed on both laptops and mobile devices to provide visual feedback to patients and physical therapists. The system has been tested on healthy subjects and then applied in a clinical study with 24 patients. It has been verified that the patients are able to understand the intuitive visual feedback from the system, and similar training performance has been achieved compared to the traditional gait training with physical therapists. The experimental results with one healthy subject, one stroke patient, and one Parkinson's disease patient are compared to demonstrate the performance of the system.

Copyright © 2016 by ASME
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References

Figures

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Fig. 1

Traditional rehabilitation

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Fig. 2

A block diagram of the rehabilitation training with visual feedback

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Fig. 3

Structure of the wireless human motion monitoring system

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Fig. 6

Geometry for step length calculation

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Fig. 7

Visual feedback on a laptop

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Fig. 4

Wireless smart shoes for gait detection: (a) sole of the smart shoes with four air bladders and (b) wireless smart shoes

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Fig. 5

Foot placement on the ground and corresponding joint rotation

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Fig. 8

User interface of the iPad application for visual feedback

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Fig. 9

Experiment of the wireless human motion monitoring system with a healthy subject

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Fig. 18

Statistics of the joint rotations from a Parkinson's disease patient: (a) left hip joint angles in the sagittal plane and (b) left knee joint angles in the sagittal plane

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Fig. 10

Setup of the markers and IMU sensors

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Fig. 13

CoP from two subjects during walking: (a) left side from the healthy subject and (b) right side from the stroke patient

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Fig. 14

Statistics of the joint rotations from a healthy subject: (a) left hip joint angles in the sagittal plane and (b) left knee joint angles in the sagittal plane

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Fig. 15

GCF signals and gait phases of a stroke patient: (a) GCF signals from right shoe and (b) detected gait phases from right shoe

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Fig. 16

Statistics of the joint rotations from a stroke patient: (a) right hip joint angles in the sagittal plane and (b) right knee joint angles in the sagittal plane

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Fig. 17

GCF signals and gait phases of a Parkinson's disease patient: (a) GCF signals from left shoe and (b) detected gait phases from left shoe

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Fig. 11

Joint angles of squat in the sagittal plane from cameras and IMU sensors

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Fig. 12

GCF signals and gait phases of a healthy subject: (a) GCF signals from left shoe and (b) detected gait phases from left shoe

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