Research Papers

A Practical Measurement of Parkinson's Patients Gait Using Simple Walker-Based Motion Sensing and Data Analysis

[+] Author and Article Information
Vered Aharonson

Electrical and Information
Engineering Department,
University of the Witwatersrand,
Johannesburg 2000, South Africa
e-mail: vered.aharonson@wits.ac.za

Ilana Schlesinger

Department of Neurology,
Rambam Healthcare Campus,
Haifa 31096, Israel
e-mail: i_schles@rambam.health.gov.il

Andre M. McDonald

Modelling and Digital Science Unit,
Council for Scientific and Industrial Research,
Pretoria 0001, South Africa
e-mail: amcdonald@csir.co.za

Steven Dubowsky

Mechanical Engineering Department,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: dubowsky@mit.edu

Amos D. Korczyn

Department of Neurology,
Tel-Aviv University Medical School,
Tel-Aviv 69978, Israel
e-mail: amoskor@post.tau.ac.il

1Corresponding author.

Manuscript received March 15, 2017; final manuscript received December 3, 2017; published online February 12, 2018. Assoc. Editor: Venketesh Dubey.

J. Med. Devices 12(1), 011012 (Feb 12, 2018) (8 pages) Paper No: MED-17-1053; doi: 10.1115/1.4038810 History: Received March 15, 2017; Revised December 03, 2017

We present personal aid for mobility and monitoring (PAMM II), an instrumented walker for Parkinson's disease (PD) patients' gait monitoring. The objective of the walker is to aid in the diagnosis and monitoring of PD progression as well as the effects of clinical treatment and rehabilitation. In contrast to existing devices, the walker is a low-cost solution that is simple to operate and maintain, requiring no adjustments, special usage instructions, or infrastructure. This preliminary study reports on the efficiency, reliability, and accuracy of PAMM II when used to evaluate 22 PD patients and 20 control individuals. All subjects walked two prescribed paths while pushing the walker, and their kinematic motion signals were automatically collected by the walker. Feature derivation from the walker's signals was followed by combinations of two classical feature selection methods and two learning algorithms, with the objective of discriminating PD patients from control subjects. Sensitivity and specificity scores of 91% and 95% were achieved for the first walking protocol, whereas discrimination over the second walking protocol produced sensitivity and specificity scores of 96% and 100%. These preliminary results provide insight as to the usefulness of PAMM II and its data processing algorithms for the assessment of PD patients' condition.

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Fig. 1

PAMM II schematic with major components installed on a conventional walker frame

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Fig. 2

Photograph of the PAMM II system

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Fig. 3

Front wheel encoder assembly

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Fig. 4

Block diagram illustrating the processing of accelerometer, force sensor and motion encoder sensor samples

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Fig. 6

Antero-posterior acceleration signal from the accelerometer, as recorded during straight-line walking by a control subject (10 m path). Diamonds indicate approximate instances of initial foot contact with the floor surface.

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Fig. 5

Velocity signals derived from the motion encoders coupled to the walker's wheels, as recorded during straight-line movement by a control subject (left) and patient (right) over the 10 m path

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Fig. 7

Classification performance attained using the feature selection/extraction and machine learning algorithms



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