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Sensors 2011, 11(8), 7314-7326; doi:10.3390/s110807314

Real-Time Gait Cycle Parameter Recognition Using a Wearable Accelerometry System

1
Department of Mechanical Engineering, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li, Tao-Yuan 320, Taiwan
2
Division of Orthopedics, Shin Kong Wu Ho-Su Memorial Hospital, 95 Wen Chang Rd., Shih Lin District, Taipei 111, Taiwan
3
Gerontechnology Research Center, Yuan Ze University, 135 Yuan-Tung Rd., Chung-Li 320, Taiwan
*
Author to whom correspondence should be addressed.
Received: 13 June 2011 / Revised: 13 July 2011 / Accepted: 18 July 2011 / Published: 25 July 2011
(This article belongs to the Section Physical Sensors)
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Abstract

This paper presents the development of a wearable accelerometry system for real-time gait cycle parameter recognition. Using a tri-axial accelerometer, the wearable motion detector is a single waist-mounted device to measure trunk accelerations during walking. Several gait cycle parameters, including cadence, step regularity, stride regularity and step symmetry can be estimated in real-time by using autocorrelation procedure. For validation purposes, five Parkinson’s disease (PD) patients and five young healthy adults were recruited in an experiment. The gait cycle parameters among the two subject groups of different mobility can be quantified and distinguished by the system. Practical considerations and limitations for implementing the autocorrelation procedure in such a real-time system are also discussed. This study can be extended to the future attempts in real-time detection of disabling gaits, such as festinating or freezing of gait in PD patients. Ambulatory rehabilitation, gait assessment and personal telecare for people with gait disorders are also possible applications. View Full-Text
Keywords: accelerometry; accelerometer; Parkinson’s disease; gait; mobility accelerometry; accelerometer; Parkinson’s disease; gait; mobility
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Yang, C.-C.; Hsu, Y.-L.; Shih, K.-S.; Lu, J.-M. Real-Time Gait Cycle Parameter Recognition Using a Wearable Accelerometry System. Sensors 2011, 11, 7314-7326.

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