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Sensors 2017, 17(5), 1002;

An Acceleration-Based Gait Assessment Method for Children with Cerebral Palsy

Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230026, China
Department of Pediatrics, The First Affiliated Hospital of Anhui Medical University, Hefei 230026, China
Author to whom correspondence should be addressed.
Academic Editor: Thurmon Lockhart
Received: 13 February 2017 / Revised: 25 April 2017 / Accepted: 27 April 2017 / Published: 2 May 2017
(This article belongs to the Section Physical Sensors)
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With the aim of providing an objective tool for motion disability assessment in clinical diagnosis and rehabilitation therapy of cerebral palsy (CP) patients, an acceleration-based gait assessment method was proposed in this paper. To capture gait information, three inertial measurement units (IMUs) were placed on the lower trunk and thigh, respectively. By comparing differences in the gait acceleration modes between children with CP and healthy subjects, an assessment method based on grey relational analysis and five gait parameters, including Pearson coefficient, variance ratio, the number of extreme points, harmonic ratio and symmetry was established. Twenty-two children with cerebral palsy (7.49 ± 2.86 years old), fourteen healthy adults (24.2 ± 1.55 years old) and ten healthy children (7.03 ± 1.49 years old) participated in the gait data acquisition experiment. The results demonstrated that, compared to healthy subjects, the symptoms and severity of motor dysfunction of CP children could result in abnormality of the gait acceleration modes, and the proposed assessment method was able to effectively evaluate the degree gait abnormality in CP children. View Full-Text
Keywords: acceleration; cerebral palsy (CP); gait assessment; grey relational analysis acceleration; cerebral palsy (CP); gait assessment; grey relational analysis

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Chen, X.; Liao, S.; Cao, S.; Wu, D.; Zhang, X. An Acceleration-Based Gait Assessment Method for Children with Cerebral Palsy. Sensors 2017, 17, 1002.

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