Open Access

ARTICLE

A New Mixed Clustering-Based Method to Analyze the Gait of Children with Cerebral Palsy

Jing Hu¹, Ling Zhang¹, Jie Li^2,3,*, Qirun Wang⁴

1 Department of Rehabilitation Medicine, Children’s Hospital of Nanjing Medical University, Nanjing, 210008, China
2 Liaoning Children’s Hospital, Shenyang, 110085, China
3 Shenyang Jing’an Mental Health Hospital, Shenyang, 110163, China
4 Department of Physics and Astronomy, University College London, London, 0044, UK

* Corresponding Author: Jie Li. Email:

Computers, Materials & Continua 2021, 66(2), 1551-1562. https://doi.org/10.32604/cmc.2020.011829

Received 31 May 2020; Accepted 15 September 2020; Issue published 26 November 2020

Abstract

Cerebral palsy is a group of persistent central movement and postural developmental disorders, and restricted activity syndromes. This syndrome is caused by non-progressive brain damage to the developing fetus or infants. Cerebral palsy assessment can determine whether the brain is behind or abnormal. If it exists, early intervention and rehabilitation can be carried out as soon as possible to restore brain function to the greatest extent. The direct external manifestation of cerebral palsy is abnormal gait. Accurately determining the muscle strength-related reasons that cause this abnormal gait is the primary problem for treatment. In this paper, clustering methods were used to compare and analyze the differences between the abnormal and normal gait parameters of children with and without cerebral palsy. Since the collected data contains overlapping data that may be mutated, while the centroids are also different, the expected result is stratified. To solve this problem, a mixed clustering method is proposed that combines the advantages of K-means and hierarchical clustering, meaning that each set of data shows a similar trend to specific parameters. Experiment results show that this method can detect cerebral palsy through the difference between the abnormal gait of children with cerebral palsy and that of normal children.

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