Journal
TECHNOLOGY AND HEALTH CARE
Volume 26, Issue 4, Pages 715-721Publisher
IOS PRESS
DOI: 10.3233/THC-181338
Keywords
Gait analysis; prosthetic alignment; ground reaction forces; machine learning; lower limb amputee
Ask authors/readers for more resources
BACKGROUND: Prosthetists conventionally evaluate alignment based on visual interpretation of patients' gait, which is convenient, but largely subjective and depends on prosthetists' experience. OBJECTIVE: In this paper, we explore the feasibility of using a support vector machine (SVM) approach to automatically detect misalignment of trans-tibial prostheses through ground reaction force (GRF). METHODS: Alternate classification algorithms with varying kernels and feature sets were compared to assess the suitability for detection of a representative misalignment (six degrees of ankle plantar flexion) from normal alignment. A classical feature selection algorithm, Fisher Score, was further introduced to identify valuable features and reduce the dimension of feature sets. RESULTS: The SVMs achieved a detection accuracy of 96.67% at best within the same subject and 88.89%, respectively, for inter-subject. Combined horizontal and vertical components of GRF features provided the maximum detection accuracies. Propulsion peak force was identified as key variable of gait for misalignment prediction. CONCLUSIONS: As a proof of concept, the results demonstrate potential in applying this approach to detect prosthetic misalignment based on gait patterns, and is a step towards future developments of tools for early prevention of misalignment in clinical.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available