Development of Stride Detection System for Helping Stroke Walking Training

Ilham Ari Elbaith Zaeni; Dyah Lestari; Anik N. Handayani; Muhammad Khusairi Osman

doi:10.35882/jeemi.v5i3.306

Ilham Ari Elbaith Zaeni Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Indonesia https://orcid.org/0000-0001-9665-8613
Dyah Lestari Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Indonesia https://orcid.org/0000-0002-8488-5146
Anik N. Handayani Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Indonesia https://orcid.org/0009-0003-0767-8471
Muhammad Khusairi Osman Center for Electrical Eng. Studies, Universiti Teknologi MARA, Malaysia

DOI: https://doi.org/10.35882/jeemi.v5i3.306

Keywords: Stride Detection System, Walking Training, Stride Length, Circumduction, Decision Tree, Mobile Application

Abstract

Walking is a popular post-stroke rehabilitation exercise for patients. Stroke walking training is a sort of physical therapy that aims to help people who have had a stroke improve their walking ability. The goal of this research is to classify stride length and include it into a mobile application. The accelerometer sensor on a smartphone can be used to construct a stride detection system to aid in stroke walking training. This application was created for Android-powered smartphones. A binder must be used to secure the smartphone device to the patient's thigh. This application reads the accelerometer sensor included into the smartphone. In this study, a stride detection model is designed to increase the performance of stride length and circumduction detection. The accelerometer is read and saved by the application as the participant walks on the specific path. After the signal has been pre-processed and its feature extracted, the data is used to create the stride detection model. The performance is good, as evidenced by accuracy, precision, recall, and f-measure values of 88.60%, 88.60%, 88.60%, and 88.60%, respectively. When utilized on a stride detection system, the decision tree algorithms function admirably. The model is then loaded into the Android walking app.

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