<![CDATA[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.]]>
