Forecasting Sit-to-Stand Transitions in Wheelchair Patients: a Textile Pressure Sensor-based Approach

Abstract

This paper introduces the identification between safe standup and risky standup activity using a sit-to-stand transition prediction system from 2D pressure sensor data to mitigate the occurrence of unexpected falls. For wheelchair users, the sit-to-stand transition is a vital daily activity requiring considerable physical effort and balance control. Elderly people, especially those with dementia, may experience significant adverse effects if they cannot perform sit-to-stand correctly, which can result in falls and serious injuries. In this regard, an e-textile pressure sensor-based wheelchair opens up possibilities to reduce unexpected falls by tracking behavioral activities, such as sit-to-stand transition. In the laboratory environment, we collect 20 subjects' pressure sensor data from these modified wheelchairs to forecast sit-to-stand activity (e.g.,trying to standup and assistive standup) and other daily activities (e.g., sitting, exercising, and eating). For predicting these activities, we investigated various machine learning techniques, such as ResNet-50, Long short-term memory (LSTM), XGBoost (XGB),Random Forest (RnF), K-Nearest Neighbor (KNN), Support vector machine (SVM). In this study, we also evaluated the performance of various statistical feature sets for 2D pressure sensor data. Overall, the proposed system can potentially improve the safety and quality of life of wheelchair patients by preventing falls and reducing the risk of serious injuries.