Fall Detection Using LSTM and Transfer Learning

Prior detection for high risk of falls in elderly people is an essential and challenging task. Wearable sensors have already proven as beneficial resource in monitoring daily living activities. Sensors worn on body such as gyroscope, accelerometer can provide a valuable input into detection of fall. In our research, we have implemented the deep learning methods, and analyzed that they are suitable for extracted features from sensors data i.e. accelerometer, gyroscope that evaluate fall risks. We used a publicly available dataset that is based on different daily living activities of elderly people. Furthermore, to conduct the comparative analysis, the performance of two deep learning architectures, the Long short-term memory (LSTM) and CNN based Transfer learning is considered. We also observed that CNN-transfer learning resulted in optimal performance quantitatively bearing 98% accuracy, we summarized that deep learning architectures are very effective in multi-task learning and are capable to effectively predict the high risk of human falls in the terms of wearable sensors.