Abstract
Falls are considered one of the most severe health problems, especially among older people with physical disabilities. To ensure the security of the elderly, it is necessary to predict fall before it happens. This paper proposes a computer vision method for fall prediction among physically disabled elderly. Within our approach, we propose a novel implementation of Encoder-Decoder ConvLSTM (Convolutional Long Short-Term Memory). This work includes three parts: Data Acquisition, Data Preprocessing, and Data Analysis. Starting by acquiring skeleton streams using the Kinect camera. Then, applying preprocessing techniques: extracting skeleton features and selecting key frames. Finally, the analysis step consists of predicting the next frames and classifying them. In case of a predicted fall, an alert will be launched. To evaluate our approach, we use the FallFree dataset that covers all fall types and scenarios of people using canes and others without any mobility aid. Our method achieves an accuracy of 99.64%.