Abstract: Aiming at the injury caused by the elderly who cannot receive timely assistance after falling, the study of fall detection algorithms and timely warnings can reduce the serious harm and consequences of falling of the elderly. In order to improve the accuracy and real-time performance of fall detection, a wearable fall detection algorithm based on bi-directional long and short-term memory neural network is proposed. This algorithm can automatically extract the deeper features from the input fall data (extracted from inertial sensors), and realize the processing from the pre-processed data to the detection result. The algorithm extracts the feature vectors of the acceleration sensor data through the neural network, and performs fall detection using bi-directional long and short-term memory neural network. The model is evaluated with SisFall dataset. The results show that the algorithm achieves high accuracy, while the latency also meets the requirements of real-time detection. The algorithm model has both good practicability and strong generalization ability.

Key words: fall detection, long short-term memory, accelerometer, neural network, feature extraction