关键词: 长短时记忆网络, 人体姿态, 多传感器, 时序数据, 深度学习

Abstract: Concerning the problem that distant historical signals cannot be transmitted to the current time under the network structure of Recurrent Neural Network (RNN), Long Short Term Memory (LSTM) network was proposed as a variant of RNN. On the premise of inheriting RNN's excellent memory ability for time series, LSTM overcomes the long-term dependence problem of time series and has a remarkable performance in natural language processing and speech recognition. For the long-term dependence problem of human behavior data as a time series and the problem of not real-time detection caused by using the traditional sliding window algorithm to collect data, the LSTM was extended and applied to the human posture detection, and then a human posture detection method based on LSTM was proposed. By using the real-time data collected by the accelerometers, gyroscopes, barometers and direction sensors in the smartphones, a human posture dataset with a total of 3336 manual annotation data was produced. The five kinds of daily behavior postures such as walking, running, going upstairs, going downstairs, calmness as well as the four kinds of sudden behavior postures of fallling, standing, sitting, jumping, were predicted and classified. The LSTM network was compared with the commonly used methods such as shallow learning algorithm, deep learning fully connected neural network and convolution neural network. The experimental results show that, by using the end-to-end deep learning method, the proposed method has improved the accuracy by 4.49 percentage points compared to the model of human posture detection algorithm trained on the produced dataset. The generalization ability of the proposed network structure is verified and it is more suitable for posture detection.

Key words: Long Short Term Memory (LSTM) network, human posture, multi-sensor, time series data, deep learning