关键词: 手机传感器分析, 人体动作识别, Inception, 卷积神经网络（CNN）, 深度可分离卷积, 长短期记忆模型（LSTM）, 深度学习, OI-LSTM

Abstract:

With the widespread use of mobile phone sensors, there are more and more demands for daily human behavioral action recognition. Classical recognition methods use heuristic processes to obtain artificial features, and then use machine learning methods to identify actions. The latest research shows that the Inception convolution structure is prominent, which can avoid the deviation problem caused by the manual extraction of features. Human action is composed of complex motion sequences. It is essential for action recognition to catch this time series.Based on this, this paper first optimizes the Inception structure, proposes the O-Inception structure, and fuses it with the long short term memory model (LSTM), last proposes the optimization Inception-LSTM (OI-LSTM) action recognition model. On the one hand, OI-LSTM model can use O-Inception structure to extract features automatically. On the other hand, it can also use LSTM to capture the action sequence, and then improve the accuracy of action recognition. Experiments are conducted on the wireless sensor data mining (WISDM) and UC Irvine (UCI) datasets. The experimental results show that the proposed OI-LSTM action recognition model has an accuracy improvement of about 4% on the WISDM dataset and 1% on the UCI compared to the current state-of-the-art method. Experi-ments also show that the model has strong fault tolerance and real-time performance.

Key words: mobile phone sensor analysis, human activity recognition, Inception, convolutional neural network (CNN), depth-wise separable convolution, long short term memory (LSTM), deep learning, optimization Inception-LSTM (OI-LSTM)