中国科学院自动化研究所机构知识库

With advances in spatial-temporal Internet of Things (IoT) technologies, human activity recognition (HAR) has played a major role in human safety and medical health. Recently, most works focus on activity deep feature extraction, offering promising alternatives to manually engineered features. However, how to extract the effective and distinguishable continuity activity features and meanwhile reduce the heavy dependence on labels still remains the key challenge for HAR. This article proposes the human continuity activity semisupervised recognizing method in multiview IoT network scenarios. Our innovation combines supervised activity feature extraction with unsupervised encoder-decoder modules, which can capture continuity activity features from sensor data streams. To be more specific, our work applies a convolutional neural network (CNN) to capture the local dependence of sensor data and designs an encoder-decoder architecture to extract temporal features in an unsupervised manner. Then, we fuse these two features to recognize activities and train them with manual labels, thereby refining the temporal feature extraction and training CNN module. Experiments on five public data sets demonstrate the exceptional performance of our proposed method, which can achieve a higher recognition accuracy on almost all the data sets and is more robust and adaptive among different data sets.