Human Activity Recognition Using 2-D LiDAR and Deep Learning Technology

In this letter, a home care assistance system is proposed for elder human activity recognition that combines 2-D LiDAR and deep learning technology. First, the 2-D LiDAR is used to scan the room's interior data, and cluster algorithms are used to identify high-density areas that may be objects. The resulting data are then classified into human and nonhuman clusters. The coordinates of the human clusters are sequentially recorded to generate trajectory graphs. These trajectory graphs possess both spatial and temporal attributes and are processed using spatial–temporal graph convolutional networks to achieve accurate classification of human activities. Furthermore, the system is designed to detect abnormal trajectories that may indicate a fall and can send out a warning signal to notify the caregiver for emergency assistance. With these advanced features, the home care assistance system can improve the safety and well-being of elderly individuals living alone or with limited assistance.