Multimodal and Multi-granularity Graph Convolutional Networks for Elderly Daily Activity Recognition

With the problem of the aging population becomes serious, more attention is payed to the safety of the elderly when they are at home alone. In order to provide early warning, alarm, and report of some dangerous behaviors, several domestic and foreign research institutions are focusing on studying the intelligent monitoring of the daily activities of the elderly in robot-view. For promoting the industrialization of these technologies, this work mainly studies how to automatically recognize the daily activities of the elderly, such as “drinking water”, “washing hands”, “reading a book”, “reading a newspaper”. Through the investigation of the daily activity videos of the elderly, it is found that the semantics of the daily activities of the elderly are obviously fine-grained. For example, the semantics of “drinking water” and “taking medicine” are highly similar, and only a small number of video frames can accurately reflect their category semantics. To effectively address such problem of the elderly behavior recognition, this work proposes a new multimodal multi-granularity graph convolutional network (MM-GCN), by applying the graph convolution network on four modalities, i.e., the skeleton (“point”), bone (“line”), frame (“frame”), and proposal (“segment”), to model the activities of the elderly, and capture the semantics under the four granularities of “point-line-frame-proposal”. Finally, the experiments are conducted to validate the activity recognition performance of the proposed method on ETRI-Activity3D (110000+ videos, 50+ classes), which is the largest daily activities dataset for the elderly. Compared with the state-of-the-art methods, the proposed MM-GCN achieves the highest recognition accuracy. In addition, in order to verify the robustness of MM-GCN for the normal human action recognition tasks, the experiment is also carried out on the benchmark NTU RGB+D, and the results show that MM-GCN is comparable to the SOTA methods. © 2023 Chinese Academy of Sciences. All rights reserved.