Due to the rapid development of human-computer interaction, human activity recognition has become a hot research topic. Millimeter-wave radar is an advanced device for collecting high-quality data. In this paper, we propose a human activity recognition system utilizing millimeter-wave radar. Firstly, we collect spectrograms caused by human motion using millimeter-wave radar. Then, we adopt residual neural networks to process data and complete the classification task. Based on our proposed system, we classify four activities and achieve an average recognition accuracy of 91.75%. In the end, we discuss some factors that influence the experimental results, and provide the future development of this research field.
Guixing GaoQuanli LiuWei WangZichen YuXin Liu
Wenlong LiXiaochuan WuWeibo DengYingning Dong
Tingpei HuangGuoyong LiuShibao LiJianhang Liu
Chengxi YuZhezhuang XuKun YanYing‐Ren ChienShih‐Hau FangHsiao‐Chun Wu
Seungchan LimChaewoon ParkSeongjoo LeeYunho Jung