Target Recognition Based on Millimeter-Wave-Sensed Point Cloud Using PointNet++ Model

During walking, the human lower limbs primarily support the body and drive forward motion, while the arms exhibit greater variability and flexibility without bearing such loads. In gait-based target recognition, collecting exhaustive arm-motion data for training is challenging, and unseen arm movements during testing may degrade the performance. This paper investigates the impact of arm movements on radar-based gait recognition and proposes a gait recognition method using extracted lower limb motion data to mitigate interference from different arm motions. Gait data is collected via a millimeter-wave radar sensor encompassing four kinds of common arm movements, including natural arm swings, object-holding states, and irregular arm motions, from 11 volunteers. Using extracted lower limb motion data, millimeter wave point-cloud gait datasets covering diverse arm motions are generated. Three gait recognition experiments are conducted for comparing the performances of our proposed method using only lower limb data and existing method using all limb data, both based the on PointNet++ model. And the experimental results show that our method consistently outperforms existing methods, with a 22.9-percent improvement in accuracy. Results also show that the proposed method can enhance feature extraction, accelerate convergence, and achieve higher accuracy, especially with limited samples, and the highest recognition accuracy reaches 96.9%. In addition, in unseen arm movement cases, our method significantly outperforms existing methods, demonstrating superior robustness and recognition accuracy.