  Healthcare monitoring is crucial, especially for the daily care of elderly
individuals living alone. It can detect dangerous occurrences, such as falls,
and provide timely alerts to save lives. Non-invasive millimeter wave (mmWave)
radar-based healthcare monitoring systems using advanced human activity
recognition (HAR) models have recently gained significant attention. However,
they encounter challenges in handling sparse point clouds, achieving real-time
continuous classification, and coping with limited monitoring ranges when
statically mounted. To overcome these limitations, we propose RobHAR, a movable
robot-mounted mmWave radar system with lightweight deep neural networks for
real-time monitoring of human activities. Specifically, we first propose a
sparse point cloud-based global embedding to learn the features of point clouds
using the light-PointNet (LPN) backbone. Then, we learn the temporal pattern
with a bidirectional lightweight LSTM model (BiLiLSTM). In addition, we
implement a transition optimization strategy, integrating the Hidden Markov
Model (HMM) with Connectionist Temporal Classification (CTC) to improve the
accuracy and robustness of the continuous HAR. Our experiments on three
datasets indicate that our method significantly outperforms the previous
studies in both discrete and continuous HAR tasks. Finally, we deploy our
system on a movable robot-mounted edge computing platform, achieving flexible
healthcare monitoring in real-world scenarios.
