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.

翻译：医疗监护至关重要，尤其对于独居老人的日常照护而言。它能够检测跌倒等危险事件并及时发出警报以挽救生命。基于非侵入式毫米波雷达的医疗监护系统借助先进的人体活动识别模型，近年来受到广泛关注。然而，此类系统在静态安装时面临稀疏点云处理、实时连续分类以及有限监测范围的挑战。为克服这些限制，我们提出RobHAR——一种搭载于可移动机器人的毫米波雷达系统，采用轻量级深度神经网络实现人体活动的实时监测。具体而言，我们首先提出基于稀疏点云的全局嵌入方法，利用轻量级PointNet骨干网络学习点云特征；随后通过双向轻量级LSTM模型学习时序模式；此外，我们实施状态转换优化策略，将隐马尔可夫模型与连接时序分类进行融合，以提升连续人体活动识别的准确性与鲁棒性。在三个数据集上的实验表明，我们的方法在离散与连续的人体活动识别任务中均显著优于以往研究。最后，我们将系统部署于可移动机器人的边缘计算平台，在真实场景中实现灵活的医疗监护。