Abstract
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.
Abstract (translated)
医疗监测对于单独生活的老年人来说至关重要。它可以检测到像跌倒这样的危险情况，并为拯救生命提供及时的警报。基于先进的人活动识别（HAR）模型的非侵入性毫米波（mmWave）医疗监测系统近年来引起了广泛关注。然而，它们在处理稀疏点云、实现实时连续分类和应对有限监测范围时遇到了挑战。为了克服这些限制，我们提出了RobHAR，一种可移动的机器人搭载的mmWave雷达系统，用于实时监测人类活动。具体来说，我们首先提出了基于稀疏点云的全局嵌入来学习点云的特征，使用光点网络（LPN）骨干网络。然后，我们使用双向轻量级LSTM模型学习时间模式。此外，我们还实现了一个转换优化策略，将隐马尔可夫模型（HMM）与连接式 Temporal Classification（CTC）结合，以提高连续 HAR的准确性和鲁棒性。我们对三个数据集的实验结果表明，我们的方法在离散和连续 HAR任务中显著超过了之前的研究。最后，我们将系统部署在可移动机器人搭载的边缘计算平台上，实现了在现实场景中灵活的医疗监测。
URL
https://arxiv.org/abs/2405.01882
