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.

通过在可移动机器人上装载毫米波雷达系统与轻量级深度神经网络结合，实现了实时监测人类活动的医疗监测系统。

基于毫米波雷达的医疗监护机器人人体活动识别

Millimeter Wave Radar-based Human Activity Recognition for Healthcare  Monitoring Robot

Wearable devices like smart glasses have gained popularity across various
applications. However, their limited computational capabilities pose challenges
for tasks that require extensive processing, such as image and video
processing, leading to drained device batteries. To address this, offloading
such tasks to nearby powerful remote devices, such as mobile devices or remote
servers, has emerged as a promising solution. This paper focuses on analyzing
task-offloading scenarios for a healthcare monitoring application performed on
smart wearable glasses, aiming to identify the optimal conditions for
offloading. The study evaluates performance metrics including task completion
time, computing capabilities, and energy consumption under realistic
conditions. A specific use case is explored within an indoor area like an
airport, where security agents wearing smart glasses to detect elevated body
temperature in individuals, potentially indicating COVID-19. The findings
highlight the potential benefits of task offloading for wearable devices in
healthcare settings, demonstrating its practicality and relevance.

该论文研究了在智能可穿戴眼镜上进行的医疗监测应用的任务卸载场景，旨在识别最优的卸载条件，并评估了在现实环境下任务完成时间、计算能力和能量消耗等性能指标。研究重点关注在如机场等室内区域进行的特定用例，安全人员佩戴智能眼镜来检测个体的体温是否升高，从而可能表明患有 COVID-19。研究结果突出了在医疗环境中为可穿戴设备进行任务卸载的潜在益处，证明了其实用性与相关性。

智能眼镜中的任务卸载：增强体温升高的检测

Task Offloading for Smart Glasses in Healthcare: Enhancing Detection of  Elevated Body Temperature

The need for remote tools for healthcare monitoring has never been more
apparent. Camera measurement of vital signs leverages imaging devices to
compute physiological changes by analyzing images of the human body. Building
on advances in optics, machine learning, computer vision and medicine these
techniques have progressed significantly since the invention of digital
cameras. This paper presents a comprehensive survey of camera measurement of
physiological vital signs, describing they vital signs that can be measured and
the computational techniques for doing so. I cover both clinical and
non-clinical applications and the challenges that need to be overcome for these
applications to advance from proofs-of-concept. Finally, I describe the current
resources (datasets and code) available to the research community and provide a
comprehensive webpage (this https URL) with links to these
resource and a categorized list of all the papers referenced in this article.

摄像头技术可以通过分析人体图像以计算生理变化，因此在遥测医疗监测方面有着极大的应用潜力，同时也存在一定的挑战和需要解决的问题。该研究论文综述了这一技术的现状、能够测量的生理指标及其计算方法，并涵盖了临床和非临床应用。