Engagement-free and Contactless Bed Occupancy and Vital Signs Monitoring.

This paper presents the design and evaluation of an engagement-free and contactless vital signs and occupancy monitoring system called BedDot. While many existing works demonstrated contactless vital signs estimation, they do not address the practical challenge of environment noises, online bed occupancy detection and data quality assessment in the realworld environment. This work presents a robust signal quality assessment algorithm consisting of three parts: bed occupancy detection, movement detection, and heartbeat detection, to identify high-quality data. It also presents a series of innovative vital signs estimation algorithms that leverage the advanced signal processing and Bayesian theorem for contactless heart rate (HR), respiration rate (RR), and inter-beat interval (IBI) estimation. The experimental results demonstrate that BedDot achieves over 99% accuracy for bed occupancy detection, and MAE of 1.38 BPM, 1.54 BPM, and 24.84 ms for HR, RR, and IBI estimation, respectively, compared with an FDA-approved device. The BedDot system has been extensively tested with data collected from 75 subjects for more than 80 hours under different conditions, demonstrating its generalizability across different people and environments.

Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach.

The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications.