Efficient reconfigurable system for home monitoring of the elderly via action recognition

The rapid growth of the aging population poses serious challenges for our society e.g. the increase of the healthcare costs. Smart-Health Cyber–Physical Systems (CPSs) offer innovative solutions to ease this burden. This work proposes a general framework adapted in run-time to optimize the system's overall performance, continuously monitoring system working qualities such as response time, accuracy, or energy consumption. Adaptation is achieved through the automatic deployment of different artificial intelligence (AI) based models on local edges (particularly deep learning models (DL)). Local processing is performed in embedded devices that provide short latency and real-time processing despite their limited computation capacity compared to high-end cloud servers. The paper validates this reconfigurable CPS in a challenging scenario: indoor ambient assisted living for the elderly. Our system collects lifestyle user data in a non-invasive manner to promote healthy habits and triggers alarms in case of emergency. Local edge video processing nodes identify indoor activities powered by state-of-the-art deep-learning action recognition models. The optimized embedded nodes locally reduce cost and power consumption, but the system still needs to maximize the overall performance in a changing environment. To that end, our solution enables run-time reconfiguration to adapt in terms of functionality or resource availability, offloading computation when required. The experimental section shows a real setup performing run-time adaptation with different reconfiguration policies considering average times for different daily activities. For that example, the adaptation extends the working time in more than 60% and achieves a 3x confidence in recognition for critical actions.