Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition
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by
Han Sun, Yu Chen
2022
Abstract
With an increasing number of elders living alone, care-giving from a distance
becomes a compelling need, particularly for safety. Real-time monitoring and
action recognition are essential to raise an alert timely when abnormal
behaviors or unusual activities occur. While wearable sensors are widely
recognized as a promising solution, highly depending on user's ability and
willingness makes them inefficient. In contrast, video streams collected
through non-contact optical cameras provide richer information and release the
burden on elders. In this paper, leveraging the Independently-Recurrent neural
Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior
Safety (REMS) based on lightweight human action recognition (HAR) technology.
Using captured skeleton images, the REMS scheme is able to recognize abnormal
behaviors or actions and preserve the user's privacy. To achieve high accuracy,
the HAR module is trained and fine-tuned using multiple databases. An extensive
experimental study verified that REMS system performs action recognition
accurately and timely. REMS meets the design goals as a privacy-preserving
elderly safety monitoring system and possesses the potential to be adopted in
various smart monitoring systems.
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