Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning

TL;DR

This paper presents a reconfigurable, energy-efficient cyber-physical system for indoor human activity monitoring using embedded deep learning nodes, enhancing privacy, reducing bandwidth, and extending device battery life.

Contribution

It introduces a novel reconfigurable CPS architecture with remote management and energy-aware reconfiguration, improving efficiency while maintaining accuracy.

Findings

Reconfiguration reduces energy consumption by up to 22%.

The system maintains similar accuracy with reconfiguration.

Real-time local processing enhances privacy and bandwidth efficiency.

Abstract

Indoor monitoring of people at their homes has become a popular application in Smart Health. With the advances in Machine Learning and hardware for embedded devices, new distributed approaches for Cyber-Physical Systems (CPSs) are enabled. Also, changing environments and need for cost reduction motivate novel reconfigurable CPS architectures. In this work, we propose an indoor monitoring reconfigurable CPS that uses embedded local nodes (Nvidia Jetson TX2). We embed Deep Learning architectures to address Human Action Recognition. Local processing at these nodes let us tackle some common issues: reduction of data bandwidth usage and preservation of privacy (no raw images are transmitted). Also real-time processing is facilitated since optimized nodes compute only its local video feed. Regarding the reconfiguration, a remote platform monitors CPS qualities and a Quality and Resource Management (QRM) tool sends commands to the CPS core to trigger its reconfiguration. Our proposal is an energy-aware system that triggers reconfiguration based on energy consumption for battery-powered nodes. Reconfiguration reduces up to 22% the local nodes energy consumption extending the device operating time, preserving similar accuracy with respect to the alternative with no reconfiguration.