@misc{10481/104845,
year = {2025},
month = {5},
url = {https://hdl.handle.net/10481/104845},
abstract = {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.},
organization = {ECSEL Joint Undertaking (JU) 783162},
organization = {European Union’s Horizon 2020},
organization = {The Netherlands},
organization = {Czech Republic},
organization = {Finland},
organization = {Spain},
organization = {Italy},
organization = {MICIU/AEI/10.13039/501100011033 PID2022-141466OB-I00},
organization = {ERDF/EU},
publisher = {Elsevier},
keywords = {Cyber-Physical System},
keywords = {Artificial intelligence},
keywords = {Action recognition},
title = {Efficient reconfigurable system for home monitoring of the elderly via action recognition},
doi = {10.1016/j.engappai.2025.111383},
author = {Déniz Cerpa, José Daniel and Isern Ghosn, Juan Ignacio and Solanti, Jan and Jääskeläinen, Pekka Olavi and Hnětynka, Petr and Bulej, Lubomir and Ros Vidal, Eduardo and Barranco Expósito, Francisco},
}