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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10507957" target="_blank" >RIV/00216208:11320/25:10507957 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=vq~Ps9hRqZ" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=vq~Ps9hRqZ</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2025.111383" target="_blank" >10.1016/j.engappai.2025.111383</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Svazek periodika

158

Číslo periodika v rámci svazku

Part B

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

111383

Kód UT WoS článku

001520228200005

EID výsledku v databázi Scopus

2-s2.0-105008790119