Energy-efficient activity and sleep recognition using edge computing and wearable devices

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0201248" target="_blank" >RIV/00216305:26220/26:0201248 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11268695" target="_blank" >https://ieeexplore.ieee.org/document/11268695</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/icumt67815.2025.11268695" target="_blank" >10.1109/icumt67815.2025.11268695</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Energy-efficient activity and sleep recognition using edge computing and wearable devices

Popis výsledku v původním jazyce

Recent advancements in human activity recognition (HAR) have facilitated its integration into diverse applications, including the monitoring of vulnerable individuals. This work presents an edge computing-based framework for activity classification and sleep detection, emphasizing safety, energy efficiency, and minimized data transmission. Subsequently, dedicated algorithms for HAR and sleep detection were designed and implemented. Five HAR models and four sleep detection models were trained using selected datasets. The proposed system performs feature extraction directly on the edge device, significantly reducing communication overhead. The feature extraction methods are designed in a way that also reduces energy consumption. Results demonstrate that statistical feature computation on the edge device reduces energy consumption by around 5× compared to the on-device classification method, and data transmission volume by approximately 35× compared to a system transmitting raw sensor data. The random forest classifier achieved the highest HAR accuracy of 96.9%, while for sleep detection, the random forest reached an accuracy of 91.5%. The proposed architecture offers an effective trade-off between classification accuracy, energy usage, and communication cost, making it suitable for deployment in constrained edge environments.

Název v anglickém jazyce

Energy-efficient activity and sleep recognition using edge computing and wearable devices

Popis výsledku anglicky

Recent advancements in human activity recognition (HAR) have facilitated its integration into diverse applications, including the monitoring of vulnerable individuals. This work presents an edge computing-based framework for activity classification and sleep detection, emphasizing safety, energy efficiency, and minimized data transmission. Subsequently, dedicated algorithms for HAR and sleep detection were designed and implemented. Five HAR models and four sleep detection models were trained using selected datasets. The proposed system performs feature extraction directly on the edge device, significantly reducing communication overhead. The feature extraction methods are designed in a way that also reduces energy consumption. Results demonstrate that statistical feature computation on the edge device reduces energy consumption by around 5× compared to the on-device classification method, and data transmission volume by approximately 35× compared to a system transmitting raw sensor data. The random forest classifier achieved the highest HAR accuracy of 96.9%, while for sleep detection, the random forest reached an accuracy of 91.5%. The proposed architecture offers an effective trade-off between classification accuracy, energy usage, and communication cost, making it suitable for deployment in constrained edge environments.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/9A24005" target="_blank" >9A24005: Distributed Multi-Sensor Systems for Human Safety and Health</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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