A graph neural network model application in point cloud structure for prolonged sitting detection system based on smartphone sensor data

The prolonged sitting inherent in modern work and study environments poses significant health risks, necessitating effective monitoring solutions. Traditional human activity recognition systems often fall short in these contexts owing to their reliance on structured data, which may fail to capture t...

Full description

Saved in:
Bibliographic Details
Published in:ETRI journal Vol. 47; no. 2; pp. 290 - 302
Main Authors: Hardjianto, Mardi, Istiyanto, Jazi Eko, Tjoa, A. Min, Syahrulfath, Arfa Shaha, Purnama, Satriawan Rasyid, Sari, Rifda Hakima, Hakim, Zaidan, Fuadin, M. Ridho, Ananto, Nias
Format: Journal Article
Language:English
Published: Electronics and Telecommunications Research Institute (ETRI) 01.04.2025
한국전자통신연구원
Subjects:
graph neural network
point cloud
prolonged sitting detection
smartphone sensor
전자/정보통신공학
ISSN:1225-6463, 2233-7326
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The prolonged sitting inherent in modern work and study environments poses significant health risks, necessitating effective monitoring solutions. Traditional human activity recognition systems often fall short in these contexts owing to their reliance on structured data, which may fail to capture the complexity of human movements or accommodate the often incomplete or unstructured nature of healthcare data. To address this gap, our study introduces a novel application of graph neural networks (GNNs) for detecting prolonged sitting periods using point cloud data from smartphone sensors. Unlike conventional methods, our GNN model excels at processing the unordered, three‐dimensional structure of sensor data, enabling more accurate classification of sedentary activities. The effectiveness of our approach is demonstrated by its superior ability to identify sitting, standing, and walking activities—critical for assessing health risks associated with prolonged sitting. By providing real‐time activity recognition, our model offers a promising tool for healthcare professionals to mitigate the adverse effects of sedentary behavior.
Bibliography:Funding information
This research was supported by the Directorate of Research, Universitas Gadjah Mada and the Reputation Improvement Team for World Class University‐ Quality Assurance Office, Universitas Gadjah Mada—No. 13602/UN1.P.II/Dit‐Lit/PT.01.04/2022.
https://doi.org/10.4218/etrij.2023-0190
ISSN:1225-6463
2233-7326
DOI:10.4218/etrij.2023-0190