Odesola David Faith, Kulon Janusz, Verghese Shiny, Partlow Adam, Gibson Colin
Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd CF37 1DL, UK.
Rehabilitation Engineering Unit, Artificial Limb & Appliance Service, Cardiff and Vale University Health Board, Treforest Industrial Estate, Pontypridd CF37 5TF, UK.
Sensors (Basel). 2025 Sep 9;25(18):5610. doi: 10.3390/s25185610.
Prolonged sitting and the adoption of unhealthy sitting postures have been a common issue generally seen among many adults and the working population in recent years. This alone has contributed to the alarming rise of various health issues, such as musculoskeletal disorders and a range of long-term health conditions. Hence, this study proposes the development of a novel smart-sensing chair system designed to analyze and provide actionable insights to help encourage better postural habits and promote well-being. The proposed system was equipped with two 32 × 32 pressure sensor mats, which were integrated into an office chair to facilitate the collection of postural data. Unlike traditional approaches that rely on generalized datasets collected from multiple healthy participants to train machine learning models, this study adopts a user-tailored methodology-collecting data from a single individual to account for their unique physiological characteristics and musculoskeletal conditions. The dataset was trained using five different machine learning models-Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN)-to classify 19 distinct sitting postures. Overall, CNN achieved the highest accuracy, with 98.29%. To facilitate user engagement and support long-term behavior change, we developed SitWell-an intelligent postural feedback platform comprising both mobile and web applications. The platform's core features include sitting posture classification, posture duration analytics, and sitting quality assessment. Additionally, the platform integrates OpenAI's GPT-4o Large Language Model (LLM) to deliver personalized insights and recommendations based on users' historical posture data.
长时间坐着以及采用不健康的坐姿已成为近年来许多成年人和工作人群中普遍存在的问题。仅此一点就导致了各种健康问题惊人地增加，比如肌肉骨骼疾病和一系列长期健康状况。因此，本研究提出开发一种新型智能传感椅子系统，旨在进行分析并提供可采取行动的见解，以帮助鼓励养成更好的姿势习惯并促进健康。所提出的系统配备了两个32×32的压力传感器垫，这些传感器垫被集成到一把办公椅中，以方便收集姿势数据。与传统方法不同，传统方法依赖于从多个健康参与者收集的通用数据集来训练机器学习模型，本研究采用了一种针对用户的方法——从单个个体收集数据，以考虑其独特的生理特征和肌肉骨骼状况。使用五种不同的机器学习模型——决策树（DT）、随机森林（RF）、支持向量机（SVM）、K近邻（KNN）和卷积神经网络（CNN）——对数据集进行训练，以对19种不同的坐姿进行分类。总体而言，CNN的准确率最高，为98.29%。为了促进用户参与并支持长期行为改变，我们开发了SitWell——一个智能姿势反馈平台，包括移动应用程序和网络应用程序。该平台的核心功能包括坐姿分类、姿势持续时间分析和坐姿质量评估。此外，该平台集成了OpenAI的GPT-4o大语言模型（LLM），以根据用户的历史姿势数据提供个性化的见解和建议。
