An HCI-Centered Posture Scoring and Feedback System with Machine Learning Regression for Personalized Sitting Behavior Monitoring Using Smart E-Textile Sensing
International Journal of Human-Computer Interaction, 2026 (SCI-Expanded, SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/10447318.2026.2739764
- Dergi Adı: International Journal of Human-Computer Interaction
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, Psycinfo, Academic Search Ultimate (EBSCO), Business Source Ultimate (EBSCO), Engineering Source (EBSCO), Psychology & Behavioral Sciences Collection (EBSCO), Technology Collection (ProQuest)
- Anahtar Kelimeler: human-centered computing, Human–computer interaction, machine learning, posture assessment, smart textiles
- Kütahya Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
This study presents a human-centred sitting monitoring system that moves beyond discrete posture classification by estimating a continuous Healthy Sitting Score (HSS, 0–100) from electro-textile sensing data. Forty-eight tactile features were used with PINNR, SVR, and GPR models, and generalization was evaluated using participant-wise Group Cross-Validation and an independent unseen-subject test set. The best PINNR model achieved R = 0.948, R² = 0.898, and RMSE = 6.83 on unseen participants. The system was integrated with a Bluetooth-enabled Android application providing real-time HSS visualization, history, summaries, and configurable notifications. In a 21-participant workplace evaluation, usability ratings were high and HSS increased significantly after the first notification, although the absence of a no-feedback control limits causal interpretation. Repeated-use analysis over 21 workdays showed no widespread sensor drift. Overall, the study emphasizes continuous posture-quality scoring, personalized feedback, behavioural response, and real-world HCI evaluation rather than multi-class posture recognition.