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Designing a Smart Garment for Dynamic Sitting Reminders
Yujie Hou1, Zhaohui Wang1,2, Huanhuan Liu1
1College of Fashion and Design, Donghua University, Shanghai 200051, China.
Sensors (Basel, Switzerland)
|September 19, 2025
Summary
This study introduces a smart garment with textile sensors to correct sitting posture for office workers. It uses machine learning for accurate posture recognition and provides real-time feedback, reducing spinal discomfort.
Area of Science:
- Biomedical Engineering
- Wearable Technology
- Human-Computer Interaction
Background:
- Sedentary office work increases spinal disorders like lower back pain.
- Monitoring and improving sitting posture is crucial for prevention.
- Wearable technology offers effective health monitoring solutions.
Purpose of the Study:
- To design a textile sensor-based smart garment for sitting posture correction.
- To provide dynamic sitting reminders for sedentary office workers.
- To reduce the risk of spinal discomfort.
Main Methods:
- Integrated machine learning algorithms for real-time posture recognition (Random Forest classifier >95% accuracy).
- Developed haptic vibration and visual GUI feedback modes for intervention.
- Conducted comparative experiments and user satisfaction surveys.
Main Results:
- The smart garment achieved over 95% accuracy in sitting posture recognition.
- Effective sitting posture adjustment and reduced spinal discomfort risk observed in office workers.
- Positive user satisfaction with the system's usability and effectiveness.
Conclusions:
- The textile sensor-based smart garment is an effective solution for improving sitting posture in office workers.
- Dynamic reminders and feedback mechanisms aid in preventing spinal discomfort.
- Wearable technology shows promise in managing health issues related to sedentary lifestyles.
