IoT System for Real-Time Posture Asymmetry Detection

Area of Science:

• Biomedical Engineering
• Human-Computer Interaction
• Occupational Health

Background:

• Musculoskeletal disorders are common among computer workers due to prolonged incorrect sitting postures.
• Internet of Things (IoT) systems offer potential for health monitoring in smart environments.
• Existing solutions may lack cost-effectiveness or real-time feedback for posture correction.

Purpose of the Study:

• To propose and evaluate a low-cost IoT measurement system for monitoring sitting posture symmetry.
• To provide real-time visual alerts to workers exhibiting asymmetric sitting positions.
• To record sitting behavior data for further analysis and intervention development.

Main Methods:

• Development of a cushion embedded with four force sensing resistors (FSRs).
• Implementation of a microcontroller-based circuit for sensor data acquisition.
• Utilization of Java-based software for real-time monitoring and an uncertainty-driven asymmetry detection algorithm.
• Integration with a web database for logging posture shifts.

Main Results:

• The system successfully detects shifts between symmetric and asymmetric sitting postures.
• Real-time visual pop-up alerts are generated to notify the worker of asymmetric positions.
• Posture shift data is logged for subsequent analysis of sitting behavior patterns.

Conclusions:

• The developed IoT system offers a practical and low-cost solution for monitoring and improving sitting posture.
• Real-time alerts can prompt workers to adjust their posture, potentially reducing the risk of musculoskeletal disorders.
• The system's data logging capability supports further research into ergonomic interventions and workplace health.