Developing and Evaluating a Mixed Sensor Smart Chair System for Real-Time Posture Classification: Combining Pressure and Distance Sensors

A novel sensor-embedded smart chair system was developed to monitor and classify a worker's sitting postures in real time. The smart chair system was a mixed sensor system utilizing six pressure sensors and six infrared reflective distance sensors in combination. The pressure sensors were embedded in the seat cushion to gather seat cushion pressure distribution data. The distance sensors were placed in the seatback to measure seatback-trunk distances at different locations in the frontal plane. The use of the seatback distance sensors represented a unique design feature, which distinguished the mixed sensor system from the previous posture monitoring systems. Employing a k-Nearest Neighbor algorithm, the mixed sensor system classified an instantaneous posture as one of posture categories determined based on an analysis of the ergonomics literature on sitting postures and sitting-related musculoskeletal problems. The mixed sensor system was evaluated in posture classification performance in comparison with two benchmark systems that utilized only a single type of sensors. The purpose of the comparisons was to determine the utility of the design combining seat cushion pressure sensors and seatback distance sensors. The mixed sensor system yielded significantly superior classification performance than the two benchmark systems. The mixed sensor system is low-cost utilizing only a small number of sensors; yet, it accomplishes accurate classification of postures relevant to the ergonomic analyses of seated work tasks. The mixed sensor system could be utilized for various applications including the development of a real-time posture feedback system for preventing sitting-related musculoskeletal disorders.

Automated summary

A novel sensor-embedded smart chair system has been developed to monitor and classify a worker's sitting postures in real time. By utilizing seat cushion pressure sensors and seatback distance sensors, the system achieves significantly superior classification performance compared to benchmark systems. The low-cost system could be utilized for various applications, including the development of a real-time posture feedback system for preventing sitting-related musculoskeletal disorders.