Intelligent Seat: Tactile Signal-Based 3D Sitting Pose Inference

Abstract

Owing to people spending a large portion of their day sitting while working, commuting, or relaxing, monitoring their sitting posture is crucial for the development of adaptive interventions that respond to the user's pose, state, and behavior. This is because posture is closely linked to actions, health, attention, and engagement levels. The existing systems for posture estimation primarily use computer vision-based measurements or body-attached sensors; however, they are plagued by challenges such as privacy concerns, occlusion issues, and user discomfort. To address these drawbacks, this study proposed a posture-inference system that uses high-density piezoresistive sensors for joint reconstruction. Tactile pressure data were collected from six individuals, each performing seven different postures 20 times. The proposed system achieved an average L2 distance of 20.2 cm in the joint position reconstruction with a posture classification accuracy of 96.3%. Future research will focus on the development of a system capable of providing real-time feedback to help users maintain the correct sitting posture.