Real-Time Joint-Torque Feedback in VR Pre-Training for Safe Lifting: A Comparative Study of Visual Encodings

Kouichi Iwami, Tetsunari Inamura

Abstract: We present a VR-based pre-training system that estimates user-specific joint torques in real time by coupling a VR interaction environment (SIGVerse) with a biomechanics simulator (DhaibaWorks). The system visualizes internal load together with postural information using four encodings (color map, bar graph, exemplar posture, and exemplar+self posture) and enables rehearsal of lifting posture without handling real weight. In a within-subject study (11 participants), a simulated box-lifting task was evaluated using (i) time-integrated lumbar torque normalized by body mass and (ii) two 7-point Likert ratings (perceived comprehension and perceived load reduction). Across conditions, we did not observe a reliable reduction in normalized torque after training. Perceived load reduction showed a significant overall condition effect, whereas perceived comprehension showed no clear between-condition differences. These findings indicate that visualizing internal load can influence users’ perception of effort, although the present short session did not yield measurable torque changes. The proposed platform provides a safe pre-training route for learning low-strain movement strategies and a foundation for adaptive human-agent interaction that can leverage real-time estimates of human physical state.