VersaPants: A Loose-Fitting Textile Capacitive Sensing System for Lower-Body Motion Capture

• URL: http://arxiv.org/abs/2511.16346v1
• Date: Thu, 20 Nov 2025 13:30:02 GMT
• Title: VersaPants: A Loose-Fitting Textile Capacitive Sensing System for Lower-Body Motion Capture
• Authors: Deniz Kasap, Taraneh Aminosharieh Najafi, Jérôme Paul Rémy Thevenot, Jonathan Dan, Stefano Albini, David Atienza,
• Abstract summary: We present VersaPants, the first loose-fitting, textile-based capacitive sensing system for lower-body motion capture.<n>The system reconstructs lower-body pose without compromising comfort.<n>We employ a lightweight Transformer-based deep learning model that maps capacitance signals to joint angles.
• Score: 3.4228651201337215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present VersaPants, the first loose-fitting, textile-based capacitive sensing system for lower-body motion capture, built on the open-hardware VersaSens platform. By integrating conductive textile patches and a compact acquisition unit into a pair of pants, the system reconstructs lower-body pose without compromising comfort. Unlike IMU-based systems that require user-specific fitting or camera-based methods that compromise privacy, our approach operates without fitting adjustments and preserves user privacy. VersaPants is a custom-designed smart garment featuring 6 capacitive channels per leg. We employ a lightweight Transformer-based deep learning model that maps capacitance signals to joint angles, enabling embedded implementation on edge platforms. To test our system, we collected approximately 3.7 hours of motion data from 11 participants performing 16 daily and exercise-based movements. The model achieves a mean per-joint position error (MPJPE) of 11.96 cm and a mean per-joint angle error (MPJAE) of 12.3 degrees across the hip, knee, and ankle joints, indicating the model's ability to generalize to unseen users and movements. A comparative analysis of existing textile-based deep learning architectures reveals that our model achieves competitive reconstruction performance with up to 22 times fewer parameters and 18 times fewer FLOPs, enabling real-time inference at 42 FPS on a commercial smartwatch without quantization. These results position VersaPants as a promising step toward scalable, comfortable, and embedded motion-capture solutions for fitness, healthcare, and wellbeing applications.

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