VibWalk: Mapping Lower-limb Haptic Experiences of Everyday Walking

2025-04-12

AI Summary

This work addresses the challenge of unobtrusively acquiring and semantically decoding plantar vibration signals during daily walking to model lower-limb tactile experiences—including ground material, locomotion state, and road conditions. Method: We propose the first wearable wideband plantar vibration sensing system, integrating multi-point sensing, high-fidelity signal acquisition, and GNSS/IMU-synchronized spatiotemporal labeling. A cross-user transfer learning classification framework is developed to systematically decode tactile semantics from walking-induced vibrations. Contribution/Results: Evaluated on 31 participants across 18 real-world surface types, our system achieves >95% overall material classification accuracy and 87% cross-user generalization accuracy. It enables real-time, passive, high-precision, and robust tactile mapping—establishing a novel paradigm for embodied perception and digital twin applications.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Walking is among the most common human activities where the feet can gather rich tactile information from the ground. The dynamic contact between the feet and the ground generates vibration signals that can be sensed by the foot skin. While existing research focuses on foot pressure sensing and lower-limb interactions, methods of decoding tactile information from foot vibrations remain underexplored. Here, we propose a foot-equipped wearable system capable of recording wideband vibration signals during walking activities. By enabling location-based recording, our system generates maps of haptic data that encode information on ground materials, lower-limb activities, and road conditions. Its efficacy was demonstrated through studies involving 31 users walking over 18 different ground textures, achieving an overall identification accuracy exceeding 95% (cross-user accuracy of 87%). Our system allows pedestrians to map haptic information through their daily walking activities, which has potential applications in creating digitalized walking experiences and monitoring road conditions.

Problem

• Decoding tactile information from foot vibrations during walking
• Mapping haptic data for ground materials and road conditions
• Developing a wearable system for recording walking vibration signals

Innovation

• Wearable system records foot vibration signals
• Maps haptic data for ground materials
• Achieves high accuracy in texture identification