Lower-Limb Full-Body Motion Acquisition Using Wearable Multisensor Integration for Human-Robot Interaction
This paper presents a ROS 2-based multimodal wearable system for lower limb gait analysis, integrating custom intelligent shoes with 48 pressure sensors, IMUs, and bending sensors, along with Xsens DOT sensors on the hip and knee. All data streams are synchronized in ROS 2 for real-time fusion and preprocessed with low-pass filtering. Experiments show reliable detection of gait events like heel strike and toe-off, providing a low-cost, portable platform for future gait analysis, rehabilitation, and sports applications, with potential for machine learning-based gait classification and real-time feedback.
Authors
- Wen Qi (ORCID: https://orcid.org/0000-0002-2091-3718)
- Samer Alfayad (ORCID: https://orcid.org/0000-0001-6432-7764)
- Hanbing Liu (ORCID: https://orcid.org/0009-0005-7587-9886)
- Hang Su (ORCID: https://orcid.org/0000-0002-6877-6783)
- Loqmane Bencharif
- Abderahim Ibset
Institutions
- Université Paris-Saclay (FR)
- Informatique, BioInformatique, Systèmes Complexes (FR)
- South China University of Technology (CN)
Publication Details
- Published
- 2025-10-09
- DOI
- https://doi.org/10.1109/icabme66883.2025.11211743
- Primary Topic
- Prosthetics and Rehabilitation Robotics
- Type
- article
- Field-Weighted Citation Impact
- 0.00