Wearable Movement Training
Gait retraining to reduce the knee adduction moment is an important treatment method for knee osteoarthritis, but current paradigms do not account for differences in human anatomy and do not allow patients to change walking directions or perceive haptic feedback sensations during gait, which have prevented clinical application. This project investigates human movement control, sensing, and haptic feedback, to explore the scientific basis for a wearable system to improve knee osteoarthritis based on novel concepts of data-driven adaptable gait prediction optimization, real-time heading vector estimation, and skin mechanical resonance haptic feedback. The research is conducted based on technical theories and scientific experiments to investigate the following issues: real-time gait biomechanics modeling and wearable sensor fusion, gait prediction optimization modeling and control, and wearable skin stretch feedback device design.
This project focuses on three key problems: 1) inability to accurately predict new gait kinematics to reduce the knee adduction moment, 2) inability to measure human gait kinematics after humans change walking directions, and 3) imperceptible haptic feedback during gait. A wearable sensing and haptic feedback system prototype will be developed to demonstrate key research findings and clinical testing will be performed to establish a scientific basic for practical application. The results of this study are intended to provide a scientific foundation for wearable systems research for gait retraining.
Wearable System for Gait and Balance Training
Wearable system hardware
Eight distributed nodes (Dots) simultaneously send and receive data to and from the central control unit (Hub) where real-time computation and control algorithms are performed. Dots are configured in software to act as sensors, feedback vibrotactors, or both. Each Dot is comprised of a 9-axis IMMU sensor, vibration feedback motor, ZigBee wireless communication module, and a 100 mAh lithium ion battery. All Dot components are embedded in a single silicon mold. Each Dot weighs 12 grams and the overall top surface area is roughly the same as a 25 mm coin.
Real-time sensing and feedback control diagram
Raw wearable sensing data is transformed into relevant biomechanical measurements through the real-time gait biomechanics model. The gait prediction optimization model outputs desired biomechanical measurements and wearable haptic feedback is used to inform patients of require gait changes.
The purpose of this gait retraining is to reduce the knee adduction moment, which has been shown to reduce knee pain and improve knee symptoms for patients with medial compartment knee osteoarthritis.