The Effects of Real-Time Haptic Feedback on Gait and Cognitive Load in Older Adults
Abstract
Gait speed is a key indicator of mobility and health in older adults, with declines often reflecting neuromotor deficits rather than musculoskeletal or cardiopulmonary limitations. This study presents a wearable smartphone-based haptic feedback system that applies feedback to the thighs to increase peak thigh extension (PTE) and, consequently, improve stride length and walking speed. Thirty community-dwelling older adults ( 79.9 ± 6.5 years) participated in this study. Three treatment conditions were evaluated: (1) tactile feedback due to incorrect response when PTE was below the target ( FIR ); (2) tactile feedback due to correct response when the target PTE was met ( FCR ); and (3) verbal instructions ( IV ) without feedback. Cognitive demand during treatment was assessed using a probe reaction time task. Walking trials comparing baseline with treatment conditions were conducted. We found significant differences for all gait parameters across walking trials ( p<0.001 ), but no significant difference among the three treatment conditions. The haptic feedback system significantly increased stride length by 14% and gait speed by 18%. Gains in speed and stride length were achieved using the haptic system during a single session, comparable to following verbal instructions. Although no statistical difference was found across treatments, thigh feedback employed a different mechanism than verbal instructions for attaining greater speed. Adding haptic feedback increased reaction time, but these increases were small ( FIR : 27ms, FCR : 74ms), indicating minimal cognitive load. The observed gait improvements suggest haptic feedback is a viable option for gait training for older adults.
Type
Publication
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 33, 2335-2344
Status
Peer-reviewed

Authors
PhD Candidate in Mechanical Engineering
I am a third-year PhD candidate in Mechanical Engineering at the University of Maine’s Biorobotics and Biomechanics Lab, supervised by Dr. Babak Hejrati. My research focuses on developing wearable inertial measurement unit (IMU)-based sensing systems for real-time gait analysis and fall prevention in elderly adults.