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Intelligent Multimodal Cueing Wearable Device for Gait Rehabilitation in Parkinson's Disease Patients
Subject area: Science,Engineering and Technology · Area of research: Electronics, Rehabilitisation device
DOI: https://doi.org/10.64388/IREV9I3-1710722-8296
Abstract
Freezing of Gait (FOG) is one of the most debilitating- imitating symptoms of Parkinson?s Disease (PD), often resulting in falls, impaired mobility, and loss of in- dependence. This The paper presents a comprehensive and wearable assistive system designed to detect, predict, and mitigate FOG episodes in real time. The proposed device integrates multiple sensing and feedback modalities, including inertial measurement units (IMUs), surface electromyography (sEMG), dynamic visual cue- ing via laser projection, and haptic feedback through vibratory actuators. The system leverages a hybrid Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) model to recognize gait phases and detect movement intentions, achieving a classification accuracy of 90 percent. Simultaneously, a Random Forest classifier is trained on real-time sEMG signals to monitor dorsi flexor and plantar flexor activity, providing biomechanical insight into muscular performance. Based on this muscular feedback, the system adapts its visual and haptic cues dynamically to guide patients toward optimal step initiation and foot orientation. Visual cues?projected via a wearable laser?indicate the ideal foot placement trajectory, while vibratory feedback enhances proprioceptive awareness of foot movement, particularly aiding dorsi flexion. The device supports both indoor and treadmill- based rehabilitation, offers- In terms of flexibility for clinical deployment and home-based therapy.
Keywords
Freezing of Gait (FOG), Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM), Inertial Measurement Units (Imus), Surface Electromyography (SEMG), Dynamic Visual Cueing
How to cite this paper
@article{1710722,
author = {Duraiarasu E, Ananthanarayanan B},
title = {Intelligent Multimodal Cueing Wearable Device for Gait Rehabilitation in Parkinson's Disease Patients},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {3},
pages = {1418-1424},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710722.pdf},
abstract = {Freezing of Gait (FOG) is one of the most debilitating- imitating symptoms of Parkinson?s Disease (PD), often resulting in falls, impaired mobility, and loss of in- dependence. This The paper presents a comprehensive and wearable assistive system designed to detect, predict, and mitigate FOG episodes in real time. The proposed device integrates multiple sensing and feedback modalities, including inertial measurement units (IMUs), surface electromyography (sEMG), dynamic visual cue- ing via laser projection, and haptic feedback through vibratory actuators. The system leverages a hybrid Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) model to recognize gait phases and detect movement intentions, achieving a classification accuracy of 90 percent. Simultaneously, a Random Forest classifier is trained on real-time sEMG signals to monitor dorsi flexor and plantar flexor activity, providing biomechanical insight into muscular performance. Based on this muscular feedback, the system adapts its visual and haptic cues dynamically to guide patients toward optimal step initiation and foot orientation. Visual cues?projected via a wearable laser?indicate the ideal foot placement trajectory, while vibratory feedback enhances proprioceptive awareness of foot movement, particularly aiding dorsi flexion. The device supports both indoor and treadmill- based rehabilitation, offers- In terms of flexibility for clinical deployment and home-based therapy.},
keywords = {Freezing of Gait (FOG), Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM), Inertial Measurement Units (Imus), Surface Electromyography (SEMG), Dynamic Visual Cueing},
month = {September},
doi = {https://doi.org/10.64388/IREV9I3-1710722-8296}
}