| Abstract: |
The neuromuscular dysfunction known as Freezing of Gait (FoG), which is more prevalent in individuals suffering from Parkinson's Disease (PD), significantly reduces the quality of life and increases their risk of falling. Wearable FoG sensing technologies provide timely biofeedback cues to assist people regain control over their gait. However, the devices being bulky, intrusive, and annoyance of current FoG detection algorithms limit their usability in real-world applications. This study proposes a more efficient approach by integrating the FoG detection fusion algorithm into a Functional Electrical Stimulation (FES) module. The design leverages features with low computational requirements and specialized hardware to minimize the use of physical space and memory. The Convolutional Neural Networks (CNN) approach with SVM output was deployed to classify FoG and non-FoG periods in real-time. Additionally, the study uses CNN algorithms in fusion with data from a triaxial accelerometer, strain sensors, and piezoelectric plantar sensors to test shank-worn FoG detection devices. The study demonstrates that electrical stimulation-based cueing strategies significantly improve gait control and mitigate FoG episodes in people with Parkinson's disease. The AiCareGaitRehabilitation system employs a multi-modal sensor fusion strategy to improve the efficacy of the FES device. Data from various sensors—such as strain sensor, 18 plantar sensors, and four quadriceps sensors—the system provides a holistic view of both pre-freezing of Gait (pre-FOG) and post-freezing of Gait (post-FOG) scenarios. This research aims to improve mobility, reduce fall risks, and eventually improve the quality of life for individuals with Parkinson's disease. © 2025 |
