Akademik Veri Yönetim Sistemi
32nd IEEE Signal Processing and Communications Applications Conference (SIU), Mersin, Türkiye, 15 - 18 Mayıs 2024
This study is part of a multi-layered telerehabilitation project that requires collaboration across various areas of expertise. Duchenne Muscular Dystrophy (DMD) patients experience intense muscle atrophies and weaknesses, hindering their ability to perform daily movements. As a component of a priority field project focused on remote treatment and follow-up, a wearable robotic exoskeleton was developed to assist patients with their movements. This design utilizes Electromyography (EMG) signals, supported by artificial intelligence, to control movement, addressing the significant need for physical therapy in this disease. The objective is to predict user movements and provide assistance when necessary, during repetitive exercises in the physical therapy process. The design incorporates accelerometers and force sensors to gather movement data, allowing for evaluation of movement estimation using EMG-based Support Vector Machine (SVM) and exercise performance. Data on muscle activity, position, force, movement patterns, and quality evaluations are accessible to both patients and physiotherapists via servers. Additionally, the design aims to personalize treatment routines by comparing patient movements with predetermined movement templates set by physiotherapists, providing performance tracking and feedback. To facilitate this, the exoskeleton's movement information is transmitted bidirectionally via a wireless connection (Wi-Fi) to servers.