研究目的
Developing a customized augmented reality system for stroke rehabilitation by integrating an interactive serious game interface with a hand exoskeleton device to improve therapy for people with neurological impairments.
研究成果
The developed system successfully integrates a low-cost, 3D printed hand exoskeleton with a game-based interface using Kinect and EMG sensors, providing effective rehabilitation training. It enhances patient motivation and allows for progress monitoring. Future work should extend trials to stroke patients and integrate with conventional therapy protocols.
研究不足
The system was tested only on healthy participants; validation with stroke patients is needed. The exoskeleton design may have constraints in fitting all hand sizes perfectly, and EMG signals are subject-dependent and sensitive to external factors like electrode placement.
1:Experimental Design and Method Selection:
The methodology involves designing a hand exoskeleton using 3D printing technology, integrating it with a Kinect sensor for skeletal tracking and an EMG sensor for intention detection, and developing a LabVIEW-based interactive virtual environment as a serious game for rehabilitation. A fuzzy logic controller is implemented for EMG signal processing and exoskeleton control.
2:Sample Selection and Data Sources:
Healthy participants were used in experiments to validate the system, with EMG signals acquired from the Flexor Digitorum Superficialis muscle using surface electrodes.
3:List of Experimental Equipment and Materials:
Includes a 3D printed hand exoskeleton, Kinect sensor, EMG electrodes, Arduino Mega 2560 board, sg90 micro servomotors, E-health shield, Xbee shield, and LabVIEW software.
4:Experimental Procedures and Operational Workflow:
Participants use the exoskeleton and Kinect to interact with a virtual game, where hand movements control a 3D object to grasp targets. EMG signals are processed to detect grasping intentions, and data on performance (e.g., score, ROM) are recorded and stored.
5:Data Analysis Methods:
EMG signals are filtered and features (MAV and RMS) are extracted for fuzzy logic control. Data is stored in a database and analyzed using SQL queries and LabVIEW toolkits for reporting progress.
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