Motor Impairment

Motor impairment research focuses on understanding and mitigating movement difficulties stemming from various neurological conditions. Current efforts leverage machine learning, particularly deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs, such as LSTMs), to analyze diverse data sources including wearable sensor data, electromyography (EMG), electroencephalography (EEG), and imaging, aiming for improved diagnosis, personalized treatment, and assistive technology development. This work holds significant implications for enhancing the lives of individuals with motor impairments through improved diagnostic tools, more effective therapies, and the creation of intuitive, accessible assistive technologies.