Rehabilitative Assessment Approaches for Disabilities

Dear Colleagues,

Rehabilitative assessment strategies rely on rigorous analysis of a diverse range of measurements by applying advanced signal processing and machine learning techniques as well as sensor network approaches. Such data can relate to gait (captured by accelerometers or video cameras), speech, the brain through electroencephalography (EEG), muscle functions through electromyography (EMG), and many other modalities, which can be single- or multichannel. Tremendous advances have been made in both signal processing and machine learning, particularly in data-driven techniques for this purpose. The latter methods are mainly based on deep learning (DL), as it is a powerful tool that has developed in artificial intelligence (AI) in recent years. On the other hand, large amounts of data—principally images, including medical images; text; speech; and multimodal biomedical data—are needed to train these systems, many of which are based on deep neural networks (DNNs). Scarcity of patient data is the major barrier to optimally using the power of machine learning, particularly data-driven and deep learning-based systems, for feature estimation, learning, clustering, and classification. Therefore, generating suitable biomedical data which can be combined with real data to identify patient state is another agenda of this Special Issue. Hence, the aim of this Special Issue is to attract research on state-of-the-art techniques in signal processing, machine learning, and sensor networking for analysis of rehabilitative assessment data captured through various possible recording modalities and their corresponding surrogates.

• Advances in rehabilitation and rehabilitative data generation, recording, and processing;
• Gait analysis from accelerometer data;
• Vision-based gait analysis;
• Post-stroke data analysis;
• Body motion analysis for rehabilitative assessment;
• Data augmentation and surrogate data generation for rehabilitative assessment;
• Advances in signal processing for rehabilitative data assessment;
• Deep learning for rehabilitative data assessment;
• Sensor networks and body sensor networks for rehabilitative data assessment;
• Multimodal biodata assessment;
• Quantification of generated surrogate data quality.

Prof. Dr. Saeid Sanei
Guest Editor

Dr. Tracey KM Lee
Guest Editor Assistant

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• biosignal processing
• deep learning
• body sensor networks
• gait
• EEG
• EMG
• machine learning
• rehabilitative assessment
• vision techniques