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About

J. Guillermo Colli Alfaro is a researcher specializing in human-machine interaction, wearable mechatronic devices, and intelligent control systems for rehabilitation engineering. His work sits at the intersection of biomedical signal processing, machine learning, and assistive technology, with a particular focus on improving the lives of stroke survivors and individuals with motor impairments. Colli Alfaro's most significant contributions center on developing robust, user-independent hand gesture recognition systems. His 2022 paper on sensor fusion-based classification models has garnered 63 citations, establishing him as a notable voice in the field. Building on earlier foundational work from 2019 (25 citations), he has consistently advanced multimodal sensing approaches that combine EMG, EEG, and novel textile-based sensors to create more natural and intuitive control interfaces for wearable devices. His recent investigations explore the effects of image normalization on CNN-based EEG–EMG fusion and the performance of embroidered strain sensors under dynamic conditions, reflecting a commitment to pushing both algorithmic and hardware frontiers. Across his body of work, Colli Alfaro demonstrates a clear mission: bridging the gap between advanced rehabilitation technology and real-world clinical adoption, making assistive devices more accessible and effective for those who need them most.