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About

Deepak Joshi is a leading researcher in neural engineering and human-machine interaction, whose work bridges the gap between biological control and artificial systems. His primary research areas include myoelectric control, robotic manipulation, and sensory feedback for prosthetics. Joshi’s major contributions are centered on restoring natural touch and proprioception to amputees. Notably, his 2014 study on an “Illusory Sense of Human Touch From a Warm and Soft Artificial Hand” (47 citations) demonstrated how thermal and soft materials can evoke a realistic sense of touch, addressing the psychosocial stigma of limb loss. In another highly cited work (58 citations), he developed “Artificial Proprioceptive Feedback for Myoelectric Control,” tackling the critical problem of absent muscle signals by replacing visual feedback with intuitive sensory cues. His research also extends to intelligent control systems, as seen in his 2019 paper on “Switching-based Collaborative Fractional Order Fuzzy Logic Controllers for Robotic Manipulators” (66 citations), which advanced adaptive control for robotic arms. More recently, his 2022 work on an “Attention-based Deep CNN-BiLSTM Model” for forecasting muscle fatigue (6 citations) promises to enhance the adaptive control of assistive devices like exoskeletons and neuroprostheses. With over 170 cumulative citations, Joshi’s interdisciplinary work is shaping the future of bionic limbs and human-robot collaboration.