Prosthetic Hand
Prosthetic hand research aims to create devices that restore hand functionality and improve users' quality of life, moving beyond simplistic designs to achieve more intuitive and dexterous control. Current efforts focus on developing advanced control systems using machine learning algorithms like recurrent neural networks (RNNs), transformers, and attractor-based networks to decode electromyographic (EMG) signals or nerve interfaces, often incorporating sensory feedback (haptic or vibrotactile) for improved control and embodiment. These advancements, including sensors-free approaches and more efficient hardware implementations, are leading to more accurate, intuitive, and user-friendly prosthetic hands with improved task performance and reduced user effort.
Papers
Beyond Humanoid Prosthetic Hands: Modular Terminal Devices That Improve User Performance
Digby Chappell, Barry Mulvey, Shehara Perera, Fernando Bello, Petar Kormushev, Nicolas Rojas
Examining the physical and psychological effects of combining multimodal feedback with continuous control in prosthetic hands
Digby Chappell, Zeyu Yang, Angus B. Clark, Alexandre Berkovic, Colin Laganier, Weston Baxter, Fernando Bello, Petar Kormushev, Nicolas Rojas