The journey of a prosthetic user is characterized by the opportunities and the limitations of a
device that should enable activities of daily living (ADL). In particular, experiencing a bionic …

Machine Learning models are being deployed as parts of real-world systems with the
upsurge of interest in artificial intelligence. The design, implementation, and maintenance of …

Loss of tactile sensations is a major roadblock preventing upper limb-absent people from
multitasking or using the full dexterity of their prosthetic hands. With current myoelectric …

Objective: The next generation prosthetic hand that moves and feels like a real hand
requires a robust neural interconnection between the human minds and machines. Methods …

Motion intent recognition for controlling prosthetic systems has long relied on machine
learning algorithms. Artificial neural networks have shown great promise for solving such …

Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising
avenue for developing intelligent prostheses for upper limb amputees. However, the …

Stability and repeatability of Pattern Recognition (PR) myoelectric control for upper limb
prosthetic devices remain unresolved challenges in multi-DoFs systems. In this study, we …

Objective. Functional electrical stimulation (FES) involves artificial activation of skeletal
muscles to reinstate motor function in paralyzed individuals. While FES applied to the upper …

Achieving instinctive multi-grasp control of prosthetic hands typically still requires a large
number of sensors, such as electromyography (EMG) electrodes mounted on a residual …

Background Closing the control loop between users and their prostheses by providing
artificial sensory feedback is a fundamental step toward the full restoration of lost sensory …