Intent Prediction Based on Biomechanical Coordination of EMG and Vision-Filtered Gaze for End-Point Control of an Arm Prosthesis.

We propose a novel controller for powered prosthetic arms, where fused EMG and gaze data predict the desired end-point for a full arm prosthesis, which could drive the forward motion of individual joints. We recorded EMG, gaze, and motion-tracking during pick-and-place trials with 7 able-bodied subjects. Subjects positioned an object above a random target on a virtual interface, each completing around 600 trials.

A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices.

Teleception is defined as sensing that occurs remotely, with no physical contact with the object being sensed. To emulate innate control systems of the human body, a control system for a semi- or fully autonomous assistive device not only requires feedforward models of desired movement, but also the environmental or contextual awareness that could be provided by teleception. Several recent publications present teleception modalities integrated into control systems and provide preliminary results, for example, for performing hand grasp prediction or endpoint control of an arm assistive device; and gait segmentation, forward prediction of desired locomotion mode, and activity-specific control of a prosthetic leg or exoskeleton.

Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices.

Significant research effort has gone towards the development of powered lower limb prostheses that control power during gait. These devices use forward prediction based on electromyography (EMG), kinetics and kinematics to command the prosthesis which locomotion activity is desired. Unfortunately these predictions can have substantial errors, which can potentially lead to trips or falls.

Decoding the grasping intention from electromyography during reaching motions.

Background: Active upper-limb prostheses are used to restore important hand functionalities, such as grasping. In conventional approaches, a pattern recognition system is trained over a number of static grasping gestures. However, training a classifier in a static position results in lower classification accuracy when performing dynamic motions, such as reach-to-grasp.

Depth Sensing for Improved Control of Lower Limb Prostheses.

Powered lower limb prostheses have potential to improve the quality of life of individuals with amputations by enabling all daily activities. However, seamless ambulation mode recognition is necessary to achieve this goal and is not yet a clinical reality. Current intent recognition systems use mechanical and EMG sensors to estimate prosthesis and user status.

Design and Fabrication of a Six Degree-of-Freedom Open Source Hand.

Currently, most externally powered prostheses are controlled using electromyography (or EMG), which is the measure of the electrical signals that are produced when voluntary muscle is contracted. One of the major problems is that there are a limited number of muscular control sites that can be used, which limits the complexity of the hands that are controllable. Many upper-limb prosthetics researchers are searching for methods to simply and effectively control complex prosthetic hands, and a significant number of these researchers have utilized virtual hands and other simulations to perform testing of these control algorithms (oftentimes on able bodied subjects).