Robotic thumb grasp-based range of motion optimisation.

With the thumb serving an important role in the function of the human hand, improving robotic prosthetic thumb functionality will have a direct impact on the prosthesis itself. So far, no significant work exists that examines the ranges of motion a prosthetic thumb should exhibit; many myoelectric prostheses arbitrarily select them. We question this design practice as we expect a significant functional volume reduction for performing certain activities vs. the maximum obtainable workspace. To this end, we compare and contrast four anatomically-accurate thumb models. We quantify their angular ranges of motion by generating point clouds of end-effector positions, and by computing their alpha-shape bounded volumes. Examining the function of the thumb for several grasps, we identify a 76% reduction of the required workspace volume vis-a-vis the maximum volume of a "'generic'" human thumb.

Modeling the frictional interaction in the tendon-pulley system of the human finger for use in robotics.

Physiological studies of the human finger indicate that friction in the tendon-pulley system accounts for a considerable fraction of the total output force (9-12%) in a high-load static posteccentric configuration. Such a phenomenon can be exploited for robotic and prosthetic applications, as it can result in (1) an increase of output force or (2) a reduction of energy consumption and actuator weight. In this study, a simple frictional, two-link, one-degree-of-freedom model of a human finger was created. The model is validated against in vitro human finger data, and its behavior is examined with respect to select physiological parameters. The results point to clear benefits of incorporating friction in tendon-driven robotic fingers for actuator mass and output force. If it is indeed the case that the majority of high-load hand grasps are posteccentric, there is a clear benefit of incorporating friction in tendon-driven prosthetic hand replacements.

Gesture recognition in upper-limb prosthetics: a viability study using dynamic time warping and gyroscopes.

One of the significant challenges in the upper-limb-prosthetics research field is to identify appropriate interfaces that utilize the full potential of current state-of-the-art neuroprostheses. As the new generation of such prostheses paces towards approximating the human physiological performance in terms of movement dexterity and sensory feedback, it is clear that current non-invasive interfaces are still severely limited. Surface electromyography, the interface ubiquitously used in the field, is riddled with several shortcomings. Gesture recognition, an interface pervasively used in wearables and mobile devices, shows a strong potential as a non-invasive upper-limb prosthetic interface. This study aims at showcasing its potential in the field by using gyroscope sensors. To this end, we (1) explore the viability of Dynamic Time Warping as a classification method for upper-limb prosthetics and (2) look for appropriate sensor locations on the body. Results indicate an optimal classification rate of 97.53%, σ = 8.74 using a sensor located proximal to the endpoint performing a gesture.