Human Robot Interface for Assistive Grasping
This work addresses the need for effective assistive grasping interfaces for individuals with physical disabilities, but it is incremental as it builds on existing human-in-the-loop systems by introducing a new evaluation benchmark.
The study tackled the problem of evaluating input devices for human-in-the-loop assistive grasping systems by testing four devices (mouse, speech recognition, assistive switch, and a novel sEMG device) and creating a generalized benchmark for assessment, with preliminary results showing the potential of sEMG for severely disabled individuals.
This work describes a new human-in-the-loop (HitL) assistive grasping system for individuals with varying levels of physical capabilities. We investigated the feasibility of using four potential input devices with our assistive grasping system interface, using able-bodied individuals to define a set of quantitative metrics that could be used to assess an assistive grasping system. We then took these measurements and created a generalized benchmark for evaluating the effectiveness of any arbitrary input device into a HitL grasping system. The four input devices were a mouse, a speech recognition device, an assistive switch, and a novel sEMG device developed by our group that was connected either to the forearm or behind the ear of the subject. These preliminary results provide insight into how different interface devices perform for generalized assistive grasping tasks and also highlight the potential of sEMG based control for severely disabled individuals.