A perceptual illusion provides the sensation of complex bionic hand movements to human amputees, allowing real-time movement control without the necessity of vision.

Purpose: – This paper aims to evaluate the material properties and dimensional accuracy of a MakerBot Replicator 2 desktop 3D printer. Design/methodology/approach: – A design of experiments (DOE) test protocol was applied to determine the effect of the following variables on the material properties of 3D printed part: layer height, per cent infill and print orientation using a MakerBot Replicator 2 printer. Classical laminate plate theory was used to compare results from the DOE experiments with theoretically predicted elastic moduli for the tensile samples. Dimensional accuracy of test samples was also investigated. Findings: – DOE results suggest that per cent infill has a significant effect on the longitudinal elastic modulus and ultimate strength of the test specimens, whereas print orientation and layer thickness fail to achieve significance. Dimensional analysis of test specimens shows that the test specimen varied significantly (p < 0.05) from the nominal print dimensions. Practical implications: – Although desktop 3D printers are an attractive manufacturing option to quickly produce functional components, this study suggests that users must be aware of this manufacturing process’ inherent limitations, especially for components requiring high geometric tolerance or specific material properties. Therefore, higher quality 3D printers and more detailed investigation into the MakerBot MakerWare printing settings are recommended if consistent material properties or geometries are required. Originality/value: – Three-dimensional (3D) printing is a rapidly expanding manufacturing method. Initially, 3D printing was used for prototyping, but now this method is being used to create functional final products. In recent years, desktop 3D printers have become commercially available to academics and hobbyists as a means of rapid component manufacturing. Although these desktop printers are able to facilitate reduced manufacturing times, material costs and labor costs, relatively little literature exists to quantify the physical properties of the printed material as well as the dimensional consistency of the printing processes.

We present a case study of a novel variation of the targeted sensory reinnervation technique that provides additional control over sensory restoration after transhumeral amputation. The use of intraoperative somatosensory evoked potentials on individual fascicles of the median and ulnar nerves allowed us to specifically target sensory fascicles to reroute to target cutaneous nerves at a distance away from anticipated motor sites in a transhumeral amputee. This resulted in restored hand maps of the median and ulnar nerve in discrete spatially separated areas. In addition, the subject was able to use native and reinnervated muscle sites to control a robotic arm while simultaneously sensing touch and force feedback from the robotic gripper in a physiologically correct manner. This proof of principle study is the first to demonstrate the ability to have simultaneous dual flow of information (motor and sensory) within the residual limb. In working towards clinical deployment of a sensory integrated prosthetic device, this surgical method addresses the important issue of restoring a usable access point to provide natural hand sensation after upper limb amputation.

As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis.

Predicting the future has long been regarded as a powerful means to improvement and success. The ability to make accurate and timely predictions enhances our ability to control our situation and our environment. Assistive robotics is one prominent area in which foresight of this kind can bring improved quality of life. In this article, we present a new approach to acquiring and maintaining predictive knowledge during the online ongoing operation of an assistive robot. The ability to learn accurate, temporally abstracted predictions is shown through two case studies: 1) able-bodied myoelectric control of a robot arm and 2) an amputee's interactions with a myoelectric training robot. To our knowledge, this research is the first demonstration of a practical method for real-time prediction learning during myoelectric control. Our approach therefore represents a fundamental tool for addressing one major unsolved problem: amputee-specific adaptation during the ongoing operation of a prosthetic device. The findings in this article also contribute a first explicit look at prediction learning in prosthetics as an important goal in its own right, independent of its intended use within a specific controller or system. Our results suggest that real-time learning of predictions and anticipations is a significant step toward more intuitive myoelectric prostheses and other assistive robotic devices.