No abstract

To effortlessly complete an intentional movement, the brain needs feedback from the body regarding the movement’s progress. This largely non-conscious kinesthetic sense helps the brain to learn relationships between motor commands and outcomes to correct movement errors. Prosthetic systems for restoring function have predominantly focused on controlling motorized joint movement. Without the kinesthetic sense, however, these devices do not become intuitively controllable. Here we report a method for endowing human amputees with a kinesthetic perception of dexterous robotic hands. Vibrating the muscles used for prosthetic control via a neural-machine interface produced the illusory perception of complex grip movements. Within minutes, three amputees integrated this kinesthetic feedback and improved movement control. Combining intent, kinesthesia, and vision instilled participants with a sense of agency over the robotic movements. This feedback approach for closed-loop control opens a pathway to seamless integration of minds and machines.

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.

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.

Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses.

No abstract

The aim of this paper was to demonstrate the functionality of an inexpensive mechanotactile sensory feedback system for transhumeral myoelectric prostheses. We summarize the development of a tactile-integrated prosthesis, including 1) evaluation of sensors that were retrofit onto existing commercial terminal devices; 2) design of two custom mechanotactile tactors that were integrated into a socket without compromising suction suspension; 3) design of a modular controller which translated sensor input to tactor output, was wirelessly adjusted, and fit within a prosthetic forearm; and 4) evaluation of the system with a single transhumeral participant. Prosthesis functionality was demonstrated over three test sessions; the participant was able to identify tactor stimulation location and demonstrated a reduction in grasp force with the mechanotactile stimulation. This system offers an inexpensive and modular solution for integration of a mechanotactile sensory feedback system into a prosthetic socket without compromising the suction seal. These principles can be applied in future studies to investigate the direct impact of sensory feedback on tangible outcomes for prosthetic users, thereby reducing barriers to clinical translation.