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.

Research on human motor adaptation has often focused on how people adapt to self-generated or externally-influenced errors. Trial-by-trial adaptation is a person’s response to self-generated errors. Externally-influenced errors applied as catch-trial perturbations are used to calculate a person’s perturbation adaptation rate. Although these adaptation rates are sometimes compared to one another, we show through simulation and empirical data that the two metrics are distinct. We demonstrate that the trial-by-trial adaptation rate, often calculated as a coefficient in a linear regression, is biased under typical conditions. We tested 12 able-bodied subjects moving a cursor on a screen using a computer mouse. Statistically different adaptation rates arise when sub-sets of trials from different phases of learning are analyzed from within a sequence of movement results. We propose a new approach to identify when a person’s learning has stabilized in order to identify steady-state movement trials from which to calculate a more reliable trial-by-trial adaptation rate. Using a Bayesian model of human movement, we show that this analysis approach is more consistent and provides a more confident estimate than alternative approaches. Constraining analyses to steady-state conditions will allow researchers to better decouple the multiple concurrent learning processes that occur while a person makes goal-directed movements. Streamlining this analysis may help broaden the impact of motor adaptation studies, perhaps even enhancing their clinical usefulness.

A variety of factors affect the performance of a person using a myoelectric prosthesis, including increased control noise, reduced sensory feedback, and muscle fatigue. Many studies use able-bodied subjects to control a myoelectric prosthesis using a bypass socket in order to make comparisons to movements made with intact limbs. Depending on the goals of the study, this approach can also allow for greater subject numbers and more statistical power in the analysis of the results. As we develop assessment tools and techniques to evaluate how peripheral nerve interfaces impact prosthesis incorporation, involving normally limbed subjects in the studies becomes challenging. We have designed a novel bypass prosthesis to allow for the assessment of prosthesis incorporation in able-bodied subjects. Incorporation of a prosthetic hand worn by a normally limbed subject requires that the prosthesis is a convincing, functional extension of their own body. We present the design and development of the bypass prosthesis with special attention to mounting position and angle of the prosthetic hand, the quality of the control system and the responsiveness of the feedback. The bypass prosthesis has been fitted with a myoelectrically-controlled hand that has been instrumented to measure the forces applied to the thumb, index, and middle fingers. The prosthetic hand was mounted on the bypass socket such that it is the same length as the subject's intact limb but at a medial rotation angle of 20° to prevent visual occlusion of the prosthetic hand. Force feedback is provided in the form of electrical stimulation, vibration, or force applied to the intact limb with milliseconds of delay. Preliminary data results from a cross-modal congruency task are included showing evidence of prosthesis incorporation in able-bodied subjects. This bypass will allow able-bodied subjects to participate in research studies that require the use of a prosthetic limb while also allowing the subjects to sense that the prosthesis is an extension of the body.

No abstract

Advanced neural interfaces show promise in making prosthetic limbs more biomimetic and ultimately more intuitive and useful for patients. However, approaches to assess these emerging technologies are limited in scope and the insight they provide. When outfitting a prosthesis with a feedback system, such as a peripheral nerve interface, it would be helpful to quantify its physiological correspondence, i.e. how well the prosthesis feedback mimics the perceived feedback in an intact limb. Here we present an approach to quantify this aspect of feedback quality using the crossmodal congruency effect (CCE) task. We show that CCE scores are sensitive to feedback modality, an important characteristic for assessment purposes, but are confounded by the spatial separation between the expected and perceived location of a stimulus. Using data collected from 60 able-bodied participants trained to control a bypass prosthesis, we present a model that results in adjusted-CCE scores that are unaffected by percept misalignment which may result from imprecise neural stimulation. The adjusted-CCE score serves as a proxy for a feedback modality’s physiological correspondence or ‘naturalness’. This quantification approach gives researchers a tool to assess an aspect of emerging augmented feedback systems that is not measurable with current motor assessments.