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During every waking moment, we must engage with our environments, the people around us, the tools we use, and even our own bodies to perform actions and achieve our intentions. There is a spectrum of control that we have over our surroundings that spans the extremes from full to negligible. When the outcomes of our actions do not align with our goals, we have a tremendous capacity to displace blame and frustration on external factors while forgiving ourselves. This is especially true when we cooperate with machines; they are rarely afforded the level of forgiveness we provide our bodies and often bear much of our blame. Yet, our brain readily engages with autonomous processes in controlling our bodies to coordinate complex patterns of muscle contractions, make postural adjustments, adapt to external perturbations, among many others. This acceptance of biological autonomy may provide avenues to promote more forgiving human-machine partnerships. In this perspectives paper, we argue that striving for machine embodiment is a pathway to achieving effective and forgiving human-machine relationships. We discuss the mechanisms that help us identify ourselves and our bodies as separate from our environments and we describe their roles in achieving embodied cooperation. Using a representative selection of examples in neurally interfaced prosthetic limbs and intelligent mechatronics, we describe techniques to engage these same mechanisms when designing autonomous systems and their potential bidirectional interfaces.

Cutaneous sensation is vital to controlling our hands and upper limbs. It helps close the motor control loop by informing adjustments of grasping forces during object manipulations and provides much of the information the brain requires to perceive our limbs as a part of our bodies. This sensory information is absent to upper-limb prosthesis users. Although robotic prostheses are becoming increasingly sophisticated, the absence of feedback imposes a reliance on open-loop control and limits the functional potential as an integrated part of the body. Experimental systems to restore physiologically relevant sensory information to prosthesis users are beginning to emerge. However, the impact of their long-term use on functional abilities, body image, and neural adaptation processes remains unclear. Understanding these effects is essential to transition sensate prostheses from sophisticated assistive tools to integrated replacement limbs. We recruited three participants with high-level upper-limb amputation who previously received targeted reinnervation surgery. Each participant was fit with a neural-machine-interface prosthesis that allowed participants to operate their device by thinking about moving their missing limb. Additionally, we fit a sensory feedback system that allowed participants to experience touch to the prosthesis as touch on their missing limb. All three participants performed a long-term take-home trial. Two participants used their neural-machine-interface systems with touch feedback and one control participant used his prescribed, insensate prosthesis. A series of functional outcome metrics and psychophysical evaluations were performed using sensate neural-machine-interface prostheses before and after the take-home period to capture changes in functional abilities, limb embodiment, and neural adaptation. Our results demonstrated that the relationship between users and sensate neural-machine-interface prostheses is dynamic and changes with long-term use. The presence of touch sensation had a near-immediate impact on how the users operated their prostheses. In the multiple independent measures of users' functional abilities employed, we observed a spectrum of performance changes following long-term use. Furthermore, after the take-home period, participants more appropriately integrated their prostheses into their body images and psychophysical tests provided strong evidence that neural and cortical adaptation occurred.

Object stiffness discrimination is fundamental to shaping the way we interact with our environment. Investigating the sensorimotor mechanisms underpinning stiffness discrimination may help further our understanding of healthy and sensory-impaired upper limb function. We developed a metric that leverages sensory discrimination techniques and a foraging-based analysis to characterize participant accuracy and discrimination processes of sensorimotor control. Our metric required searching and discriminating two variants of test-object: rubber blocks and spring cells, which emphasized cutaneous-force and proprioceptive feedback, respectively. We measured the number of test-objects handled, selection accuracy, and foraging duration. These values were used to derive six indicators of performance. We observed higher discrimination accuracies, with quicker search and handling durations, for blocks compared to spring cells. Correlative analyses of accuracy, error rates, and foraging times suggested that the block and spring variants were, in fact, unique sensory tasks. These results provide evidence that our metric is sensitive to the contributions of sensory feedback, motor control, and task performance strategy, and will likely be effective in further characterizing the impact of sensory feedback on motor control in healthy and sensory-impaired populations.

ObjectiveRecent advances in neural engineering have restored mobility to people with paralysis, relieved symptoms of movement disorders, reduced chronic pain, restored the sense of hearing, and provided sensory perception to individuals with sensory deficits.ApproachThis progress was enabled by the team-based, interdisciplinary approaches used by neural engineers. Neural engineers have advanced clinical frontiers by leveraging tools and discoveries in quantitative and biological sciences and through collaborations between engineering, science, and medicine. The movement toward bioelectronic medicines, where neuromodulation aims to supplement or replace pharmaceuticals to treat chronic medical conditions such as high blood pressure, diabetes and psychiatric disorders is a prime example of a new frontier made possible by neural engineering. Although one of the major goals in neural engineering is to develop technology for clinical applications, this technology may also offer unique opportunities to gain insight into how biological systems operate.Main resultsDespite significant technological progress, a number of ethical and strategic questions remain unexplored. Addressing these questions will accelerate technology development to address unmet needs. The future of these devices extends far beyond treatment of neurological impairments, including potential human augmentation applications. Our task, as neural engineers, is to push technology forward at the intersection of disciplines, while responsibly considering the readiness to transition this technology outside of the laboratory to consumer products.SignificanceThis article aims to highlight the current state of the neural engineering field, its links with other engineering and science disciplines, and the challenges and opportunities ahead. The goal of this article is to foster new ideas for innovative applications in neurotechnology.

BackgroundIndices of inter-evaluator reliability are used in many fields such as computational linguistics, psychology, and medical science; however, the interpretation of resulting values and determination of appropriate thresholds lack context and are often guided only by arbitrary "rules of thumb" or simply not addressed at all. Our goal for this work was to develop a method for determining the relationship between inter-evaluator agreement and error to facilitate meaningful interpretation of values, thresholds, and reliability.MethodsThree expert human evaluators completed a video analysis task, and averaged their results together to create a reference dataset of 300 time measurements. We simulated unique combinations of systematic error and random error onto the reference dataset to generate 4900 new hypothetical evaluators (each with 300 time measurements). The systematic errors and random errors made by the hypothetical evaluator population were approximated as the mean and variance of a normally-distributed error signal. Calculating the error (using percent error) and inter-evaluator agreement (using Krippendorff's alpha) between each hypothetical evaluator and the reference dataset allowed us to establish a mathematical model and value envelope of the worst possible percent error for any given amount of agreement.ResultsWe used the relationship between inter-evaluator agreement and error to make an informed judgment of an acceptable threshold for Krippendorff's alpha within the context of our specific test. To demonstrate the utility of our modeling approach, we calculated the percent error and Krippendorff's alpha between the reference dataset and a new cohort of trained human evaluators and used our contextually-derived Krippendorff's alpha threshold as a gauge of evaluator quality. Although all evaluators had relatively high agreement (> 0.9) compared to the rule of thumb (0.8), our agreement threshold permitted evaluators with low error, while rejecting one evaluator with relatively high error.ConclusionsWe found that our approach established threshold values of reliability, within the context of our evaluation criteria, that were far less permissive than the typically accepted "rule of thumb" cutoff for Krippendorff's alpha. This procedure provides a less arbitrary method for determining a reliability threshold and can be tailored to work within the context of any reliability index.

To effortlessly complete an intentional movement, the brain needs feedback from the body regarding the movement's progress. This largely nonconscious 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. 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.

Prosthetic elevated vacuum is a suspension method used to reduce daily volume changes of the residual limb. Evaluation of the effectiveness of these systems is limited due to a lack of correlation to actual socket air pressure, particularly during unconstrained movements. This may explain some of the variability in functional outcomes reported in the literature. Our objective was to develop a light-weight portable socket measurement system to quantify internal socket air pressure, temperature, and acceleration; and to present preliminary results from implementation with three transtibial prosthesis users with mechanical elevated vacuum pumps. Participants completed five functional tasks with and without the vacuum pumps actively connected, including the 2-Minute Walk test, 5-Times Sit-to-Stand test, 4-Square Step test, L-Test, and Figure-8 test. Results demonstrated different gait profiles and pressure ranges for each user. Two of the participants demonstrated substantially lower air pressure (higher vacuum) over time while the pump was active compared to inactive. The minimum air pressure measured for all participants was -34.6 ± 7.7 kPa. One participant did not show substantial changes in pressure over time for either pump condition. Functional task performance was not significantly different between pump conditions. Correlation with accelerometer readings indicated peak positive pressures occurred just following initial contact of the foot in early stance, and the most negative pressures (highest vacuum) were observed throughout swing. This study has demonstrated the use of a portable data logging tool that may serve the clinical and research communities to quantify the operation of elevated vacuum systems, and better understand the variability of mechanical pump operation and overall system performance.

One of the most important factors in successful upper limb prostheses is the socket design. Sockets must be individually fabricated to arrive at a geometry that suits the user's morphology and appropriately distributes the pressures associated with prosthetic use across the residual limb. In higher levels of amputation, such as transhumeral, this challenge is amplified as prosthetic weight and the physical demands placed on the residual limb are heightened. Yet, in the upper limb, socket fabrication is largely driven by heuristic practices. An analytical understanding of the interactions between the socket and residual limb is absent in literature. This work describes techniques, adapted from lower limb prosthetic research, to empirically characterize the pressure distribution occurring between the residual limb and well-fit transhumeral prosthetic sockets. A case series analyzing the result of four participants with transhumeral amputation is presented. A Tekscan VersaTek pressure measurement system and FaroArm Edge coordinate measurement machine were employed to capture socket-residual limb interface pressures and geometrically register these values to the anatomy of participants. Participants performed two static poses with their prosthesis under two separate loading conditions. Surface pressure maps were constructed from the data, highlighting pressure distribution patterns, anatomical locations bearing maximum pressure, and the relative pressure magnitudes. Pressure distribution patterns demonstrated unique characteristics across the four participants that could be traced to individual socket design considerations. This work presents a technique that implements commercially available tools to quantitatively characterize upper limb socket-residual limb interactions. This is a fundamental first step toward improved socket designs developed through informed, analytically-based design tools.

In this paper, we discuss the potential contribution of affective touch to the user experience and robot performance in human-robot interaction, with an in-depth look into upper-limb prosthesis use as a well-suited example. Research on providing haptic feedback in human-robot interaction has worked to relay discriminative information during functional activities of daily living, like grasping a cup of tea. However, this approach neglects to recognize the affective information our bodies give and receive during social activities of daily living, like shaking hands. The discussion covers the emotional dimensions of affective touch and its role in conveying distinct emotions. In this work, we provide a human needs-centered approach to human-robot interaction design and argue for an equal emphasis to be placed on providing affective haptic feedback channels to meet the social tactile needs and interactions of human agents. We suggest incorporating affective touch to enhance user experience when interacting with and through semi-autonomous systems such as prosthetic limbs, particularly in fostering trust. Real-time analysis of trust as a dynamic phenomenon can pave the way towards adaptive shared autonomy strategies and consequently enhance the acceptance of prosthetic limbs. Here we highlight certain feasibility considerations, emphasizing practical designs and multi-sensory approaches for the effective implementation of affective touch interfaces. [ABSTRACT FROM AUTHOR]

Force Sensitive Resistors (FSRs) are commercially available thin film polymer sensors commonly employed in a multitude of biomechanical measurement environments. Reasons for such wide spread usage lie in the versatility, small profile, and low cost of these sensors. Yet FSRs have limitations. It is commonly accepted that temperature, curvature and biological tissue compliance may impact sensor conductance and resulting force readings. The effect of these variables and degree to which they interact has yet to be comprehensively investigated and quantified. This work systematically assesses varying levels of temperature, sensor curvature and surface compliance using a full factorial design-of-experiments approach. Three models of Interlink FSRs were evaluated. Calibration equations under 12 unique combinations of temperature, curvature and compliance were determined for each sensor. Root mean squared error, mean absolute error, and maximum error were quantified as measures of the impact these thermo/mechanical factors have on sensor performance. It was found that all three variables have the potential to affect FSR calibration curves. The FSR model and corresponding sensor geometry are sensitive to these three mechanical factors at varying levels. Experimental results suggest that reducing sensor error requires calibration of each sensor in an environment as close to its intended use as possible and if multiple FSRs are used in a system, they must be calibrated independently.

The generation of an internal body model and its continuous update is essential in sensorimotor control. Although known to rely on proprioceptive sensory feedback, the underlying mechanism that transforms this sensory feedback into a dynamic body percept remains poorly understood. However, advances in the development of genetic tools for proprioceptive circuit elements, including the sensory receptors, are beginning to offer new and unprecedented leverage to dissect the central pathways responsible for proprioceptive encoding. Simultaneously, new data derived through emerging bionic neural machine–interface technologies reveal clues regarding the relative importance of kinesthetic sensory feedback and insights into the functional proprioceptive substrates that underlie natural motor behaviors. [ABSTRACT FROM AUTHOR]