A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details-spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

A Case Series in Position-Aware Myoelectric Prosthesis Control Using Recurrent Convolutional Neural Network Classification with Transfer Learning.

Position-aware myoelectric prosthesis controllers require long, data-intensive training routines. Transfer Learning (TL) might reduce training burden. A TL model can be pre-trained using forearm muscle signal data from many individuals to become the starting point for a new user. A recurrent convolutional neural network (RCNN)-based classifier has already been shown to benefit from TL in offline analysis (95% accuracy). The present real-time study tested whether an RCNN-based classification controller with TL (RCNN-TL) could reduce training burden, offer improved device control (per functional task performance metrics), and mitigate what is known as the "limb position effect". 27 participants without amputation were recruited. 19 participants performed wrist/hand movements across multiple limb positions, with resulting forearm muscle signal data used to pre-train RCNN-TL. 8 other participants donned a simulated prosthesis, retrained (calibrated) and tested RCNN-TL, plus trained and tested a conventional linear discriminant analysis classification controller (LDA-Baseline). Results confirmed that TL reduces user training burden. RCNN-TL yielded improved task performance durations over LDA-Baseline (in specific Grasp and Release phases), yet other metrics worsened. Overall, this work contributes training condition factors necessary for TL success, identifies metrics needed for comprehensive control analysis, and contributes insights towards improved position-aware control.

Communicative capital: a key resource for human-machine shared agency and collaborative capacity.

In this work, we present a perspective on the role machine intelligence can play in supporting human abilities. In particular, we consider research in rehabilitation technologies such as prosthetic devices, as this domain requires tight coupling between human and machine. Taking an agent-based view of such devices, we propose that human-machine collaborations have a capacity to perform tasks which is a result of the combined agency of the human and the machine. We introduce communicative capital as a resource developed by a human and a machine working together in ongoing interactions. Development of this resource enables the partnership to eventually perform tasks at a capacity greater than either individual could achieve alone. We then examine the benefits and challenges of increasing the agency of prostheses by surveying literature which demonstrates that building communicative resources enables more complex, task-directed interactions. The viewpoint developed in this article extends current thinking on how best to support the functional use of increasingly complex prostheses, and establishes insight toward creating more fruitful interactions between humans and supportive, assistive, and augmentative technologies.

What's a good prediction? Challenges in evaluating an agent's knowledge.

Constructing general knowledge by learning task-independent models of the world can help agents solve challenging problems. However, both constructing and evaluating such models remain an open challenge. The most common approaches to evaluating models is to assess their accuracy with respect to observable values. However, the prevailing reliance on estimator accuracy as a proxy for the usefulness of the knowledge has the potential to lead us astray. We demonstrate the conflict between accuracy and usefulness through a series of illustrative examples including both a thought experiment and an empirical example in Minecraft, using the General Value Function framework (GVF). Having identified challenges in assessing an agent's knowledge, we propose an alternate evaluation approach that arises naturally in the online continual learning setting: we recommend evaluation by examining internal learning processes, specifically the relevance of a GVF's features to the prediction task at hand. This paper contributes a first look into evaluation of predictions through their use, an integral component of predictive knowledge which is as of yet unexplored.

Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture.

Assessing gaze behavior during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make gaze analysis difficult. Current approaches involve laborious coding of pupil positions. In settings where motion capture and mobile eye tracking are used concurrently in naturalistic tasks, it is critical that data collection be simple, efficient, and systematic. One solution is to combine eye tracking with motion capture to generate 3D gaze vectors. When combined with tracked or known object locations, 3D gaze vector generation can be automated. Here we use combined eye and motion capture and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three pupil data inputs: the efficacy of calibration routines was assessed, a validation task requiring short fixations on task-relevant locations, and a naturalistic object interaction task to bridge the gap between laboratory and "in the wild" studies. Further, we generated and compared models using spherical and Cartesian coordinate systems and monocular (left or right) or binocular data. All calibration routines performed similarly, with the best performance (i.e., sub-centimeter errors) coming from the naturalistic task trials when the participant is looking at an object in front of them. We found that spherical coordinate systems generate the most accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend 1-min calibration routines using binocular pupil data combined with a spherical world coordinate system to produce the highest-quality gaze vectors.

Machine-learned Adaptive Switching in Voluntary Lower-limb Exoskeleton Control: Preliminary Results.

Lower-limb exoskeletons utilize fixed control strategies and are not adaptable to user's intention. To this end, the goal of this study was to investigate the potential of using temporal-difference learning and general value functions for predicting the next possible walking mode that will be selected by users wearing exoskeletons in order to reduce the effort and cognitive load while switching between different modes of walking. Experiments were performed with a user wearing the Indego exoskeleton and given the authority to switch between five walking modes that were different in terms of speed and turn direction. The user's switching preferences were learned and predicted from device-centric and room-centric measurements by considering similarities in the movements being performed. A switching list was updated to show the most probable future next modes to be selected by the user. In contrast to other approaches that either can only predict a single time-step or require intensive offline training, this work used a computationally inexpensive method for learning and has the potential of providing temporally extended sets of predictions in real-time. Comparing the number of required manual switches between the machine-learned switching list and the best possible static lists showed an average decrease of 42.44% in the required switches for the machine-learned adaptive strategy. These promising results will facilitate the path for real-time application of this technique.

Composite Recurrent Convolutional Neural Networks Offer a Position-Aware Prosthesis Control Alternative While Balancing Predictive Accuracy with Training Burden.

To mitigate the "limb position effect" that hinders myoelectric upper limb prosthesis control, pattern recognition-based models must accurately predict user-intended movements across a multitude of limb positions. Such models can use electromyography (EMG) and inertial measurement units to capture necessary multi-position data. However, this data capture solution requires lengthy user-performed model training routines, with movements in many limb positions, plus retraining thereafter due to inherent signal variations over time. While a general-purpose control model (trained with a dataset that represents numerous device users) eliminates the user-training requirement altogether, it yields low movement predictive accuracy. Conversely, a user-specific control model (trained with a smaller dataset from an individual) yields high predictive accuracy, but requires retraining over time. This study capitalizes on the benefits offered by both such control options, and contributes an alternative control solution-a novel recurrent convolutional neural network (RCNN)-based Composite Model that combines the representation portion of a general-purpose model, with the decision portion of a user-specific model. The resulting Composite Model offers moderate movement predictive accuracy across various limb positions and a reduction in user training routine requirements, suggesting a new research direction to help mitigate the limb position effect along with model training burden.

Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge.

Within computational reinforcement learning, a growing body of work seeks to express an agent's knowledge of its world through large collections of predictions. While systems that encode predictions as General Value Functions (GVFs) have seen numerous developments in both theory and application, whether such approaches are explainable is unexplored. In this perspective piece, we explore GVFs as a form of explainable AI. To do so, we articulate a subjective agent-centric approach to explainability in sequential decision-making tasks. We propose that prior to explaining its decisions to others, an self-supervised agent must be able to introspectively explain decisions to itself. To clarify this point, we review prior applications of GVFs that involve human-agent collaboration. In doing so, we demonstrate that by making their subjective explanations public, predictive knowledge agents can improve the clarity of their operation in collaborative tasks.

Comparison of a Miniaturized Cassette PCR System with a Commercially Available Platform for Detecting Escherichia coli in Beef Carcass Swabs.

Detection sensitivity of cassette PCR was compared with a commercial BAX® PCR system for detection of eae and stx genes in Escherichia coli from 806 beef carcass swabs. Cassette PCR detects multiple genetic markers on multiple samples using PCR and melt curve analysis. Conventional PCR served as a gold standard. Overall, for positive and negative concordance, cassette PCR was 98.6% concordant with conventional PCR, and BAX PCR was 65.4% concordant. Of 806 beef carcass swabs, 339 by cassette PCR and 84 by BAX PCR harbored eae + stx+E. coli. For BAX PCR reactions, 84% of eae+ swabs, 79% of stx+ swabs, and 86% of eae + stx+ swabs were also detected by cassette PCR. For cassette PCR reactions, 457 swabs were eae+ with only 117 scored as eae+ using BAX PCR for 26% positive concordance. For stx primers, cassette PCR scored 480 samples as stx+ but only 215 samples were stx+ by BAX PCR, giving 45% positive concordance. Importantly, cassette PCR scored 339 swabs as harboring eae + stx+ E. coli, but BAX PCR detected only 71 positives giving only 21% positive concordance, with many false negatives. Cassette PCR is a highly sensitive method for detection of STEC genes in E. coli found in carcass swabs.

Myoelectric prosthesis users and non-disabled individuals wearing a simulated prosthesis exhibit similar compensatory movement strategies.

BACKGROUND: Research studies on upper limb prosthesis function often rely on the use of simulated myoelectric prostheses (attached to and operated by individuals with intact limbs), primarily to increase participant sample size. However, it is not known if these devices elicit the same movement strategies as myoelectric prostheses (operated by individuals with amputation). The objective of this study was to address the question of whether non-disabled individuals using simulated prostheses employ the same compensatory movements (measured by hand and upper body kinematics) as individuals who use actual myoelectric prostheses. METHODS: The upper limb movements of two participant groups were investigated: (1) twelve non-disabled individuals wearing a simulated prosthesis, and (2) three individuals with transradial amputation using their custom-fitted myoelectric devices. Motion capture was used for data collection while participants performed a standardized functional task. Performance metrics, hand movements, and upper body angular kinematics were calculated. For each participant group, these measures were compared to those from a normative baseline dataset. Each deviation from normative movement behaviour, by either participant group, indicated that compensatory movements were used during task performance. RESULTS: Results show that participants using either a simulated or actual myoelectric prosthesis exhibited similar deviations from normative behaviour in phase durations, hand velocities, hand trajectories, number of movement units, grip aperture plateaus, and trunk and shoulder ranges of motion. CONCLUSIONS: This study suggests that the use of a simulated prosthetic device in upper limb research offers a reasonable approximation of compensatory movements employed by a low- to moderately-skilled transradial myoelectric prosthesis user.

Embodied Cooperation to Promote Forgiving Interactions With Autonomous Machines.

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.

Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison.

Modern automation systems largely rely on closed loop control, wherein a controller interacts with a controlled process via actions, based on observations. These systems are increasingly complex, yet most deployed controllers are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but their simplicity is at odds with the robustness required to reliably control complex processes. Modern machine learning techniques offer a way to extend PID controllers beyond their linear control capabilities by using neural networks. However, such an extension comes at the cost of losing stability guarantees and controller interpretability. In this paper, we examine the utility of extending PID controllers with recurrent neural networks--namely, General Dynamic Neural Networks (GDNN); we show that GDNN (neural) PID controllers perform well on a range of complex control systems and highlight how they can be a scalable and interpretable option for modern control systems. To do so, we provide an extensive study using four benchmark systems that represent the most common control engineering benchmarks. All control environments are evaluated with and without noise as well as with and without disturbances. The neural PID controller performs better than standard PID control in 15 of 16 tasks and better than model-based control in 13 of 16 tasks. As a second contribution, we address the lack of interpretability that prevents neural networks from being used in real-world control processes. We use bounded-input bounded-output stability analysis to evaluate the parameters suggested by the neural network, making them understandable for engineers. This combination of rigorous evaluation paired with better interpretability is an important step towards the acceptance of neural-network-based control approaches for real-world systems. It is furthermore an important step towards interpretable and safely applied artificial intelligence.

Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures.

Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation. A property that makes these predictive approaches well-suited for robotics is that they can be learned online and incrementally through interaction with the environment. However, a remaining challenge for many prediction-learning approaches is an appropriate choice of prediction-learning parameters, especially parameters that control the magnitude of a learning machine's updates to its predictions (the learning rates or step sizes). Typically, these parameters are chosen based on an extensive parameter search-an approach that neither scales well nor is well-suited for tasks that require changing step sizes due to non-stationarity. To begin to address this challenge, we examine the use of online step-size adaptation using the Modular Prosthetic Limb: a sensor-rich robotic arm intended for use by persons with amputations. Our method of choice, Temporal-Difference Incremental Delta-Bar-Delta (TIDBD), learns and adapts step sizes on a feature level; importantly, TIDBD allows step-size tuning and representation learning to occur at the same time. As a first contribution, we show that TIDBD is a practical alternative for classic Temporal-Difference (TD) learning via an extensive parameter search. Both approaches perform comparably in terms of predicting future aspects of a robotic data stream, but TD only achieves comparable performance with a carefully hand-tuned learning rate, while TIDBD uses a robust meta-parameter and tunes its own learning rates. Secondly, our results show that for this particular application TIDBD allows the system to automatically detect patterns characteristic of sensor failures common to a number of robotic applications. As a third contribution, we investigate the sensitivity of classic TD and TIDBD with respect to the initial step-size values on our robotic data set, reaffirming the robustness of TIDBD as shown in previous papers. Together, these results promise to improve the ability of robotic devices to learn from interactions with their environments in a robust way, providing key capabilities for autonomous agents and robots.

Compensatory strategies of body-powered prosthesis users reveal primary reliance on trunk motion and relation to skill level.

BACKGROUND: While body-powered prostheses are commonly used, the compensatory strategies required to operate body-powered devices are not well understood. Kinematic assessment in addition to standard clinical tests can give a comprehensive evaluation of prosthesis user function and skill. This study investigated the movement compensations of body-powered prosthesis users and determined whether a correlation is present between compensatory strategies and skill level, as measured by a standard clinical test. METHODS: Five transradial body-powered prosthesis users completed two standardized upper limb tasks. A 12-camera motion capture system was used to obtain three-dimensional angular kinematics for eight degrees of freedom at the trunk, shoulder, and elbow. Range of motion was compared to a normative dataset. Pearson's correlation was used to assess the relationship between the Activities Measure for Upper Limb Amputees and range of motion for each degree of freedom. FINDINGS: Participants displayed a statistically significant (P < .05) increase in range of motion at the trunk for both tasks. Shoulder flexion/extension range of motion was significantly reduced (P < .05) compared to normative values, but shoulder abduction/adduction range of motion did not show a consistent difference compared to norms. Skill level was correlated with range of motion for specific degrees of freedom at the trunk, shoulder, and elbow. INTERPRETATION: Body-powered prosthesis users compensated with trunk movement and showed reduced motion for shoulder flexion/extension, with relatively normal shoulder abduction/adduction. Skill level was correlated with angular kinematic strategies, which may allow targeting of specific therapeutic interventions for reducing compensatory movements.

Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol.

BACKGROUND: Successful hand-object interactions require precise hand-eye coordination with continual movement adjustments. Quantitative measurement of this visuomotor behaviour could provide valuable insight into upper limb impairments. The Gaze and Movement Assessment (GaMA) was developed to provide protocols for simultaneous motion capture and eye tracking during the administration of two functional tasks, along with data analysis methods to generate standard measures of visuomotor behaviour. The objective of this study was to investigate the reproducibility of the GaMA protocol across two independent groups of non-disabled participants, with different raters using different motion capture and eye tracking technology. METHODS: Twenty non-disabled adults performed the Pasta Box Task and the Cup Transfer Task. Upper body and eye movements were recorded using motion capture and eye tracking, respectively. Measures of hand movement, angular joint kinematics, and eye gaze were compared to those from a different sample of twenty non-disabled adults who had previously performed the same protocol with different technology, rater and site. RESULTS: Participants took longer to perform the tasks versus those from the earlier study, although the relative time of each movement phase was similar. Measures that were dissimilar between the groups included hand distances travelled, hand trajectories, number of movement units, eye latencies, and peak angular velocities. Similarities included all hand velocity and grip aperture measures, eye fixations, and most peak joint angle and range of motion measures. DISCUSSION: The reproducibility of GaMA was confirmed by this study, despite a few differences introduced by learning effects, task demonstration variation, and limitations of the kinematic model. GaMA accurately quantifies the typical behaviours of a non-disabled population, producing precise quantitative measures of hand function, trunk and angular joint kinematics, and associated visuomotor behaviour. This work advances the consideration for use of GaMA in populations with upper limb sensorimotor impairment.

Quantitative Eye Gaze and Movement Differences in Visuomotor Adaptations to Varying Task Demands Among Upper-Extremity Prosthesis Users.

Importance: New treatments for upper-limb amputation aim to improve movement quality and reduce visual attention to the prosthesis. However, evaluation is limited by a lack of understanding of the essential features of human-prosthesis behavior and by an absence of consistent task protocols. Objective: To evaluate whether task selection is a factor in visuomotor adaptations by prosthesis users to accomplish 2 tasks easily performed by individuals with normal arm function. Design, Setting, and Participants: This cross-sectional study was conducted in a single research center at the University of Alberta, Edmonton, Alberta, Canada. Upper-extremity prosthesis users were recruited from January 1, 2016, through December 31, 2016, and individuals with normal arm function were recruited from October 1, 2015, through November 30, 2015. Eight prosthesis users and 16 participants with normal arm function were asked to perform 2 goal-directed tasks with synchronized motion capture and eye tracking. Data analysis was performed from December 3, 2018, to April 15, 2019. Main Outcome and Measures: Movement time, eye fixation, and range of motion of the upper body during 2 object transfer tasks (cup and box) were the main outcomes. Results: A convenience sample comprised 8 male prosthesis users with acquired amputation (mean [range] age, 45 [30-64] years), along with 16 participants with normal arm function (8 [50%] of whom were men; mean [range] age, 26 [18-43] years; mean [range] height, 172.3 [158.0-186.0] cm; all right handed). Prosthesis users spent a disproportionately prolonged mean (SD) time in grasp and release phases when handling the cups (grasp: 2.0 [2.3] seconds vs 0.9 [0.8] seconds; P < .001; release: 1.1 [0.6] seconds vs 0.7 [0.4] seconds; P < .001). Prosthesis users also had increased mean (SD) visual fixations on the hand for the cup compared with the box task during reach (10.2% [12.1%] vs 2.2% [2.8%]) and transport (37.1% [9.7%] vs 22.3% [7.6%]). Fixations on the hand for both tasks were significantly greater for prosthesis users compared with normative values. Prosthesis users had significantly more trunk flexion and extension for the box task compared with the cup task (mean [SD] trunk range of motion, 32.1 [10.7] degrees vs 21.2 [3.7] degrees; P = .01), with all trunk motions greater than normative values. The box task required greater shoulder movements compared with the cup task for prosthesis users (mean [SD] flexion and extension; 51.3 [12.6] degrees vs 41.0 [9.4] degrees, P = .01; abduction and adduction: 40.5 [7.2] degrees vs 32.3 [5.1] degrees, P = .02; rotation: 50.6 [15.7] degrees vs 35.5 [10.0] degrees, P = .02). However, other than shoulder abduction and adduction for the box task, these values were less than those seen for participants with normal arm function. Conclusions and Relevance: This study suggests that prosthesis users have an inherently different way of adapting to varying task demands, therefore suggesting that task selection is crucial in evaluating visuomotor performance. The cup task required greater compensatory visual fixations and prolonged grasp and release movements, and the box task required specific kinematic compensatory strategies as well as increased visual fixation. This is the first study to date to examine visuomotor differences in prosthesis users across varying task demands, and the findings appear to highlight the advantages of quantitative assessment in understanding human-prosthesis interaction.

Detection of pathogenic Escherichia coli on potentially contaminated beef carcasses using cassette PCR and conventional PCR.

BACKGROUND: Over a one year period, swabs of 820 beef carcasses were tested for the presence of Shiga toxin-producing Escherichia coli by performing Polymerase Chain Reaction (PCR) in a novel technology termed "cassette PCR", in comparison to conventional liquid PCR. Cassette PCR is inexpensive and ready-to-use. The operator need only add the sample and press "go". Cassette PCR can simultaneously test multiple samples for multiple targets. Carcass swab samples were first tested for the presence of STEC genes (O157, eae, stx1 and stx2). Samples were considered to be pathogenic if positive for eae plus stx1 and/or stx2. For samples scored as pathogenic, further testing screened for 6 additional high frequency O-antigens (O26, O45, O103, O111, O121, and O145). RESULTS: Of the 820 samples, 41% were pathogenic and 30% were O157 positive. Of these, 19% of samples were positive for O157 and carried potentially pathogenic E. coli (eae plus stx1 and/or stx2). Of all samples identified as carrying pathogenic E. coli, 18.9, 38.8, 41.4, 0, 36.1, and 4.1% respectively were positive for O26, O45, O103, O111, O121, and O145. To validate cassette PCR testing, conventional PCR using STEC primers was performed on each of the 820 samples. Only 148 of 3280 cassette PCR tests were discordant with conventional PCR results. However, further fractional testing showed that 110 of these 148 PCRs reflected low numbers of E. coli in the enrichment broth and could be explained as due to Poisson limiting dilution of the template, affecting both cassette PCR and conventional PCR. Of the remaining 38 discordant tests, 27 initial capillary PCRs and 10 initial conventional tests were nominally discordant between cassette and conventional PCR, perhaps reflecting human/technical error on both sides of the comparison. CONCLUSIONS: Contaminated beef carcass swabs were often complex, likely harboring more than one strain of pathogenic E. coli. Cassette PCR had 98.8% concordance with parallel conventional PCR for detection of STEC genes. This indicates that cassette PCR is highly reliable for detecting multiple pathogens in beef carcass swabs from processing plants.

Hand Function Kinematics when using a Simulated Myoelectric Prosthesis.

Studies that investigate myoelectric prosthesis control commonly use non-disabled participants fitted with a simulated prosthetic device. This approach improves participant recruitment numbers but assumes that simulated movements represent those of actual prosthesis users. If this assumption is valid, then movement performance differences between simulated prosthesis users and normative populations should be similar to differences between actual prosthesis users and normative populations. As a first step in testing this assumption, the objective of this study was to quantify movement performance differences between simulated transradial myoelectric prosthesis hand function and normative hand function. Motion capture technology was used to obtain hand kinematics for 12 non-disabled simulated prosthesis participants who performed a functional object-manipulation task. Performance metrics, end effector movement, and grip aperture results were compared to 20 nondisabled participants who used their own hand during task execution. Simulated prosthesis users were expected to perform the functional task more slowly, with multiple peaks in end effector velocity profiles, and a plateau in grip aperture when reaching to pick up objects, when compared to non-disabled participants. This study confirmed these expectations and recommends that subsequent research be undertaken to quantify differences in actual myoelectric prosthesis hand function versus normative hand function.

Supporting Play by Applying Haptic Guidance Along a Surface Learnt from Single Motion Trajectories.

Haptic-enabled teleoperated robots can help children with physical disabilities to reach toys by applying haptic guidance towards their toys, thus compensating for their limitations in reaching and manipulating objects. In this article we preliminarily tested a learning from demonstration (LfD) approach, where a robotic system learnt the surface that best approximated to all motion trajectories demonstrated by the participants while playing a whack-a-mole game. The end-goal of the system is for therapists or parents to demonstrate to it how to play a game, and then be used by children with physical disabilities. In this study, four adults without disabilities participated, to identify aspects that will be necessary to improve before conducting trials with children. During the demonstration phase, participants played the game in normal teleoperation, assuming the role of the therapist/parent. Then, the surface was modeled using a neural network. Participants played the game without and with the haptic guidance. The movements of the robotic system were mirrored to induce errors in movements, and thus require the guidance. Participants spent more time, moved the robot longer distances, and had jerkier movements when they played the game with the guidance than without it. Possible reasons were discussed, and several solutions were proposed to improve the system. The main contribution of this paper was the learning of a surface instead of learning a single motion trajectory.

The Effect of an Automatically Levelling Wrist Control System.

Upper limb loss is a devastating injury for which current prosthetic replacement inadequately compensates. A lack of wrist movement in prostheses due to mechanical design and control system considerations compels prosthetic users to employ compensatory movements using their upper back and shoulder that can eventually result in strain and overuse injuries. One possible means of easing this control burden is to allow a prosthetic wrist to self-regulate, keeping the terminal device of the prosthesis level relative to the ground when appropriate, such as when raising a cup of liquid. This study aims to outline such a wrist control scheme, and evaluate its function in terms of the effect on compensatory movements, objective system performance, and subjective perception of system performance based on user feedback. To that end, twelve able-bodied participants were recruited to control a body-mounted robotic arm using three different control schemes: fixed-wrist (FW), sequential switching (SS), and automatic levelling (AL). The resulting movement strategies were recorded for two different tasks using 3D motion-capture. SS and AL control schemes induced similar movement strategies and less compensation than FW for horizontal movements, while AL reduced shoulder flexion compared to FW and SS for vertical movements. However, AL was ranked less intuitive and less reliable than the FW. AL and SS both seemed to involve more conscious thought to operate than FW. These results suggest that more complex wrist control schemes may indeed be able to eliminate harmful compensatory movements, but reinforce prior observations that control must be reliable and simple to use or people will opt for an easier system.