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.

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.

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.

Using synchronized eye and motion tracking to determine high-precision eye-movement patterns during object-interaction tasks.

This study explores the role that vision plays in sequential object interactions. We used a head-mounted eye tracker and upper-limb motion capture to quantify visual behavior while participants performed two standardized functional tasks. By simultaneously recording eye and motion tracking, we precisely segmented participants' visual data using the movement data, yielding a consistent and highly functionally resolved data set of real-world object-interaction tasks. Our results show that participants spend nearly the full duration of a trial fixating on objects relevant to the task, little time fixating on their own hand when reaching toward an object, and slightly more time-although still very little-fixating on the object in their hand when transporting it. A consistent spatial and temporal pattern of fixations was found across participants. In brief, participants fixate an object to be picked up at least half a second before their hand arrives at the object and stay fixated on the object until they begin to transport it, at which point they shift their fixation directly to the drop-off location of the object, where they stay fixated until the object is successfully released. This pattern provides additional evidence of a common system for the integration of vision and object interaction in humans, and is consistent with theoretical frameworks hypothesizing the distribution of attention to future action targets as part of eye and hand-movement preparation. Our results thus aid the understanding of visual attention allocation during planning of object interactions both inside and outside the field of view.

NOTCH2 and FLT3 gene mis-splicings are common events in patients with acute myeloid leukemia (AML): new potential targets in AML.

Our previous studies revealed an increase in alternative splicing of multiple RNAs in cells from patients with acute myeloid leukemia (AML) compared with CD34(+) bone marrow cells from normal donors. Aberrantly spliced genes included a number of oncogenes, tumor suppressor genes, and genes involved in regulation of apoptosis, cell cycle, and cell differentiation. Among the most commonly mis-spliced genes (>70% of AML patients) were 2, NOTCH2 and FLT3, that encode myeloid cell surface proteins. The splice variants of NOTCH2 and FLT3 resulted from complete or partial exon skipping and utilization of cryptic splice sites. Longitudinal analyses suggested that NOTCH2 and FLT3 aberrant splicing correlated with disease status. Correlation analyses between splice variants of these genes and clinical features of patients showed an association between NOTCH2-Va splice variant and overall survival of patients. Our results suggest that NOTCH2 and FLT3 mis-splicing is a common characteristic of AML and has the potential to generate transcripts encoding proteins with altered function. Thus, splice variants of these genes might provide disease markers and targets for novel therapeutics.

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.

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.

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.

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.

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.

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.

Inherited and acquired variations in the hyaluronan synthase 1 (HAS1) gene may contribute to disease progression in multiple myeloma and Waldenstrom macroglobulinemia.

To characterize genetic contributions toward aberrant splicing of the hyaluronan synthase 1 (HAS1) gene in multiple myeloma (MM) and Waldenstrom macroglobulinemia (WM), we sequenced 3616 bp in HAS1 exons and introns involved in aberrant splicing, from 17 patients. We identified a total of 197 HAS1 genetic variations (GVs), a range of 3 to 24 GVs/patient, including 87 somatic GVs acquired in splicing regions of HAS1. Nearly all newly identified inherited and somatic GVs in MM and/or WM were absent from B chronic lymphocytic leukemia, nonmalignant disease, and healthy donors. Somatic HAS1 GVs recurred in all hematopoietic cells tested, including normal CD34(+) hematopoietic progenitor cells and T cells, or as tumor-specific GVs restricted to malignant B and plasma cells. An in vitro splicing assay confirmed that HAS1 GVs direct aberrant HAS1 intronic splicing. Recurrent somatic GVs may be enriched by strong mutational selection leading to MM and/or WM.

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.

Genetic abnormalities in Waldenström's macroglobulinemia.

The genetic factors that lead to WM are mostly unknown but are likely to involve inherited polymorphisms that might be markers of increased risk for developing WM, and somatic mutations that might be acquired during the events leading to oncogenesis and cancer progression. By intensive sequencing of the hyaluronan synthase 1 (HAS1) gene in malignant and normal cells from patients with WM, we have identified both types of mutation in HAS1 exons and introns. Acquired HAS1 mutations are found in malignant cells as well as presumptively nonmalignant CD34+ progenitor cells. This suggests that acquired HAS1 mutations precede frank malignancy and might contribute to the initial transforming events in WM as well as to disease progression.

Exploring the Impact of Machine-Learned Predictions on Feedback from an Artificial Limb.

Learning to get by without an arm or hand can be very challenging, and existing prostheses do not yet fill the needs of individuals with amputations. One promising solution is to improve the feedback from the device to the user. Towards this end, we present a simple machine learning interface to supplement the control of a robotic limb with feedback to the user about what the limb will be experiencing in the near future. A real-time prediction learner was implemented to predict impact-related electrical load experienced by a robot limb; the learning system's predictions were then communicated to the device's user to aid in their interactions with a workspace. We tested this system with five able-bodied subjects. Each subject manipulated the robot arm while receiving different forms of vibrotactile feedback regarding the arm's contact with its workspace. Our trials showed that using machine-learned predictions as a basis for feedback led to a statistically significant improvement in task performance when compared to purely reactive feedback from the device. Our study therefore contributes initial evidence that prediction learning and machine intelligence can benefit not just control, but also feedback from an artificial limb. We expect that a greater level of acceptance and ownership can be achieved if the prosthesis itself takes an active role in transmitting learned knowledge about its state and its situation of use.