User training for machine learning controlled upper limb prostheses: a serious game approach

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education

The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.

The Role of AI in Serious Games and Gamification for Health: Scoping Review

BACKGROUND

Artificial intelligence (AI) and game-based methods such as serious games or gamification are both emerging technologies and methodologies in health care. The merging of the two could provide greater advantages, particularly in the field of therapeutic interventions in medicine.

OBJECTIVE

This scoping review sought to generate an overview of the currently existing literature on the connection of AI and game-based approaches in health care. The primary objectives were to cluster studies by disease and health topic addressed, level of care, and AI or games technology.

METHODS

For this scoping review, the databases PubMed, Scopus, IEEE Xplore, Cochrane Library, and PubPsych were comprehensively searched on February 2, 2022. Two independent authors conducted the screening process using Rayyan software (Rayyan Systems Inc). Only original studies published in English since 1992 were eligible for inclusion. The studies had to involve aspects of therapy or education in medicine and the use of AI in combination with game-based approaches. Each publication was coded for basic characteristics, including the population, intervention, comparison, and outcomes (PICO) criteria; the level of evidence; the disease and health issue; the level of care; the game variant; the AI technology; and the function type. Inductive coding was used to identify the patterns, themes, and categories in the data. Individual codings were analyzed and summarized narratively.

RESULTS

A total of 16 papers met all inclusion criteria. Most of the studies (10/16, 63%) were conducted in disease rehabilitation, tackling motion impairment (eg, after stroke or trauma). Another cluster of studies (3/16, 19%) was found in the detection and rehabilitation of cognitive impairment. Machine learning was the main AI technology applied and serious games the main game-based approach used. However, direct interaction between the technologies occurred only in 3 (19%) of the 16 studies. The included studies all show very limited quality evidence. From the patients' and healthy individuals' perspective, generally high usability, motivation, and satisfaction were found.

CONCLUSIONS

The review shows limited quality of evidence for the combination of AI and games in health care. Most of the included studies were nonrandomized pilot studies with few participants (14/16, 88%). This leads to a high risk for a range of biases and limits overall conclusions. However, the first results present a broad scope of possible applications, especially in motion and cognitive impairment, as well as positive perceptions by patients. In future, the development of adaptive game designs with direct interaction between AI and games seems promising and should be a topic for future reviews.

Reducing Motor Variability Enhances Myoelectric Control Robustness Across Untrained Limb Positions

The limb position effect is a multi-faceted problem, associated with decreased upper-limb prosthesis control acuity following a change in arm position. Factors contributing to this problem can arise from distinct environmental or physiological sources. Despite their differences in origin, the effect of each factor manifests similarly as increased input data variability. This variability can cause incorrect decoding of user intent. Previous research has attempted to address this by better capturing input data variability with data abundance. In this paper, we take an alternative approach and investigate the effect of reducing trial-to-trial variability by improving the consistency of muscle activity through user training. Ten participants underwent 4 days of myoelectric training with either concurrent or delayed feedback in a single arm position. At the end of training participants experienced a zero-feedback retention test in multiple limb positions. In doing so, we tested how well the skill learned in a single limb position generalized to untrained positions. We found that delayed feedback training led to more consistent muscle activity across both the trained and untrained limb positions. Analysis of patterns of activations in the delayed feedback group suggest a structured change in muscle activity occurs across arm positions. Our results demonstrate that myoelectric user-training can lead to the retention of motor skills that bring about more robust decoding across untrained limb positions. This work highlights the importance of reducing motor variability with practice, prior to examining the underlying structure of muscle changes associated with limb position.

Current status and clinical perspectives of extended reality for myoelectric prostheses: review

Training with "Extended Reality" or X-Reality (XR) systems can undoubtedly enhance the control of the myoelectric prostheses. However, there is no consensus on which factors improve the efficiency of skill transfer from virtual training to actual prosthesis abilities. This review examines the current status and clinical applications of XR in the field of myoelectric prosthesis training and analyses possible influences on skill migration. We have conducted a thorough search on databases in the field of prostheses using keywords such as extended reality, virtual reality and serious gaming. Our scoping review encompassed relevant applications, control methods, performance evaluation and assessment metrics. Our findings indicate that the implementation of XR technology for myoelectric rehabilitative training on prostheses provides considerable benefits. Additionally, there are numerous standardised methods available for evaluating training effectiveness. Recently, there has been a surge in the number of XR-based training tools for myoelectric prostheses, with an emphasis on user engagement and virtual training evaluation. Insufficient attention has been paid to significant limitations in the behaviour, functionality, and usage patterns of XR and myoelectric prostheses, potentially obstructing the transfer of skills and prospects for clinical application. Improvements are recommended in four critical areas: activities of daily living, training strategies, feedback, and the alignment of the virtual environment with the physical devices.

Training prosthesis users to switch between modes of a multi-articulating prosthetic hand

PURPOSE

Producing triggers to switch between modes of myoelectric prosthetic hands has proven to be difficult. We evaluated whether digital training methods were feasible in individuals with an upper limb defect (ULD), whether myosignals in these individuals differ from those of non-impaired individuals and whether acquired skills transfer to prosthesis use.

MATERIALS AND METHODS

Two groups participated in a 9-day pre-test-post-test design study with seven 45-minute training sessions. One group trained using a serious game, the other with their myosignals digitally displayed. Both groups also trained using a prosthesis. The pre- and post-tests consisted of an adapted Clothespin Relocation Test and the spherical subset of the Southampton Hand Assessment Procedure. After the post-test, the System Usability Scale (SUS) was administered. Clinically relevant performance measures and myosignal features were analysed.

RESULTS

Four individuals with a ULD participated. SUS-scores deemed both training methods feasible. Three participants produced only a few correct triggers. Myosignals features indicated larger variability for individuals with a ULD compared to non-impaired individuals (previously published data [1]). Three participants indicated transfer of skill.

CONCLUSIONS

Even though both training methods were deemed feasible and most participants showed transfer, seven training sessions were insufficient to learn reliable switching behaviour.Trial registration: The study was approved by the medical ethics committee of the University Medical Center Groningen (METc 2018.268).Implications for rehabilitationSwitching between pre-programmed modes of a myoelectric prosthetic hand can be learned, however it does require training.Serious games can be considered useful training tools for trigger production in early phases of myoelectric prosthesis control training.In order to evoke transfer of skill from training to daily life both task-specificity and focus of attention during training should be taken into account.

Artificial intelligence in physical rehabilitation: A systematic review

BACKGROUND

Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear.

METHODS

We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey.

RESULTS

We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost.

CONCLUSION

Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.

Progressive unsupervised control of myoelectric upper limbs

Objective.Unsupervised myocontrol methods aim to create control models for myoelectric prostheses while avoiding the complications of acquiring reliable, regular, and sufficient labeled training data. A limitation of current unsupervised methods is that they fix the number of controlled prosthetic functions a priori, thus requiring an initial assessment of the user's motor skills and neglecting the development of novel motor skills over time.Approach.We developed a progressive unsupervised myocontrol (PUM) paradigm in which the user and the control model coadaptively identify distinct muscle synergies, which are then used to control arbitrarily associated myocontrol functions, each corresponding to a hand or wrist movement. The interaction starts with learning a single function and the user may request additional functions after mastering the available ones, which aligns the evolution of their motor skills with an increment in system complexity. We conducted a multi-session user study to evaluate PUM and compare it against a state-of-the-art non-progressive unsupervised alternative. Two participants with congenital upper-limb differences tested PUM, while ten non-disabled control participants tested either PUM or the non-progressive baseline. All participants engaged in myoelectric control of a virtual hand and wrist.Main results.PUM enabled autonomous learning of three myocontrol functions for participants with limb differences, and of all four available functions for non-disabled subjects, using both existing or newly identified muscle synergies. Participants with limb differences achieved similar success rates to non-disabled ones on myocontrol tests, but faced greater difficulties in internalizing new motor skills and exhibited slightly inferior movement quality. The performance was comparable with either PUM or the non-progressive baseline for the group of non-disabled participants.Significance.The PUM paradigm enables users to autonomously learn to operate the myocontrol system, adapts to the users' varied preexisting motor skills, and supports the further development of those skills throughout practice.

Delivery of UK military upper limb prosthetics: current concepts and future directions

Upper limb prosthetics have a challenging task. A natural upper limb combines strength, coordination and dexterity to accomplish daily activities such as eating, writing, working and social interaction. Artificially replicating these functions requires a prosthetic with composite, synchronous motor function while maintaining sensory feedback and skeletal stability. Achieving these functions requires interfaces between biology and machine across nerve, muscle, bone and skin. This leads to issues related to infection, foreign material encapsulation and implant stability, and electrical signal transduction and interpretation. Over the last 20 years the advent of technologies such as osseointegration, targeted muscle reinnervation, implantable myoelectric sensors, peripheral nerve interfaces and pattern recognition technology has sought to address these problems.Due to many advances in prehospital care, truncated timelines to damage control surgery and improved combat personal protective equipment, the numbers of amputees have increased with more patients surviving injury. From October 2001 to March 2019 there were 333 amputees from Afghanistan and Iraq compared with 457 fatalities over a similar period. Over a third of these were significant multiple amputees. With a functional, robust upper limb prosthetic which mirrors or exceeds normal function, injured service personnel could be returned to an active combat role. This has benefits for their physical and mental health, improves employability prospects and allows Defence to retain some of its most highly motivated and skilled people who represent significant financial investment.

Use of machine learning in the field of prosthetics and orthotics: A systematic narrative review

Although machine learning is not yet being used in clinical practice within the fields of prosthetics and orthotics, several studies on the use of prosthetics and orthotics have been conducted. We intend to provide relevant knowledge by conducting a systematic review of prior studies on using machine learning in the fields of prosthetics and orthotics. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Cochrane, Embase, and Scopus databases and retrieved studies published until July 18, 2021. The study included the application of machine learning algorithms to upper-limb and lower-limb prostheses and orthoses. The criteria of the Quality in Prognosis Studies tool were used to assess the methodological quality of the studies. A total of 13 studies were included in this systematic review. In the realm of prostheses, machine learning has been used to identify prosthesis, select an appropriate prosthesis, train after wearing the prosthesis, detect falls, and manage the temperature in the socket. In the field of orthotics, machine learning was used to control real-time movement while wearing an orthosis and predict the need for an orthosis. The studies included in this systematic review are limited to the algorithm development stage. However, if the developed algorithms are actually applied to clinical practice, it is expected that it will be useful for medical staff and users to handle prosthesis and orthosis.

Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design

BACKGROUND

Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the user. Nevertheless, a long-term study assessing the efficacy of incremental myocontrol is still missing, partially due to the lack of an adequate tool to do so. In this work we close this gap and report about a person with upper-limb absence who learned to control a dexterous hand prosthesis using incremental myocontrol through a novel functional assessment protocol called SATMC (Simultaneous Assessment and Training of Myoelectric Control).

METHODS

The participant was fitted with a custom-made prosthetic setup with a controller based on Ridge Regression with Random Fourier Features (RR-RFF), a non-linear, incremental machine learning method, used to build and progressively update the myocontrol system. During a 13-month user study, the participant performed increasingly complex daily-living tasks, requiring fine bimanual coordination and manipulation with a multi-fingered hand prosthesis, in a realistic laboratory setup. The SATMC was used both to compose the tasks and continually assess the participant's progress. Patient satisfaction was measured using Visual Analog Scales.

RESULTS

Over the course of the study, the participant progressively improved his performance both objectively, e.g., the time required to complete each task became shorter, and subjectively, meaning that his satisfaction improved. The SATMC actively supported the improvement of the participant by progressively increasing the difficulty of the tasks in a structured way. In combination with the incremental RR-RFF allowing for small adjustments when required, the participant was capable of reliably using four actions of the prosthetic hand to perform all required tasks at the end of the study.

CONCLUSIONS

Incremental myocontrol enabled an upper-limb amputee to reliably control a dexterous hand prosthesis while providing a subjectively satisfactory experience. The SATMC can be an effective tool to this aim.

Virtual regression-based myoelectric hand-wrist prosthesis control and electrode site selection using no force feedback

Most transradial prosthesis users with conventional "Sequential" myoelectric control have two electrode sites which control one degree of freedom (DoF) at a time. Rapid EMG co-activation toggles control between DoFs (e.g., hand and wrist), providing limited function. We implemented a regression-based EMG control method which achieved simultaneous and proportional control of two DoFs in a virtual task. We automated electrode site selection using a short-duration (90 s) calibration period, without force feedback. Backward stepwise selection located the best electrodes for either six or 12 electrodes (selected from a pool of 16). We additionally studied two, 2-DoF controllers: "Intuitive" control (hand open-close and wrist pronation-supination controlled virtual target size and rotation, respectively) and "Mapping" control (wrist flexion-extension and ulnar-radial deviation controlled virtual target left-right and up-down movement, respectively). In practice, a Mapping controller would be mapped to control prosthesis hand open-close and wrist pronation-supination. Eleven able-bodied subjects and 4 limb-absent subjects completed virtual target matching tasks (fixed target moves to a new location after being "matched," and subject immediately pursues) and fixed (static) target tasks. For all subjects, both 2-DoF controllers with 6 optimally-sited electrodes had statistically better target matching performance than Sequential control in number of matches (average of 4-7 vs. 2 matches, p< 0.001) and throughput (average of 0.75-1.25 vs. 0.4 bits/s, p< 0.001), but not overshoot rate and path efficiency. There were no statistical differences between 6 and 12 optimally-sited electrodes for both 2-DoF controllers. These results support the feasibility of 2-DoF simultaneous, proportional myoelectric control.

Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

Background

People use their hands to perform sophisticated tasks like playing a musical instrument by integrating manifold and diverse sensations of touch with motor control strategies. In contrast, prosthetic hands lack the capacity for multichannel haptic feedback and multitasking functionality remains rudimentary. There is a dearth of research exploring the potential of upper limb absent (ULA) people to integrate multiple channels of haptic feedback into dexterous prosthetic hand control strategies.

Methods

In this paper, we designed a novel experimental paradigm for three ULA people and nine additional subjects to investigate their ability to integrate two simultaneously activated channels of context-specific haptic feedback into their dexterous artificial hand control strategies. Artificial neural networks (ANN) were designed for pattern recognition of the array of efferent electromyogram signals that controlled the dexterous artificial hand. ANNs were also used to classify the directions that objects were sliding across two tactile sensor arrays on the index (I) and little (L) fingertips of the robotic hand. The direction of sliding contact at each robotic fingertip was encoded by different stimulation frequencies of wearable vibrotactile actuators for haptic feedback. The subjects were tasked with implementing different control strategies with each finger simultaneously depending upon the perceived directions of sliding contact. This required the 12 subjects to concurrently control individual fingers of the artificial hand by successfully interpreting two channels of simultaneously activated context-specific haptic feedback.

Results

Subjects were able to accomplish this complex feat of multichannel sensorimotor integration with an overall accuracy of 95.53% ± 0.23%. While there was no statistically significant difference in the classification accuracy between ULA people and the other subjects, the ULA people required more time to correctly respond to the simultaneous haptic feedback slip signals, suggesting a higher cognitive load required by the ULA people.

Conclusion

ULA people can integrate multiple channels of simultaneously activated and nuanced haptic feedback with their control of individual fingers of an artificial hand. These findings provide a step toward empowering amputees to multitask with dexterous prosthetic hands, which remains an ongoing challenge.

Direction of attentional focus in prosthetic training: Current practice and potential for improving motor learning in individuals with lower limb loss

Objective

Adopting an external focus of attention has been shown to benefit motor performance and learning. However, the potential of optimizing attentional focus for improving prosthetic motor skills in lower limb prosthesis (LLP) users has not been examined. In this study, we investigated the frequency and direction of attentional focus embedded in the verbal instructions in a clinical prosthetic training setting.

Methods

Twenty-one adult LLP users (8 female, 13 male; 85% at K3 level; mean age = 50.5) were recruited from prosthetic clinics in the Southern Nevada region. Verbal interactions between LLP users and their prosthetists (mean experience = 10 years, range = 4–21 years) during prosthetic training were recorded. Recordings were analyzed to categorize the direction of attentional focus embedded in the instructional and feedback statements as internal, external, mixed, or unfocused. We also explored whether LLP users’ age, time since amputation, and perceived mobility were associated with the proportion of attentional focus statements they received.

Results

We recorded a total of 20 training sessions, yielding 904 statements of instruction from 338 minutes of training. Overall, one verbal interaction occurred every 22.1 seconds. Among the statements, 64% were internal, 9% external, 3% mixed, and 25% unfocused. Regression analysis revealed that female, older, and higher functioning LLP users were significantly more likely to receive internally-focused instructions (p = 0.006, 0.035, and 0.024, respectively).

Conclusions

Our results demonstrated that verbal instructions and feedback are frequently provided to LLP users during prosthetic training. Most verbal interactions are focused internally on the LLP users’ body movements and not externally on the movement effects.

Impact statement

While more research is needed to explore how motor learning principles may be applied to improve LLP user outcomes, clinicians should consider adopting the best available scientific evidence during treatment. Overreliance on internally-focused instructions as observed in the current study may hinder prosthetic skill learning.

A scoping review of the application of motor learning principles to optimize myoelectric prosthetic hand control

Although prosthetic hand rejection rates remain high, evidence suggests that effective training plays a major role in device acceptance. Receiving training early in the rehabilitation process also enhances functional prosthetic use, decreases the likelihood of developing an overreliance on the intact limb, and reduces amputation-related pain. Despite these obvious benefits, there is a current lack of evidence regarding the most effective training techniques to facilitate myoelectric prosthetic hand control, and it remains unknown whether training is effective in facilitating the acquisition and transfer of prosthetic skill. In this scoping review, we introduced and summarized key motor learning principles related to attentional focus, implicit motor learning, training eye-hand coordination, practice variability, motor imagery, and action observation, and virtual training and biofeedback. We then reviewed the existing literature that has applied these principles for training prosthetic hand control before outlining future avenues for further research. The importance of optimizing early and appropriate training cannot be overlooked. While the intuition and experience of clinicians holds enormous value, evidence-based guidelines based on well-established motor learning principles will also be crucial for training effective prosthetic hand control. While it is clear that more research is needed to form the basis of such guidelines, it is hoped that this review highlights the potential avenues for this work.

Competitive motivation increased home use and improved prosthesis self-perception after Cybathlon 2020 for neuromusculoskeletal prosthesis user

Background

Assistive technologies, such as arm prostheses, are intended to improve the quality of life of individuals with physical disabilities. However, certain training and learning is usually required from the user to make these technologies more effective. Moreover, some people can be encouraged to train more through competitive motivation.

Methods

In this study, we investigated if the training for and participation in a competitive event (Cybathlon 2020) could promote behavioral changes in an individual with upper limb amputation (the pilot). We defined behavioral changes as the active time while his prosthesis was actuated, ratio of opposing and simultaneous movements, and the pilot’s ability to finely modulate his movement speeds. The investigation was based on extensive home-use data from the period before, during and after the Cybathlon 2020 competition.

Results

Relevant behavioral changes were found from both quantitative and qualitative analyses. The pilot’s home use of his prosthesis nearly doubled in the period before the Cybathlon, and remained 66% higher than baseline after the competition. Moreover, he improved his speed modulation when controlling his prosthesis, and he learned and routinely operated new movements in the prosthesis (wrist rotation) at home. Additionally, as confirmed by semi-structured interviews, his self-perception of the prosthetic arm and its functionality also improved.

Conclusions

An event like the Cybathlon may indeed promote behavioral changes in how competitive individuals with amputation use their prostheses. Provided that the prosthesis is suitable in terms of form and function for both competition and at-home daily use, daily activities can become opportunities for training, which in turn can improve prosthesis function and create further opportunities for daily use. Moreover, these changes appeared to remain even well after the event, albeit relevant only for individuals who continue using the technology employed in the competition.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01024-4.

Classification Performance and Feature Space Characteristics in Individuals With Upper Limb Loss Using Sonomyography

Objective: Sonomyography, or ultrasound-based sensing of muscle deformation, is an emerging modality for upper limb prosthesis control. Although prior studies have shown that individuals with upper limb loss can achieve successful motion classification with sonomyography, it is important to better understand the time-course over which proficiency develops. In this study, we characterized user performance during their initial and subsequent exposures to sonomyography. Method: Ultrasound images corresponding to a series of hand gestures were collected from individuals with transradial limb loss under three scenarios: during their initial exposure to sonomyography (Experiment 1), during a subsequent exposure to sonomyography where they were provided biofeedback as part of a training protocol (Experiment 2), and during testing sessions held on different days (Experiment 3). User performance was characterized by offline classification accuracy, as well as metrics describing the consistency and separability of the sonomyography signal patterns in feature space. Results: Classification accuracy was high during initial exposure to sonomyography (96.2 ± 5.9%) and did not systematically change with the provision of biofeedback or on different days. Despite this stable classification performance, some of the feature space metrics changed. Conclusions: User performance was strong upon their initial exposure to sonomyography and did not improve with subsequent exposure. Clinical Impact: Prosthetists may be able to quickly assess if a patient will be successful with sonomyography without submitting them to an extensive training protocol, leading to earlier socket fabrication and delivery.

Serious Games Are Not Serious Enough for Myoelectric Prosthetics

Serious games show a lot of potential for use in movement rehabilitation (eg, after a stroke, injury to the spinal cord, or limb loss). However, the nature of this research leads to diversity both in the background of the researchers and in the approaches of their investigation. Our close examination and categorization of virtual training software for upper limb prosthetic rehabilitation found that researchers typically followed one of two broad approaches: (1) focusing on the game design aspects to increase engagement and muscle training and (2) concentrating on an accurate representation of prosthetic training tasks, to induce task-specific skill transfer. Previous studies indicate muscle training alone does not lead to improved prosthetic control without a transfer-enabling task structure. However, the literature shows a recent surge in the number of game-based prosthetic training tools, which focus on engagement without heeding the importance of skill transfer. This influx appears to have been strongly influenced by the availability of both software and hardware, specifically the launch of a commercially available acquisition device and freely available high-profile game development engines. In this Viewpoint, we share our perspective on the current trends and progress of serious games for prosthetic training.

The Need to Work Arm in Arm: Calling for Collaboration in Delivering Neuroprosthetic Limb Replacements

Over the last few decades there has been a push to enhance the use of advanced prosthetics within the fields of biomedical engineering, neuroscience, and surgery. Through the development of peripheral neural interfaces and invasive electrodes, an individual's own nervous system can be used to control a prosthesis. With novel improvements in neural recording and signal decoding, this intimate communication has paved the way for bidirectional and intuitive control of prostheses. While various collaborations between engineers and surgeons have led to considerable success with motor control and pain management, it has been significantly more challenging to restore sensation. Many of the existing peripheral neural interfaces have demonstrated success in one of these modalities; however, none are currently able to fully restore limb function. Though this is in part due to the complexity of the human somatosensory system and stability of bioelectronics, the fragmentary and as-yet uncoordinated nature of the neuroprosthetic industry further complicates this advancement. In this review, we provide a comprehensive overview of the current field of neuroprosthetics and explore potential strategies to address its unique challenges. These include exploration of electrodes, surgical techniques, control methods, and prosthetic technology. Additionally, we propose a new approach to optimizing prosthetic limb function and facilitating clinical application by capitalizing on available resources. It is incumbent upon academia and industry to encourage collaboration and utilization of different peripheral neural interfaces in combination with each other to create versatile limbs that not only improve function but quality of life. Despite the rapidly evolving technology, if the field continues to work in divided "silos," we will delay achieving the critical, valuable outcome: creating a prosthetic limb that is right for the patient and positively affects their life.