Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Robotic devices and ICT in long-term care in Japan: Their potential and limitations from a workplace perspective

In light of its rapidly aging society, Japan is pressured to full-heartedly address the labor shortage in long-term care. Among the various policy options currently in discussion, government agencies and business sector representatives agree that robotic devices and information and communication technology (ICT) constitute a suitable countermeasure. However, during our research in Japan in 2019, we found that robotic devices and ICT are only reluctantly being introduced into long-term care facilities. Based on our field visits and interviews as well as supplementary document research, this paper discusses the potential that facility managers ascribe to robotic devices and ICT when it comes to alleviating the labor shortage in the long-term care institutions they run. Of particular interest is the question to what degree the usage of robotic devices and ICT could reduce the physical hardships and mental stress that staff in long-term caregiving experience. This paper will further our understanding of the labor situation in long-term care facilities and contribute to the research field of robotic devices and ICT in Japan’s labor market

How, for Whom, and in Which Contexts or Conditions Augmented and Virtual Reality Training Works in Upskilling Health Care Workers: Realist Synthesis

BACKGROUND: Using traditional simulators (eg, cadavers, animals, or actors) to upskill health workers is becoming less common because of ethical issues, commitment to patient safety, and cost and resource restrictions. Virtual reality (VR) and augmented reality (AR) may help to overcome these barriers. However, their effectiveness is often contested and poorly understood and warrants further investigation. OBJECTIVE: The aim of this review is to develop, test, and refine an evidence-informed program theory on how, for whom, and to what extent training using AR or VR works for upskilling health care workers and to understand what facilitates or constrains their implementation and maintenance. METHODS: We conducted a realist synthesis using the following 3-step process: theory elicitation, theory testing, and theory refinement. We first searched 7 databases and 11 practitioner journals for literature on AR or VR used to train health care staff. In total, 80 papers were identified, and information regarding context-mechanism-outcome (CMO) was extracted. We conducted a narrative synthesis to form an initial program theory comprising of CMO configurations. To refine and test this theory, we identified empirical studies through a second search of the same databases used in the first search. We used the Mixed Methods Appraisal Tool to assess the quality of the studies and to determine our confidence in each CMO configuration. RESULTS: Of the 41 CMO configurations identified, we had moderate to high confidence in 9 (22%) based on 46 empirical studies reporting on VR, AR, or mixed simulation training programs. These stated that realistic (high-fidelity) simulations trigger perceptions of realism, easier visualization of patient anatomy, and an interactive experience, which result in increased learner satisfaction and more effective learning. Immersive VR or AR engages learners in deep immersion and improves learning and skill performance. When transferable skills and knowledge are taught using VR or AR, skills are enhanced and practiced in a safe environment, leading to knowledge and skill transfer to clinical practice. Finally, for novices, VR or AR enables repeated practice, resulting in technical proficiency, skill acquisition, and improved performance. The most common barriers to implementation were up-front costs, negative attitudes and experiences (ie, cybersickness), developmental and logistical considerations, and the complexity of creating a curriculum. Facilitating factors included decreasing costs through commercialization, increasing the cost-effectiveness of training, a cultural shift toward acceptance, access to training, and leadership and collaboration. CONCLUSIONS: Technical and nontechnical skills training programs using AR or VR for health care staff may trigger perceptions of realism and deep immersion and enable easier visualization, interactivity, enhanced skills, and repeated practice in a safe environment. This may improve skills and increase learning, knowledge, and learner satisfaction. The future testing of these mechanisms using hypothesis-driven approaches is required. Research is also required to explore implementation considerations

Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design

<p>Abstract</p> <p>Background</p> <p>It is the purpose of this article to identify and review criteria that rehabilitation technology should meet in order to offer arm-hand training to stroke patients, based on recent principles of motor learning.</p> <p>Methods</p> <p>A literature search was conducted in PubMed, MEDLINE, CINAHL, and EMBASE (1997–2007).</p> <p>Results</p> <p>One hundred and eighty seven scientific papers/book references were identified as being relevant. Rehabilitation approaches for upper limb training after stroke show to have shifted in the last decade from being analytical towards being focussed on environmentally contextual skill training (task-oriented training). Training programmes for enhancing motor skills use patient and goal-tailored exercise schedules and individual feedback on exercise performance. Therapist criteria for upper limb rehabilitation technology are suggested which are used to evaluate the strengths and weaknesses of a number of current technological systems.</p> <p>Conclusion</p> <p>This review shows that technology for supporting upper limb training after stroke needs to align with the evolution in rehabilitation training approaches of the last decade. A major challenge for related technological developments is to provide engaging patient-tailored task oriented arm-hand training in natural environments with patient-tailored feedback to support (re) learning of motor skills.</p

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Using thermochromism to simulate blood oxygenation in extracorporeal membrane oxygenation

Introduction: Extracorporeal membrane oxygenation (ECMO) training programs employ real ECMO components, causing them to be extremely expensive while offering little realism in terms of blood oxygenation and pressure. To overcome those limitations, we are developing a standalone modular ECMO simulator that reproduces ECMO’s visual, audio and haptic cues using affordable mechanisms. We present a central component of this simulator, capable of visually reproducing blood oxygenation color change using thermochromism. Methods: Our simulated ECMO circuit consists of two physically distant modules, responsible for adding and withdrawing heat from a thermochromic fluid. This manipulation of heat creates a temperature difference between the fluid in the drainage line and the fluid in the return line of the circuit and, hence, a color difference. Results: Thermochromic ink mixed with concentrated dyes was used to create a recipe for a realistic and affordable blood-colored fluid. The implemented “ECMO circuit” reproduced blood’s oxygenation and deoxygenation color difference or lack thereof. The heat control circuit costs 300 USD to build and the thermochromic fluid costs 40 USD/L. During a ten-hour in situ demonstration, nineteen ECMO specialists rated the fidelity of the oxygenated and deoxygenated “blood” and the color contrast between them as highly realistic. Conclusions: Using low-cost yet high-fidelity simulation mechanisms, we implemented the central subsystem of our modular ECMO simulator, which creates the look and feel of an ECMO circuit without using an actual one.Peer reviewedFinal Accepted Versio

A realist process evaluation of robot-assisted surgery: integration into routine practice and impacts on communication, collaboration and decision-making

YesBackground: The implementation of robot-assisted surgery (RAS) can be challenging, with reports of surgical robots being underused. This raises questions about differences compared with open and laparoscopic surgery and how best to integrate RAS into practice. Objectives: To (1) contribute to reporting of the ROLARR (RObotic versus LAparoscopic Resection for Rectal cancer) trial, by investigating how variations in the implementation of RAS and the context impact outcomes; (2) produce guidance on factors likely to facilitate successful implementation; (3) produce guidance on how to ensure effective teamwork; and (4) provide data to inform the development of tools for RAS. Design: Realist process evaluation alongside ROLARR. Phase 1 – a literature review identified theories concerning how RAS becomes embedded into practice and impacts on teamwork and decision-making. These were refined through interviews across nine NHS trusts with theatre teams. Phase 2 – a multisite case study was conducted across four trusts to test the theories. Data were collected using observation, video recording, interviews and questionnaires. Phase 3 – interviews were conducted in other surgical disciplines to assess the generalisability of the findings. Findings: The introduction of RAS is surgeon led but dependent on support at multiple levels. There is significant variation in the training provided to theatre teams. Contextual factors supporting the integration of RAS include the provision of whole-team training, the presence of handpicked dedicated teams and the availability of suitably sized operating theatres. RAS introduces challenges for teamwork that can impact operation duration, but, over time, teams develop strategies to overcome these challenges. Working with an experienced assistant supports teamwork, but experience of the procedure is insufficient for competence in RAS and experienced scrub practitioners are important in supporting inexperienced assistants. RAS can result in reduced distraction and increased concentration for the surgeon when he or she is supported by an experienced assistant or scrub practitioner. Conclusions: Our research suggests a need to pay greater attention to the training and skill mix of the team. To support effective teamwork, our research suggests that it is beneficial for surgeons to (1) encourage the team to communicate actions and concerns; (2) alert the attention of the assistant before issuing a request; and (3) acknowledge the scrub practitioner’s role in supporting inexperienced assistants. It is beneficial for the team to provide oral responses to the surgeon’s requests. Limitations: This study started after the trial, limiting impact on analysis of the trial. The small number of operations observed may mean that less frequent impacts of RAS were missed. Future work: Future research should include (1) exploring the transferability of guidance for effective teamwork to other surgical domains in which technology leads to the physical or perceptual separation of surgeon and team; (2) exploring the benefits and challenges of including realist methods in feasibility and pilot studies; (3) assessing the feasibility of using routine data to understand the impact of RAS on rare end points associated with patient safety; (4) developing and evaluating methods for whole-team training; and (5) evaluating the impact of different physical configurations of the robotic console and team members on teamwork.National Inst for Health Research (NIHR