Automated patient-robot assignment for a robotic rehabilitation gym: a simplified simulation model.

BACKGROUND: A robotic rehabilitation gym can be defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, we propose an automated system that could dynamically assign patients to different robots within a session in order to optimize rehabilitation outcome. METHODS: As a first step toward implementing a practical patient-robot assignment system, we present a simplified mathematical model of a robotic rehabilitation gym. Mixed-integer nonlinear programming algorithms are used to find effective assignment and training solutions for multiple evaluation scenarios involving different numbers of patients and robots (5 patients and 5 robots, 6 patients and 5 robots, 5 patients and 7 robots), different training durations (7 or 12 time steps) and different complexity levels (whether different patients have different skill acquisition curves, whether robots have exit times associated with them). In all cases, the goal is to maximize total skill gain across all patients and skills within a session. RESULTS: Analyses of variance across different scenarios show that disjunctive and time-indexed optimization models significantly outperform two baseline schedules: staying on one robot throughout a session and switching robots halfway through a session. The disjunctive model results in higher skill gain than the time-indexed model in the given scenarios, and the optimization duration increases as the number of patients, robots and time steps increases. Additionally, we discuss how different model simplifications (e.g., perfectly known and predictable patient skill level) could be addressed in the future and how such software may eventually be used in practice. CONCLUSIONS: Though it involves unrealistically simple scenarios, our study shows that intelligently moving patients between different rehabilitation robots can improve overall skill acquisition in a multi-patient multi-robot environment. While robotic rehabilitation gyms are not yet commonplace in clinical practice, prototypes of them already exist, and our study presents a way to use intelligent decision support to potentially enable more efficient delivery of technologically aided rehabilitation.

Effects of the Auxivo CarrySuit occupational exoskeleton when carrying front and side loads on a treadmill.

Low-cost exoskeletons can effectively support workers in physically demanding jobs, but most such exoskeletons have been developed to support repetitive lifting or uncomfortable static postures. Very few low-cost exoskeletons have been designed to support walking while carrying heavy objects, which would be beneficial for jobs such as moving furniture and warehouse work. This paper thus presents a single-session lab evaluation of the Auxivo CarrySuit, a low-cost upper-body exoskeleton designed for carrying objects that would normally be held with the arms. Twenty participants carried four loads (box or two bags, 20 or 40 lb total weight) for 2 min each on a treadmill with and without the CarrySuit. Across all loads, the CarrySuit significantly reduced the mean electromyogram of the middle trapezius (partial eta-squared = 0.74 - from 16.1% to 8.8% of maximum voluntary contraction value) and anterior deltoid (partial eta-squared = 0.26 - from 3.0% to 1.1% of maximum voluntary contraction value) with no corresponding increase in lower back muscle activation. Furthermore, maximum heart rate and Ratings of Perceived Exertion were also reduced by the CarrySuit, and discomfort was shifted from the upper body to the legs. While arm EMG was not measured, it is likely that it was also reduced due to the unloading of the arms. The CarrySuit can thus be considered beneficial in the short term, though longer-term evaluations with actual workers are needed to determine practical benefits.

Effects of a Passive Back Support Exoskeleton when Lifting and Carrying Lumber Boards.

Passive back support exoskeletons, which support the human trunk using elements like springs and elastic bands, have demonstrated positive results in laboratory-based studies, but have seen significantly less field testing. As an intermediate step between generic lab evaluations and field tests, we conducted a single-session lab evaluation of the HeroWear Apex exoskeleton with mockup construction tasks: 20 adult men (without extensive construction experience) lifted, carried and raised lumber boards (265 cm length, up to 18 kg total load). The exoskeleton significantly reduced mean erector spinae electromyograms, with effect sizes (Cohen's d) ranging from -0.2 to -0.55 - corresponding to reductions of 5-25% relative to noexoskeleton electromyogram values. In asymmetric carrying tasks, the exoskeleton provided more assistance to the more heavily loaded erector spinae muscle. Additionally, in lifting tasks, the exoskeleton decreased trunk/hip flexion/extension range of motion and increased knee range of motion, indicating changes in lifting strategy. These results indicate potential exoskeleton benefits for lumber board carrying and will serve as the basis for further evaluations with workers in the field.Clinical Relevance- This study establishes that a passive back exoskeleton reduces erector spinae electromyograms by 525% when lifting and carrying lumber boards used in construction work.

Automated Patient-Robot Task Assignment in a Simulated Stochastic Rehabilitation Gym.

Rehabilitation after neurological injury can be provided by robots that help patients perform different exercises. Multiple such robots can be combined in a rehabilitation robot gym to allow multiple patients to perform a diverse range of exercises simultaneously. In pursuit of better multipatient supervision, we aim to develop an automated assignment system that assigns patients to different robots during a training session to maximize their skill development. Our previous work was designed for simplified simulated environments where each patient's skill development is known beforehand. The current work improves upon that work by changing the deterministic environment into a stochastic environment where part of the skill development is random and the assignment system must estimate each patient's predicted skill development using a neural network based on the patient's previous training success rate with that robot. These skill development estimates are used to create patient-robot assignments on a timestep-by-timestep basis to maximize the skill development of the patient group. Results from simplified simulation trials show that the schedules produced by our assignment system outperform multiple baseline schedules (e.g., schedules where patients never switch robots and schedules where patients only switch robots once halfway through the session). Additionally, we discuss how some of our simplifications could be addressed in the future.

Learning Skill Training Schedules From Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym.

A robotic gym with multiple rehabilitation robots allows multiple patients to exercise simultaneously under the supervision of a single therapist. The multi-patient training outcome can potentially be improved by dynamically assigning patients to robots based on monitored patient data. In this paper, we present an approach to learn dynamic patient-robot assignment from a domain expert via supervised learning. The dynamic assignment algorithm uses a neural network model to predict assignment priorities between patients. This neural network was trained using a synthetic dataset created in a simulated rehabilitation gym to imitate a domain expert's assignment behavior. The approach is evaluated in three simulated scenarios with different complexities and different expert behaviors meant to achieve different training objectives. Evaluation results show that our assignment algorithm imitates the expert's behavior with mean accuracies ranging from 75.4% to 84.5% across scenarios and significantly outperforms three baseline assignment methods with respect to mean skill gain. Our approach solves simplified patient training scheduling problems without complete knowledge about the patient skill acquisition dynamics and leverages human knowledge to learn automated assignment policies.

Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses.

Two people's physiological responses become more similar as those people talk or cooperate, a phenomenon called physiological synchrony. The degree of synchrony correlates with conversation engagement and cooperation quality, and could thus be used to characterize interpersonal interaction. In this study, we used a combination of physiological synchrony metrics and pattern recognition algorithms to automatically classify four different dyadic conversation scenarios: two-sided positive conversation, two-sided negative conversation, and two one-sided scenarios. Heart rate, skin conductance, respiration and peripheral skin temperature were measured from 16 dyads in all four scenarios, and individual as well as synchrony features were extracted from them. A two-stage classifier based on stepwise feature selection and linear discriminant analysis achieved a four-class classification accuracy of 75.0% in leave-dyad-out crossvalidation. Removing synchrony features reduced accuracy to 65.6%, indicating that synchrony is informative. In the future, such classification algorithms may be used to, e.g., provide real-time feedback about conversation mood to participants, with applications in areas such as mental health counseling and education. The approach may also generalize to group scenarios and adjacent areas such as cooperation and competition.

Short-term effects of the Auxivo LiftSuit during lifting and static leaning.

Back support exosuits can support workers in physically demanding jobs by reducing muscle load, which could reduce risk of work-related musculoskeletal disorders. This paper presents a two-session evaluation of a commercial exosuit, the Auxivo LiftSuit 1.1. In session 1, 17 participants performed single repetitions of lifting and static leaning tasks with and without the LiftSuit. In session 2, 10 participants performed 50 box lifting repetitions with and without the LiftSuit. In session 1, the exosuit was considered mildly to moderately helpful, and reduced erector spinae and middle trapezius electromyograms. In session 2, the exosuit was not considered helpful, but reduced the middle trapezius electromyogram and trunk and thigh ranges of motion. These effects are likely due to placement of elastic elements and excessive stiffness at the hips. Overall, the LiftSuit appears suboptimal for long-term use, though elastic elements on the upper back may reduce muscle activation in future exosuit designs.

Learning Dynamic Patient-Robot Task Assignment and Scheduling for A Robotic Rehabilitation Gym.

A robotic rehabilitation gym is a setup that allows multiple patients to exercise together using multiple robots. The effectiveness of training in such a group setting could be increased by dynamically assigning patients to specific robots. In this simulation study, we develop an automated system that dynamically makes patient-robot assignments based on measured patient performance to achieve optimal group rehabilitation outcome. To solve the dynamic assignment problem, we propose an approach that uses a neural network classifier to predict the assignment priority between two patients for a specific robot given their task success rate on that robot. The priority classifier is trained using assignment demonstrations provided by a domain expert. In the absence of real human data from a robotic gym, we develop a robotic gym simulator and create a synthetic dataset for training the classifier. The simulation results show that our approach makes effective assignments that yield comparable patient training outcomes to those obtained by the domain expert.