There currently exist no practical tools to identify functional movements in the upper extremities (UEs). This absence has limited the precise therapeutic dosing of patients recovering from stroke. In this proof-of-principle study, we aimed to develop an accurate approach for classifying UE functional movement primitives, which comprise functional movements. Data were generated from inertial measurement units (IMUs) placed on upper body segments of older healthy individuals and chronic stroke patients. Subjects performed activities commonly trained during rehabilitation after stroke. Data processing involved the use of a sliding window to obtain statistical descriptors, and resulting features were processed by a Hidden Markov Model (HMM). The likelihoods of the states, resulting from the HMM, were segmented by a second sliding window and their averages were calculated. The final predictions were mapped to human functional movement primitives using a Logistic Regression algorithm. Algorithm performance was assessed with a leave-one-out analysis, which determined its sensitivity, specificity, and positive and negative predictive values for all classified primitives. In healthy control and stroke participants, our approach identified functional movement primitives embedded in training activities with, on average, 80% precision. This approach may support functional movement dosing in stroke rehabilitation.

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Introduction: Primary deficits in individuals with cerebellar degeneration include ataxia, unstable gait, and incoordination. Balance training is routinely recommended to improve function whereas little is known regarding aerobic training. Objective: To determine the feasibility of conducting a randomized trial comparing balance and aerobic training in individuals with cerebellar degeneration. Design: Assessor blinded randomized control phase I trial. Setting: Assessments in medical center, home training. Participants: Twenty participants with cerebellar degeneration were randomized to home balance or aerobic training. Intervention: Aerobic training consisted of 4 weeks of stationary bicycle training, five times per week for 30-minute sessions. Home balance training consisted of performing the same duration of easy, moderate, and/or hard exercises. Outcome Measures: Scale for the Assessment and Rating of Ataxia (SARA), maximal oxygen consumption (VO 2 max), Dynamic Gait Index, Timed Up and Go, gait speed.Results: All 20 participants completed assigned training with no major adverse events. Seven of each group attained target training duration, frequency, and intensity. Although both groups had significant improvements in ataxia severity, balance, and gait measures, there were greater improvements in individuals who performed aerobic training in ataxia severity and maximal oxygen consumption when compared to balance training. The effect size for these outcome measures was determined to be large, indicating a phase II trial comparing the benefits of aerobic and balance training was feasible and required 26 participants per group. Improvements in SARA score and VO 2 max remained in the aerobic training group at 3 months posttraining, but these improvements were trending back to baseline. In contrast, all balance group measures for pretraining and 3 months posttraining were statistically similar. Conclusions: A phase II trial comparing balance and aerobic training in individuals with cerebellar degeneration is feasible. Benefits trended back toward baseline after training stopped, although benefits of longer duration exercise programs still need to be determined.

The boundary-based assist-as-needed (BAAN) force field is widely used in robotic rehabilitation and has shown promising results in improving trunk control and postural stability. However, the fundamental understanding of how the BAAN force field affects the neuromuscular control remains unclear. In this study, we investigate how the BAAN force field impacts muscle synergy in the lower limbs during standing posture training. We integrated virtual reality (VR) into a cable-driven Robotic Upright Stand Trainer (RobUST) to define a complex standing task that requires both reactive and voluntary dynamic postural control. Ten healthy subjects were randomly assigned to two groups. Each subject performed 100 trials of the standing task with or without assistance from the BAAN force field provided by RobUST. The BAAN force field significantly improved balance control and motor task performance. Our results also indicate that the BAAN force field reduced the total number of lower limb muscle synergies while concurrently increasing the synergy density (i.e., number of muscles recruited in each synergy) during both reactive and voluntary dynamic posture training. This pilot study provides fundamental insights into understanding the neuromuscular basis of the BAAN robotic rehabilitation strategy and its potential for clinical applications. In addition, we expanded the repertoire of training with RobUST that integrates both perturbation training and goal-oriented functional motor training within a single task. This approach can be extended to other rehabilitation robots and training approaches with them.