Muscle adaptations in acute SCI following overground exoskeleton + FES training: A pilot study.

Objective: To evaluate the combined effects of robotic exoskeleton and functional electrical stimulation (FES) training on muscle composition during over-ground gait training in persons with acute spinal cord injury (SCI). Design: Randomized crossover pilot study. Setting: Inpatient-rehabilitation Hospital. Participants: Six individuals with acute SCI. Intervention: Participants were randomized to either receive training with the Ekso® Bionics exoskeleton combined with FES in addition to standard-of-care or standard-of-care alone. Outcome measures: The main outcome measures for the study were quantified using magnetic resonance imaging (MRI), specifically, lower extremity muscle volume and intramuscular adipose tissue (IMAT). Static balance and fall risk were assessed using the Berg Balance Scale. Results: Significant improvements were observed in muscle volume in the exoskeleton intervention group when compared to only standard-of-care (p < 0.001). There was no significant difference between the groups in IMAT even though the intervention group saw a reduction in IMAT that trended towards statistical significance (p = 0.07). Static balance improved in both groups, with greater improvements seen in the intervention group. Conclusions: Early intervention with robotic exoskeleton may contribute to improved muscle function measured using MRI in individuals with acute SCI.

Validation of Amazon Halo Movement: a smartphone camera-based assessment of movement health.

Movement health is understanding our body's ability to perform movements during activities of daily living such as lifting, reaching, and bending. The benefits of improved movement health have long been recognized and are wide-ranging from improving athletic performance to helping ease of performing simple tasks, but only recently has this concept been put into practice by clinicians and quantitatively studied by researchers. With digital health and movement monitoring becoming more ubiquitous in society, smartphone applications represent a promising avenue for quantifying, monitoring, and improving the movement health of an individual. In this paper, we validate Halo Movement, a movement health assessment which utilizes the front-facing camera of a smartphone and applies computer vision and machine learning algorithms to quantify movement health and its sub-criteria of mobility, stability, and posture through a sequence of five exercises/activities. On a diverse cohort of 150 participants of various ages, body types, and ability levels, we find moderate to strong statistically significant correlations between the Halo Movement assessment overall score, metrics from sensor-based 3D motion capture, and scores from a sequence of 13 standardized functional movement tests. Further, the smartphone assessment is able to differentiate regular healthy individuals from professional movement athletes (e.g., dancers, cheerleaders) and from movement impaired participants, with higher resolution than that of existing functional movement screening tools and thus may be more appropriate than the existing tests for quantifying functional movement in able-bodied individuals. These results support using Halo Movement's overall score as a valid assessment of movement health.