Objective: To examine the perceived value, benefits, drawbacks, and ideas for technology development and implementation of surface electromyography (sEMG) recordings in neurologic rehabilitation practice from clinical stakeholder perspectives. Design: A qualitative, phenomenological study was conducted. In-depth, semi-structured interviews and focus groups were completed. Sessions included questions about clinician perspectives and demonstrations of four sEMG systems to garner perceptions of specific system features. Setting: The study was conducted at four hospital systems in a large metropolitan area. Participants: 22 adult and pediatric physical therapists, occupational therapists, and physiatrists from inpatient, outpatient, and research settings took part in the study. Interventions: Not Applicable Main Outcome Measures: Interviews and focus groups were audio-recorded, transcribed verbatim, then coded for analysis into themes.

No abstract

In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbancerejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward controllers. We find that performance improves because feedback adapts, regardless of the hand used. We do not detect statistically significant differences in performance or learned controllers between hands. Adaptation to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise) explains observed performance improvements.

In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a continuous trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward elements of subjects' controllers. We find that handedness does not affect the learned controller and that controllers transfer between hands. Observed improvements in time-domain tracking performance may be attributed to adaptation of feedback to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise)

No abstract

Background: Assessments of human movement are clinically important. However, accurate measurements are often unavailable due to the need for expensive equipment or intensive processing. For orthotists and therapists, shank-to-vertical angle is one critical measure used to assess gait and guide prescriptions. Smartphone-based sensors may provide a widely available platform to expand access to this measurement. Objectives: Assess accuracy and repeatability of smartphone-based measurement of shank-to-vertical angle compared to marker-based 3D motion analysis. Study design: Repeated-measures. Methods: Four licensed clinicians (two physical therapists and two orthotists) measured shank-to-vertical angle during gait with a smartphone attached to the anterior or lateral shank surface of unimpaired adults. We compared the shank-to-vertical angle calculated from the smartphone’s inertial measurement unit to marker-based measurements. Each clinician completed three sessions/day on two days with each participant to assess repeatability. Results: Average absolute differences in shank-to-vertical angle measured with a smartphone versus marker-based 3D motion analysis during gait were 0.67 ± 0.25° and 4.89 ± 0.72°, with anterior or lateral smartphone positions, respectively. The inter- and intra-day repeatability of shank-to-vertical angle were within 2° for both smartphone positions. Conclusions: Smartphone sensors can be used to measure shank-to-vertical angle with high accuracy and repeatability during unimpaired gait, providing a widely available tool for quantitative gait assessments. Clinical relevance Smartphone sensors demonstrated high accuracy and repeatability for monitoring shank-to-vertical angle during gait. Measurement of shank-to-vertical angle from the front of the shank was more accurate than the side of the shank. Smartphones may expand access to quantitative assessments of gait.