: High intensity training may enhance neuroplasticity after stroke; however, gait deficits limit the ability to achieve and sustain high walking training intensities. We hypothesize that soft robotic exosuits can facilitate speed-based gait training at higher intensities and longer durations, resulting in a corresponding increase in circulating brain-derived neurotrophic factor (BDNF). Results: Eleven individuals >6-mo poststroke completed a two-session, pilot randomized crossover trial (NCT05138016). Maximum training speed (Δ: 0.07±0.03 m/s), duration (Δ: 2.07±0.88 min), and intensity (VO2 peak, Δ: 1.75±0.60 ml-O2/kg/min) significantly increased (p<0.05) during exosuit-augmented training compared to no-exosuit training. Post-session increases in BDNF (Δ: 5.96±2.27 ng/ml, p=0.03) were observed only after exosuit-augmented training. Biomechanical changes were not observed after exosuit-augmented training; however, a deterioration in gait propulsion symmetry (%Δ: -5±2 %) and an increase in nonparetic propulsion (Δ: 0.9±0.3 %bw) were observed (p<0.05) after no-exosuit training. Conclusion: Soft robotic exosuits facilitate faster, longer duration, and higher intensity walking training associated with enhanced neuroplasticity.

Plantarflexor central drive is a promising biomarker of neuromotor impairment; however, routine clinical assessment is hindered by the unavailability of force measurement systems with integrated neurostimulation capabilities. In this study, we evaluate the accuracy of a portable, neurostimulation-integrated, plantarflexor force measurement system we developed to facilitate the assessment of plantarflexor neuromotor function in clinical settings. Two experiments were conducted with the Central Drive System (CEDRS). To evaluate accuracy, experiment #1 included 16 neurotypical adults and used intra-class correlation (ICC_2,1) to test agreement of plantarflexor strength capacity measured with CEDRS versus a stationary dynamometer. To evaluate validity, experiment #2 added 26 individuals with post-stroke hemiparesis and used one-way ANOVAs to test for between-limb differences in CEDRS’ measurements of plantarflexor neuromotor function, comparing neurotypical, non-paretic, and paretic limb measurements. The association between paretic plantarflexor neuromotor function and walking function outcomes derived from the six-minute walk test (6MWT) were also evaluated. CEDRS’ measurements of plantarflexor neuromotor function showed high agreement with measurements made by the stationary dynamometer (ICC = 0.83, p < 0.001). CEDRS’ measurements also showed the expected between-limb differences (p’s < 0.001) in maximum voluntary strength (Neurotypical: 76.21 ± 13.84 ft-lbs., Non-paretic: 56.93 ± 17.75 ft-lbs., and Paretic: 31.51 ± 14.08 ft-lbs.), strength capacity (Neurotypical: 76.47 ± 13.59 ft-lbs., Non-paretic: 64.08 ± 14.50 ft-lbs., and Paretic: 44.55 ± 14.23 ft-lbs.), and central drive (Neurotypical: 88.73 ± 1.71%, Non-paretic: 73.66% ± 17.74%, and Paretic: 52.04% ± 20.22%). CEDRS-measured plantarflexor central drive was moderately correlated with 6MWT total distance (r = 0.69, p < 0.001) and distance-induced changes in speed (r = 0.61, p = 0.002). CEDRS is a clinician-operated, portable, neurostimulation-integrated force measurement platform that produces accurate measurements of plantarflexor neuromotor function that are associated with post-stroke walking ability.

The dynamic motor control index is an emerging biomarker of age-related neuromuscular impairment. To date, it has been computed by quantifying the co-activity of eleven lower limb muscles. Because clinics that routinely employ electromyography typically collect from fewer muscles, a reduced muscle sensor set may improve the clinical usability of this metric of motor control. This study aimed to test if commonly used eight- and five-muscle electromyography (EMG) sensor sets produce similar dynamic motor control indices as the previously examined eleven-muscle sensor set and similarly differentiate across age subgroups. EMG data were collected during treadmill walking from 36 adults separated into young (N = 18, <35 yrs.), young-old (N = 13, 65–74 yrs.), and old-old (N = 5, ≥75 yrs.) subgroups. Dynamic motor control indices generated using the sensor set with eleven muscles correlated with the eight-muscle set (R² = 0.70) but not the five-muscle set (R² = 0.30). Regression models using the eleven-muscle (χ²(4) = 10.62, p = 0.031, Nagelkerke R² = 0.297) and eight-muscle (χ²(4) = 9.418, p = 0.051, Nagelkerke R² = 0.267) sets were significant and approaching significance, respectively, whereas the model for the five-muscle set was not significant (p = 0.663, Nagelkerke R² = 0.073). In both the eleven-muscle (Wald χ² = 5.16, p = 0.023, OR = 1.26) and eight-muscle models (Wald χ² = 4.20, p = 0.04, OR = 1.19), a higher index significantly predicted being in the young group compared to the old-old group. Age-related differences in the neuromuscular control of walking can be detected using dynamic motor control indices generated using eleven- and eight-muscle sensor sets, increasing clinical usability of the dynamic motor control index.

Infant development is characterized by the acquisition of key gross motor milestones—sitting, crawling, standing, and walking—in the first year of life. Each new skill brings with it new opportunit...

Background: Quantifying movement during early mobility interventions is important for evaluating intervention participation and progression in toddlers with Down syndrome (DS), but behavioral video coding is time and labor-intensive. This study established and evaluated an activity count cutpoint for distinguishing moderate-to-vigorous physical activity (MVPA) and sedentary-to-light physical activity (SLPA) in toddlers with DS during a home-based mobility intervention using a wearable accelerometer integrated into a body-weight support harness system. Methods: Video recordings behaviorally coded for MVPA and SLPA were synchronized with accelerometer activity counts binned in 5 s epochs. Receiver operating characteristic curve analysis was used to determine both group-derived and participant-specific activity count cutpoints. Results: The optimal group-derived cutpoint was 141 activity counts per 5 s, demonstrating strong classification performance (AUC = 0.903), with 94.4% sensitivity, 70.2% specificity, and an overall classification accuracy of 79.2%. Participant-specific cutpoints resulted in only minimal improvements in classification performance. Conclusions: These findings suggest that using individualized cutpoints may not be necessary for classifying MVPA in toddlers with DS during this intervention, though replication in a larger sample is warranted. A standardized activity count cutpoint may provide a practical approach for objectively monitoring activity during home-based mobility interventions.