Lowering locomotion's metabolic cost: Walking and running require different gaits, with each type of motion putting a greater bias on different muscles and joints. Kim et al. developed a soft, fully portable, lightweight exosuit that is able to reduce the metabolic rate for both running and walking by assisting each motion via the hip extension (see the Perspective by Pons). A waist belt holds most of the mass, thus reducing the cost of carrying the suit. By tracking the motion of the user, the suit is able to switch modes between the two types of motion automatically. Science , this issue p. 668 ; see also p. 636

Objective: Soft robotic exosuits can improve the mechanics and energetics of walking after stroke. Building on this prior work, we evaluated the effects of the first prototype of a portable soft robotic exosuit. Methods: Exosuit-induced changes in the overground walking speed, distance, and energy expenditure of individuals post-stroke were evaluated statistically and compared to minimal clinically important difference scores. Results: Compared to walking without the exosuit worn, the <; 5 kg exosuit did not substantially modify speed, distance, or energy expenditure when worn unpowered. In contrast, when powered on to provide an average 22.87 ± 0.58 %bodyweight of paretic plantarflexor force assistance during stance phase and assist the paretic dorsiflexors during swing phase to reduce drop-foot, study participants walked a median 0.14 ± 0.06 m/s faster during the 10-meter walk test and traveled 32 ± 8 m farther during the six minute walk test (P <; 0.05). Conclusions: Individuals post-stroke can leverage the paretic plantarflexor and dorsiflexor assistance provided by soft robotic exosuits to achieve clinically-meaningful increases in speed and distance.

Background: The anterior-posterior ground reaction force (AP-GRF) and propulsion and braking point metrics derived from the AP-GRF time series are indicators of locomotor function across healthy and neurological diagnostic groups. In this paper, we describe the use of a minimal set of wearable inertial measurement units (IMUs) to indirectly measure the AP-GRFs generated during healthy and hemiparetic walking. Methods: Ten healthy individuals and five individuals with chronic post-stroke hemiparesis completed a 6-minute walk test over a walking track instrumented with six forceplates while wearing three IMUs securely attached to the pelvis, thigh, and shank. Subject-specific models driven by IMU-measured thigh and shank angles and an estimate of body acceleration provided by the pelvis IMU were used to generate indirect estimates of the AP-GRF time series. Propulsion and braking point metrics (i.e., peaks, peak timings, and impulses) were extracted from the IMU-generated time series. Peaks and impulses were expressed as % bodyweight (%bw) and peak timing was expressed as % stance phase (%sp). A 75%-25% split of 6-minute walk test data was used to train and validate the models. Indirect estimates of the AP-GRF time series and point metrics were compared to direct measurements made by the forceplates. Results: Indirect measurements of the AP-GRF time series approximated the direct measurements made by forceplates, with low error and high consistency in both the healthy (RMSE= 4.5%bw; R²= 0.93) and post-stroke (RMSE= 2.64%bw; R²= 0.90) cohorts. In the healthy cohort, the average errors between indirect and direct measurements of the peak propulsion magnitude, peak propulsion timing, and propulsion impulse point estimates were 2.37%bw, 0.67%sp, and 0.43%bw. In the post-stroke cohort, the average errors for these point estimates were 1.07%bw, 1.27%sp, and 0.31%bw. Average errors for the braking estimates were higher, but comparable. Conclusions: Accurate estimates of AP-GRF metrics can be generated using three strategically mounted IMUs and subject-specific calibrations. This study advances the development of point-of-care diagnostic systems that can catalyze the routine assessment and management of propulsion and braking locomotor deficits during rehabilitation.

We present the use of a single inertial measurement unit (IMU) worn on the thigh to produce stride-by-stride estimates of walking speed and its spatiotemporal determinants (i.e., stride time and stride length). Ten healthy and eight post-stroke individuals completed a 6-min walk test with an 18-camera motion capture system used for ground truth measurements. Subject-specific estimation models were trained to estimate walking speed using the polar radius extracted from phase portraits produced from the IMU-measured thigh angular position and velocity. Consecutive flexion peaks in the thigh angular position data were used to define each stride and compute stride times. Stride-by-stride estimates of walking speed and stride time were then used to compute stride length. In both the healthy and post-stroke cohorts, low error and high consistency were observed for the IMU estimates of walking speed (MAE < 0.035 m/s; ICC > 0.98), stride time (MAE < 30 ms; ICC > 0.97), and stride length (MAE < 0.037 m; ICC > 0.96). This study advances the use of a single wearable sensor to accurately estimate walking speed and its spatiotemporal determinants during both healthy and hemiparetic walking.

Habitual moderate intensity walking has disease-modifying benefits in Parkinson’s disease (PD). However, the lack of sustainable gait interventions that collectively promote sufficient intensity, daily amount, and quality of walking marks a critical gap in PD rehabilitation. In this randomized controlled trial (clinicaltrials.gov#: NCT05421624, registered on June 6, 2022), we demonstrate the effectiveness of a real-world walking intervention delivered using an autonomous music-based digital rhythmic auditory stimulation (RAS) system. In comparison to an active-control arm (N = 20) of moderately intense brisk walking, the autonomous RAS system used in the experimental arm (N = 21) amplified moderate-to-vigorous walking intensities, increased daily steps, and improved (reduced) gait variability. While regular engagement in real-world walking with or without RAS each cultivated habits for walking, only the RAS intervention yielded a combination of strengthened habits and improved gait outcomes. Findings from this study supported the use of a personalized, autonomous RAS gait intervention that is effective, habit-forming and translatable to real-world walking in individuals with PD.

A quantitative analysis of human gait patterns in space–time provides an opportunity to observe variability within and across individuals of varying motor capabilities. Impaired gait significantly affects independence and quality of life, and thus a large part of clinical research is dedicated to improving gait through rehabilitative therapies. Evaluation of these paradigms relies on understanding the characteristic differences in the kinematics and underlying biomechanics of impaired and unimpaired locomotion, which has motivated quantitative measurement and analysis of the gait cycle. Previous analysis has largely been limited to a statistical comparison of manually selected pointwise metrics identified through expert knowledge. Here, we use a recent statistical-geometric framework, elastic functional data analysis (FDA), to decompose kinematic data into continuous ‘amplitude’ (spatial) and ‘phase’ (temporal) components, which can then be integrated with established dimensionality reduction techniques. We demonstrate the utility of elastic FDA through two unsupervised applications to post-stroke gait datasets. First, we distinguish between unimpaired, paretic and non-paretic gait presentations. Then, we use FDA to reveal robust, interpretable groups of differential response to exosuit assistance. The proposed methods aim to benefit clinical practice for post-stroke gait rehabilitation, and more broadly, to automate the quantitative analysis of motion.