Soft robotic apparel to avert freezing of gait in Parkinson's disease.

Freezing of gait (FoG) is a profoundly disruptive gait disturbance in Parkinson's disease, causing unintended stops while walking. Therapies for FoG reveal modest and transient effects, resulting in a lack of effective treatments. Here we show proof of concept that FoG can be averted using soft robotic apparel that augments hip flexion. The wearable garment uses cable-driven actuators and sensors, generating assistive moments in concert with biological muscles. In this n-of-1 trial with five repeated measurements spanning 6 months, a 73-year-old male with Parkinson's disease and substantial FoG demonstrated a robust response to robotic apparel. With assistance, FoG was instantaneously eliminated during indoor walking (0% versus 39 ± 16% time spent freezing when unassisted), accompanied by 49 ± 11 m (+55%) farther walking compared to unassisted walking, faster speeds (+0.18 m s-1) and improved gait quality (-25% in gait variability). FoG-targeting effects were repeatable across multiple days, provoking conditions and environment contexts, demonstrating potential for community use. This study demonstrated that FoG was averted using soft robotic apparel in an individual with Parkinson's disease, serving as an impetus for technological advancements in response to this serious yet unmet need.

Individualized Learning-Based Ground Reaction Force Estimation in People Post-Stroke Using Pressure Insoles.

Stroke is a leading cause of gait disability that leads to a loss of independence and overall quality of life. The field of clinical biomechanics aims to study how best to provide rehabilitation given an individual's impairments. However, there remains a disconnect between assessment tools used in biomechanical analysis and in clinics. In particular, 3-dimensional ground reaction forces (3D GRFs) are used to quantify key gait characteristics, but require lab-based equipment, such as force plates. Recent efforts have shown that wearable sensors, such as pressure insoles, can estimate GRFs in real-world environments. However, there is limited understanding of how these methods perform in people post-stroke, where gait is highly heterogeneous. Here, we evaluate three subject-specific machine learning approaches to estimate 3D GRFs with pressure insoles in people post-stroke across varying speeds. We find that a Convolutional Neural Network-based approach achieves the lowest estimation errors of 0.75 ± 0.24, 1.13 ± 0.54, and 4.79 ± 3.04 % bodyweight for the medio-lateral, antero-posterior, and vertical GRF components, respectively. Estimated force components were additionally strongly correlated with the ground truth measurements ( ). Finally, we show high estimation accuracy for three clinically relevant point metrics on the paretic limb. These results suggest the potential for an individualized machine learning approach to translate to real-world clinical applications.

Ankle-targeted exosuit resistance increases paretic propulsion in people post-stroke.

BACKGROUND: Individualized, targeted, and intense training is the hallmark of successful gait rehabilitation in people post-stroke. Specifically, increasing use of the impaired ankle to increase propulsion during the stance phase of gait has been linked to higher walking speeds and symmetry. Conventional progressive resistance training is one method used for individualized and intense rehabilitation, but often fails to target paretic ankle plantarflexion during walking. Wearable assistive robots have successfully assisted ankle-specific mechanisms to increase paretic propulsion in people post-stroke, suggesting their potential to provide targeted resistance to increase propulsion, but this application remains underexamined in this population. This work investigates the effects of targeted stance-phase plantarflexion resistance training with a soft ankle exosuit on propulsion mechanics in people post-stroke. METHODS: We conducted this study in nine individuals with chronic stroke and tested the effects of three resistive force magnitudes on peak paretic propulsion, ankle torque, and ankle power while participants walked on a treadmill at their comfortable walking speeds. For each force magnitude, participants walked for 1 min while the exosuit was inactive, 2 min with active resistance, and 1 min with the exosuit inactive, in sequence. We evaluated changes in gait biomechanics during the active resistance and post-resistance sections relative to the initial inactive section. RESULTS: Walking with active resistance increased paretic propulsion by more than the minimal detectable change of 0.8 %body weight at all tested force magnitudes, with an average increase of 1.29 ± 0.37 %body weight at the highest force magnitude. This improvement corresponded to changes of 0.13 ± 0.03 N m kg- 1 in peak biological ankle torque and 0.26 ± 0.04 W kg- 1 in peak biological ankle power. Upon removal of resistance, propulsion changes persisted for 30 seconds with an improvement of 1.49 ± 0.58 %body weight after the highest resistance level and without compensatory involvement of the unresisted joints or limb. CONCLUSIONS: Targeted exosuit-applied functional resistance of paretic ankle plantarflexors can elicit the latent propulsion reserve in people post-stroke. After-effects observed in propulsion highlight the potential for learning and restoration of propulsion mechanics. Thus, this exosuit-based resistive approach may offer new opportunities for individualized and progressive gait rehabilitation.

Design and evaluation of an independent 4-week, exosuit-assisted, post-stroke community walking program.

Chronic impairment in the paretic ankle following stroke often requires that individuals use compensatory patterns such as asymmetric propulsion to achieve effective walking speeds needed for community engagement. Ankle exosuit assistance can provide ankle biomechanical benefit in the lab, but such environments inherently limit the amount of practice available. Community walking studies without exosuits can provide massed practice and benefit walking speed but are limited in their ability to assist proper mechanics. In this study, we combined the positive aspects of community training with those of exosuit assistance. We developed and evaluated a community Robotic Exosuit Augmented Locomotion (cREAL) program. Four participants in the chronic stage of stroke independently used our community ankle exosuit for walking in the community 3-5 days/week for 4 weeks. We performed lab evaluations before and after the 4-week program. Two participants significantly improved their unassisted paretic propulsion by an average of 27% after the program and walked on average 4001 steps/day more in the week following the program. Despite the small number of participants, this study provides preliminary evidence for the potential of exosuits to augment gait training and rehabilitation in the community.

Accuracy of Activity Trackers in Parkinson Disease: Should We Prescribe Them?

Background: Wearable, consumer-grade activity trackers have become widely available as a means of monitoring physical activity in the form of step counts. However, step counts may not be accurate in persons with Parkinson disease (PD) due to atypical gait characteristics. Objective: This study aimed to investigate the accuracy of 4 consumer-grade activity trackers in individuals with PD while ambulating during continuous and discontinuous walking tasks. Design: This study used a cross-sectional design. Methods: Thirty-three persons with PD (Hoehn & Yahr stages 1-3) donned 4 models of activity trackers on the less affected side of their bodies. Participants performed 2 continuous walking tasks (2-minute walk tests at comfortable and fast speeds) and 2 discontinuous walking tasks (a simulated household course and an obstacle negotiation course) in an outpatient setting. Bland-Altman plots and intraclass correlation coefficients [ICC(2,1)] were computed as a measure of agreement between actual steps taken (reference standard: video recording) and steps recorded by each tracker. Results: The accuracy of the activity trackers varied widely, with ICCs ranging from -0.03 to 0.98. Overall, the most accurate device across all tasks was the Fitbit Zip, and the least accurate was the Jawbone Up Move during the simulated household course. All activity trackers were more accurate for continuous walking tasks compared with discontinuous walking tasks. Waist-mounted devices were more accurate than wrist-mounted devices with continuous tasks. Bland-Altman plots revealed that all activity trackers underestimated step counts. Limitations: All walking tasks were measured over relatively short distances. Conclusions: In persons with mild-to-moderate PD, waist-worn activity trackers may be prescribed to monitor bouts of continuous walking with reasonable accuracy; however, activity trackers have little utility in monitoring discontinuous walking common in household settings.