Effects of overground gait training assisted by a wearable exoskeleton in patients with Parkinson's disease.

BACKGROUND: In the recent past, wearable devices have been used for gait rehabilitation in patients with Parkinson's disease. The objective of this paper is to analyze the outcome of a wearable hip orthosis whose assistance adapts in real time to the patient's gait kinematics via adaptive oscillators. In particular, this study focuses on a metric characterizing natural gait variability, i.e., the level of long-range autocorrelations (LRA) in series of stride durations. METHODS: Eight patients with Parkinson's disease (Hoehn and Yahr stages 1[Formula: see text]2.5) performed overground gait training three times per week for four consecutive weeks, assisted by a wearable hip orthosis. Gait was assessed based on performance metrics such as the hip range of motion, speed, stride length and duration, and the level of LRA in inter-stride time series assessed using the Adaptive Fractal Analysis. These metrics were measured before, directly after, and 1 month after training. RESULTS: After training, patients increased their hip range of motion, their gait speed and stride length, and decreased their stride duration. These improvements were maintained 1 month after training. Regarding long-range autocorrelations, the population's behavior was standardized towards a metric closer to the one of healthy individuals after training, but with no retention after 1 month. CONCLUSION: This study showed that an overground gait training with adaptive robotic assistance has the potential to improve key gait metrics that are typically affected by Parkinson's disease and that lead to higher prevalence of fall. TRIAL REGISTRATION: ClinicalTrials.gov Identifer NCT04314973. Registered on 11 April 2020.

Model of gait control in Parkinson's disease and prediction of robotic assistance.

Gait variability of healthy adults exhibits Long-Range Autocorrelations (LRA), meaning that the stride interval at any time statistically depends on previous gait cycles; and this dependency spans over several hundreds of strides. Previous works have shown that this property is altered in patients with Parkinson's disease, such that their gait pattern corresponds to a more random process. Here, we adapted a model of gait control to interpret the reduction in LRA that characterized patients in a computational framework. Gait regulation was modeled as a Linear-Quadratic-Gaussian control problem where the objective was to maintain a fixed velocity through the coordinated regulation of stride duration and length. This objective offers a degree of redundancy in the way the controller can maintain a given velocity, resulting in the emergence of LRA. In this framework, the model suggested that patients exploited less the task redundancy, likely to compensate for an increased stride-to-stride variability. Furthermore, we used this model to predict the potential benefit of an active orthosis on the gait pattern of patients. The orthosis was embedded in the model as a low-pass filter on the series of stride parameters. We show in simulations that, with a suitable level of assistance, the orthosis could help patients recovering a gait pattern with LRA comparable to that of healthy controls. Assuming that the presence of LRA in a stride series is a marker of healthy gait control, our study provides a rationale for developing gait assistance technology to reduce the fall risk associated with Parkinson's disease.