BeatWalk: Personalized Music-Based Gait Rehabilitation in Parkinson’s Disease

Taking regular walks when living with Parkinson’s disease (PD) has beneficial effects on movement and quality of life. Yet, patients usually show reduced physical activity compared to healthy older adults. Using auditory stimulation such as music can facilitate walking but patients vary significantly in their response. An individualized approach adapting musical tempo to patients’ gait cadence, and capitalizing on these individual differences, is likely to provide a rewarding experience, increasing motivation for walk-in PD. We aim to evaluate the observance, safety, tolerance, usability, and enjoyment of a new smartphone application. It was coupled with wearable sensors (BeatWalk) and delivered individualized musical stimulation for gait auto-rehabilitation at home. Forty-five patients with PD underwent a 1-month, outdoor, uncontrolled gait rehabilitation program, using the BeatWalk application (30 min/day, 5 days/week). The music tempo was being aligned in real-time to patients’ gait cadence in a way that could foster an increase up to +10% of their spontaneous cadence. Open-label evaluation was based on BeatWalk use measures, questionnaires, and a six-minute walk test. Patients used the application 78.8% (±28.2) of the prescribed duration and enjoyed it throughout the program. The application was considered “easy to use” by 75% of the patients. Pain, fatigue, and falls did not increase. Fear of falling decreased and quality of life improved. After the program, patients improved their gait parameters in the six-minute walk test without musical stimulation. BeatWalk is an easy to use, safe, and enjoyable musical application for individualized gait rehabilitation in PD. It increases “walk for exercise” duration thanks to high observance.This research was supported by a European grant: BeatHealth: Health and Wellness on the Beat for VC, DD, CL, AGi, VD, RV, EH, ED, ML, BB, and SB (EU FP7-ICT contract #610633)

The effect of normal load force and roughness on the dynamic traction developed at the shoe-surface interface in tennis

During tennis-specific movements, such as accelerating and side stepping, the dynamic traction provided by the shoe-surface combination plays an important role in the injury risk and performance of the player. Acrylic hard court tennis surfaces have been reported to have increased injury occurrence, partly caused by increased traction that developed at the shoe-surface interface. Often mechanical test methods used for the testing and categorisation of playing surfaces do not tend to simulate loads occurring during participation on the surface, and thus are unlikely to predict the human response to the surface. A traction testing device, discussed in this paper, has been used to mechanically measure the dynamic traction force between the shoe and the surface under a range of normal loading conditions that are relevant to real-life play. Acrylic hard court tennis surfaces generally have a rough surface topography, due to their sand and acrylic paint mixed top coating. Surface micro-roughness will influence the friction mechanisms present during viscoelastic contacts, as found in footwear-surface interactions. This paper aims to further understand the influence micro-roughness and normal force has on the dynamic traction that develops at the shoe-surface interface on acrylic hard court tennis surfaces. The micro-roughness and traction of a controlled set of acrylic hard court tennis surfaces have been measured. The relationships between micro-roughness, normal force, and traction force are discussed. © 2013 The Author(s)

Reply to: “Is Gait Dysfunction Prominent Sign of Isolated Rapid Eye Movement Sleep Behavior Disorder?”

International audienc

Why do we move to the beat? A multi-scale approach, from physical principles to brain dynamics

International audienceHumans' ability to synchronize movement with auditory rhythms relies on motor networks, such as cortical areas, basal ganglia and the cerebellum, which also participate in rhythm perception and movement production. Current research has provided insights into the dependence of this action-perception coupling upon the entrainment of neuronal activity by external rhythms. At a physical level, advances on wearable robotics have enriched our understanding of the dynamical properties of the locomotor system showing evidences of mechanical entrainment. Here we defend the view that modelling brain and locomotor oscillatory activities as dynamical systems, at both neural and physical levels, provides a unified theoretical framework for the understanding of externally driven rhythmic entrainment of biological systems. To better understand the underlying mechanisms of this multi-level entrainment during locomotion, we review in a common framework the core questions related to the dynamic properties of biological oscillators and the neural bases of auditory-motor synchronization. Illustrations of our approach, using personalized auditory stimulation, to gait rehabilitation in Parkinson disease and to manipulation of runners' kinematics are presented

Classifying Idiopathic Rapid Eye Movement Sleep Behavior Disorder, Controls, and Mild Parkinson's Disease Using Gait Parameters

International audienceBackgroundSubtle gait changes associated with idiopathic rapid eye movement sleep behavior disorder (iRBD) could allow early detection of subjects with future synucleinopathies.ObjectiveThe aim of this study was to create a multiclass model, using statistical learning from probability distribution of gait parameters, to distinguish between patients with iRBD, healthy control subjects (HCs), and patients with Parkinson's disease (PD).MethodsGait parameters were collected in 21 participants with iRBD, 21 with PD, and 21 HCs, matched for age, sex, and education level. Lasso sparse linear regression explored gait features able to classify the three groups.ResultsThe final model classified iRBD from HCs and from patients with PD equally well, with 95% accuracy, 100% sensitivity, and 90% specificity.ConclusionsGait parameters and a pretrained statistical model can robustly distinguish participants with iRBD from HCs and patients with PD. This could be used to screen subjects with future synucleinopathies in the general population and to identify a conversion threshold to PD