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Prolonged Walking with a Wearable System Providing Intelligent Auditory Input in People with Parkinson's Disease.

ABSTRACT: Rhythmic auditory cueing is a well-accepted tool for gait rehabilitation in Parkinson's disease (PD), which can now be applied in a performance-adapted fashion due to technological advance. This study investigated the immediate differences on gait during a prolonged, 30?min, walk with performance-adapted (intelligent) auditory cueing and verbal feedback provided by a wearable sensor-based system as alternatives for traditional cueing. Additionally, potential effects on self-perceived fatigue were assessed. Twenty-eight people with PD and 13 age-matched healthy elderly (HE) performed four 30?min walks with a wearable cue and feedback system. In randomized order, participants received: (1) continuous auditory cueing; (2) intelligent cueing (10 metronome beats triggered by a deviating walking rhythm); (3) intelligent feedback (verbal instructions triggered by a deviating walking rhythm); and (4) no external input. Fatigue was self-scored at rest and after walking during each session. The results showed that while HE were able to maintain cadence for 30?min during all conditions, cadence in PD significantly declined without input. With continuous cueing and intelligent feedback people with PD were able to maintain cadence (p?=?0.04), although they were more physically fatigued than HE. Furthermore, cadence deviated significantly more in people with PD than in HE without input and particularly with intelligent feedback (both: p?=?0.04). In PD, continuous and intelligent cueing induced significantly less deviations of cadence (p?=?0.006). Altogether, this suggests that intelligent cueing is a suitable alternative for the continuous mode during prolonged walking in PD, as it induced similar effects on gait without generating levels of fatigue beyond that of HE.

SUBMITTER: Ginis P

PROVIDER: S-EPMC5382170 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Prolonged Walking with a Wearable System Providing Intelligent Auditory Input in People with Parkinson's Disease.

Ginis Pieter P Heremans Elke E Ferrari Alberto A Dockx Kim K Canning Colleen G CG Nieuwboer Alice A

Frontiers in neurology 20170406

Rhythmic auditory cueing is a well-accepted tool for gait rehabilitation in Parkinson's disease (PD), which can now be applied in a performance-adapted fashion due to technological advance. This study investigated the immediate differences on gait during a prolonged, 30 min, walk with performance-adapted (intelligent) auditory cueing and verbal feedback provided by a wearable sensor-based system as alternatives for traditional cueing. Additionally, potential effects on self-perceived fatigue wer ...[more]

PMID: 28428770

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Project description:BackgroundPostural instability and gait disability threaten the independence and well-being of people with Parkinson's disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson's disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson's disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population.MethodsPapers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i) did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii) did not include a control group or control condition; iii) were an abstract and/or included in the proceedings of a conference; or iv) were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed to meet the inclusion criteria. Included articles were assessed for methodological quality and relevant data from the papers were extracted and synthesized.ResultsQuality assessment of these included articles indicated that 31% were of low methodological quality, while 58% were of moderate methodological quality and 11% were of high methodological quality. All studies adopted a cross-sectional design and used a variety of sensor types and outcome measures to assess standing balance or walking stability in people with Parkinson's disease. Despite the typically low to moderate methodological quality, 81% of the studies reported differences in sensor-based measures of standing balance or walking stability between different groups of Parkinson's disease patients and/or healthy controls.ConclusionThese data support the use of wearable sensors for detecting differences in standing balance and walking stability between people with PD and controls. Further high-quality research is needed to better understand the utility of wearable sensors for the early identification of Parkinson's disease symptoms and for assessing falls risk in this population.Prospero registrationCRD42014010838.

Project description:Background: Currently, Parkinson's disease (PD) diagnosis is mainly based on medical history and physical examination, and there is no objective and consistent basis. By the time of diagnosis, the disease would have progressed to the middle and late stages. Pilot studies have shown that a unique smell was present in the skin sebum of PD patients. This increases the possibility of a noninvasive diagnosis of PD using an odor profile. Methods: Fast gas chromatography (GC) combined with a surface acoustic wave sensor with embedded machine learning (ML) algorithms was proposed to establish an artificial intelligent olfactory (AIO) system for the diagnosis of Parkinson's through smell. Sebum samples of 43 PD patients and 44 healthy controls (HCs) from Fourth Affiliated Hospital of Zhejiang University School of Medicine, China, were smelled by the AIO system. Univariate and multivariate methods were used to identify the significant volatile organic compound (VOC) features in the chromatograms. ML algorithms, including support vector machine, random forest (RF), k nearest neighbor (KNN), AdaBoost (AB), and Naive Bayes (NB), were used to distinguish PD patients from HC based on the VOC peaks in the chromatograms of sebum samples. Results: VOC peaks with average retention times of 5.7, 6.0, and 10.6 s, respectively, corresponding to octanal, hexyl acetate, and perillic aldehyde, were significantly different in PD and HC. The accuracy of the classification based on the significant features was 70.8%. Based on the odor profile, the classification had the highest accuracy and F1 of the five models with 0.855 from NB and 0.846 from AB, respectively, in the process of model establishing. The highest specificity and sensitivity of the five classifiers were 91.6% from NB and 91.7% from RF and KNN, respectively, in the evaluating set. Conclusions: The proposed AIO system can be used to diagnose PD through the odor profile of sebum. Using the AIO system is helpful for the screening and diagnosis of PD and is conducive to further tracking and frequent monitoring of the PD treatment process.

Project description:In Parkinson's disease (PD) self-directed movement, such as walking, is often found to be impaired while goal directed movement, such as catching a ball, stays relatively unaltered. This dichotomy is most clearly observed when sensory cueing techniques are used to deliver patterns of sound and/or light which in turn act as an external guide that improves gait performance. In this study we developed visual cues that could be presented in an immersive, interactive virtual reality (VR) environment. By controlling how the visual cues (black footprints) were presented, we created different forms of spatial and temporal information. By presenting the black footprints at a pre-specified distance apart we could recreate different step lengths (spatial cues) and by controlling when the black footprints changed color to red, we could convey information about the timing of the foot placement (temporal cues). A group of healthy controls (HC; N = 10) and a group of idiopathic PD patients (PD, N = 12) were asked to walk using visual cues that were tailored to their own gait performance [two spatial conditions (115% [N] and 130% [L] of an individual's baseline step length) and three different temporal conditions (spatial only condition [NT], 100 and 125% baseline step cadence)]. Both groups were found to be able to match their gait performance (step length and step cadence) to the information presented in all the visual cue conditions apart from the 125% step cadence conditions. In all conditions the PD group showed reduced levels of gait variability (p < 0.05) while the HC group did not decrease. For step velocity there was a significant increase in the temporal conditions, the spatial conditions and of the interaction between the two for both groups of participants (p < 0.05). The coefficient of variation of step length, cadence, and velocity were all significantly reduced for the PD group compared to the HC group. In conclusion, our results show how virtual footsteps presented in an immersive, interactive VR environment can significantly improve gait performance in participants with Parkinson's disease.

Project description:Freezing-of-gait (FOG) and impaired walking are common features of Parkinson's disease (PD). Provision of external stimuli (cueing) can improve gait, however, many cueing methods are simplistic, increase task loading or have limited utility in a real-world setting. Closed-loop (automated) somatosensory cueing systems have the potential to deliver personalised, discrete cues at the appropriate time, without requiring user input. Further development of cue delivery methods and FOG-detection are required to achieve this. In this feasibility study, we aimed to test if FOG-initiated vibration cues applied to the lower-leg via wearable devices can improve gait in PD, and to develop real-time FOG-detection algorithms. 17 participants with Parkinson's disease and daily FOG were recruited. During 1 h study sessions, participants undertook 4 complex walking circuits, each with a different intervention: continuous rhythmic vibration cueing (CC), responsive cueing (RC; cues initiated by the research team in response to FOG), device worn with no cueing (NC), or no device (ND). Study sessions were grouped into 3 stages/blocks (A-C), separated by a gap of several weeks, enabling improvements to circuit design and the cueing device to be implemented. Video and onboard inertial measurement unit (IMU) data were analyzed for FOG events and gait metrics. RC significantly improved circuit completion times demonstrating improved overall performance across a range of walking activities. Step frequency was significantly enhanced by RC during stages B and C. During stage C, > 10 FOG events were recorded in 45% of participants without cueing (NC), which was significantly reduced by RC. A machine learning framework achieved 83% sensitivity and 80% specificity for FOG detection using IMU data. Together, these data support the feasibility of closed-loop cueing approaches coupling real-time FOG detection with responsive somatosensory lower-leg cueing to improve gait in PD.

Project description:BackgroundBalance impairments, that lead to falls, are one of the main symptoms of Parkinson's disease (PD). Telerehabilitation is becoming more common for people with PD; however, balance is particularly challenging to assess and treat virtually. The feasibility and efficacy of virtual assessment and virtual treatment of balance in people with PD are unknown. The present study protocol has three aims: I) to determine if a virtual balance and gait assessment (instrumented L-shape mobility test) with wearable sensors can predict a gold-standard, in-person clinical assessment of balance, the Mini Balance Evaluation Systems Test (Mini-BESTest); II) to explore the effects of 12 sessions of balance telerehabilitation and unsupervised home exercises on balance, gait, executive function, and clinical scales; and III) to explore if improvements after balance telerehabilitation transfer to daily-life mobility, as measured by instrumented socks with inertial sensors worn for 7 days.MethodsThe TelePD Trial is a prospective, single-center, parallel-group, single-blind, pilot, randomized, controlled trial. This trial will enroll 80 eligible people with PD. Participants will be randomized at a 1:1 ratio into receiving home-based balance exercises in either: 1) balance telerehabilitation (experimental group, n = 40) or 2) unsupervised exercises (control group, n = 40). Both groups will perform 12 sessions of exercise at home that are 60 min long. The primary outcome will be Mini-BESTest. The secondary outcomes will be upper and lower body gait metrics from a prescribed task (instrumented L-shape mobility test); daily-life mobility measures over 7 days with wearable sensors in socks, instrumented executive function tests, and clinical scales. Baseline testing and 7 days of daily-life mobility measurement will occur before and after the intervention period.ConclusionThe TelePD Trial will be the first to explore the usefulness of using wearable sensor-based measures of balance and gait remotely to assess balance, the feasibility and efficacy of balance telerehabilitation in people with PD, and the translation of balance improvements after telerehabilitation to daily-life mobility. These results will help to develop a more effective home-based balance telerehabilitation and virtual assessment that can be used remotely in people with balance impairments.Trial registrationThis trial was prospectively registered on ClinicalTrials.gov (NCT05680597).

Dataset Information

Prolonged Walking with a Wearable System Providing Intelligent Auditory Input in People with Parkinson's Disease.

Publications

Prolonged Walking with a Wearable System Providing Intelligent Auditory Input in People with Parkinson's Disease.

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