The Diagnostic Scope of Sensor-Based Gait Analysis in Atypical Parkinsonism: Further Observations

Gaßner, Heiko; Raccagni, Cecilia; Eskofier, Bjoern M.; Klucken, Jochen; Wenning, Gregor K.

doi:10.3389/fneur.2019.00005

Cited by 26 publications

(25 citation statements)

References 34 publications

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“…Gait characteristics were evaluated in a standardized gait test using an instrumented, sensor-based gait analysis system. This system consists of wearable SHIMMER sensors (Shimmer Research Ltd., Dublin, Ireland) laterally attached to the posterior portion of both shoes ( Supplementary Figure S1) [22]. Gait signals were recorded within a (tri-axial) accelerometer range of ± 6 g (sensitivity 300 mV/g), a gyroscope range of ± 500°/sec (sensitivity 2 mV/degree/sec), and a sampling rate of 102.4 Hz.…”

Section: Methodsmentioning

confidence: 99%

“…A meta-analysis based on 800 patients with Parkinson's disease and 854 healthy subjects provided evidence that a stride time variability larger than 2.4% discriminates healthy from pathological gait [36]. Sensor-based gait variability parameters were identified as very important objective measure in differentiating patients with atypical parkinsonian disorders (larger gait variability) from patients with sporadic Parkinson's disease [22]. In HD, it has been shown that the instrumented measure of variability in grasp forces strongly correlated with motor performance assessed by TFC (r = − 0.712) and TMS (r = 0.841) suggesting that movement variability is a key feature of motor impairment in HD [29].…”

Section: Sensor-based Gait Variability Parameters Reflect Disease Sevmentioning

confidence: 99%

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Gait variability as digital biomarker of disease severity in Huntington’s disease

et al. 2020

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Background Impaired gait plays an important role for quality of life in patients with Huntington's disease (HD). Measuring objective gait parameters in HD might provide an unbiased assessment of motor deficits in order to determine potential beneficial effects of future treatments. Objective To objectively identify characteristic features of gait in HD patients using sensor-based gait analysis. Particularly, gait parameters were correlated to the Unified Huntington's Disease Rating Scale, total motor score (TMS), and total functional capacity (TFC). Methods Patients with manifest HD at two German sites (n = 43) were included and clinically assessed during their annual ENROLL-HD visit. In addition, patients with HD and a cohort of age-and gender-matched controls performed a defined gait test (4 × 10 m walk). Gait patterns were recorded by inertial sensors attached to both shoes. Machine learning algorithms were applied to calculate spatio-temporal gait parameters and gait variability expressed as coefficient of variance (CV). Results Stride length (− 15%) and gait velocity (− 19%) were reduced, while stride (+ 7%) and stance time (+ 2%) were increased in patients with HD. However, parameters reflecting gait variability were substantially altered in HD patients (+ 17% stride length CV up to + 41% stride time CV with largest effect size) and showed strong correlations to TMS and TFC (0.416 ≤ r Sp ≤ 0.690). Objective gait variability parameters correlated with disease stage based upon TFC. Conclusions Sensor-based gait variability parameters were identified as clinically most relevant digital biomarker for gait impairment in HD. Altered gait variability represents characteristic irregularity of gait in HD and reflects disease severity.

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Section: Methodsmentioning

confidence: 99%

Section: Sensor-based Gait Variability Parameters Reflect Disease Sevmentioning

confidence: 99%

Gait variability as digital biomarker of disease severity in Huntington’s disease

et al. 2020

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“…Wearable sensors have already been used for gait analysis in different clinical applications. Previous studies reported the clinical validity of IMU based gait analysis and its feasibility to measure spatiotemporal gait parameters in Parkinson disease [19,20] and atypical parkinsonian disorders [21,22]. Also sensor-based gait analysis was used to measure impairment and disease severity in Huntington's disease [23,24].…”

Section: Inertial Wearable Sensors For Gait Analysismentioning

confidence: 99%

Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

Ibrahim

Küderle

Gaßner

et al. 2020

J NeuroEngineering Rehabil

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Background Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and consequently the whole body’s functional systems resulting in different gait disorders. Fatigue is the most common symptom in MS with a prevalence of 80%. Previous research studied the relation between fatigue and gait impairment using stationary gait analysis systems and short gait tests (e.g. timed 25 ft walk). However, wearable inertial sensors providing gait data from longer and continuous gait bouts have not been used to assess the relation between fatigue and gait parameters in MS. Therefore, the aim of this study was to evaluate the association between fatigue and spatio-temporal gait parameters extracted from wearable foot-worn sensors and to predict the degree of fatigue. Methods Forty-nine patients with MS (32 women; 17 men; aged 41.6 years, EDSS 1.0–6.5) were included where each participant was equipped with a small Inertial Measurement Unit (IMU) on each foot. Spatio-temporal gait parameters were obtained from the 6-min walking test, and the Borg scale of perceived exertion was used to represent fatigue. Gait parameters were normalized by taking the difference of averaged gait parameters between the beginning and end of the test to eliminate inter-individual differences. Afterwards, normalized parameters were transformed to principle components that were used as input to a Random Forest regression model to formulate the relationship between gait parameters and fatigue. Results Six principal components were used as input to our model explaining more than 90% of variance within our dataset. Random Forest regression was used to predict fatigue. The model was validated using 10-fold cross validation and the mean absolute error was 1.38 points. Principal components consisting mainly of stride time, maximum toe clearance, heel strike angle, and stride length had large contributions (67%) to the predictions made by the Random Forest. Conclusions The level of fatigue can be predicted based on spatio-temporal gait parameters obtained from an IMU based system. The results can help therapists to monitor fatigue before and after treatment and in rehabilitation programs to evaluate their efficacy. Furthermore, this can be used in home monitoring scenarios where therapists can monitor fatigue using IMUs reducing time and effort of patients and therapists.

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“…They collected data from 66 subjects in different age groups. Accelerometer and gyroscope were explored for gait signals from 60 patients having Parkinson's disease (24 females and 36 males) [25]. They computed several spatio-temporal gait parameters (e.g., gait velocity, stride length, maximum toe clearance, and cadence), then employed a machine learning algorithm for evaluation.…”

Section: Related Workmentioning

confidence: 99%

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Ahad

Ngo

Antar

et al. 2020

Sensors

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Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

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The Diagnostic Scope of Sensor-Based Gait Analysis in Atypical Parkinsonism: Further Observations

Cited by 26 publications

References 34 publications

Gait variability as digital biomarker of disease severity in Huntington’s disease

Gait variability as digital biomarker of disease severity in Huntington’s disease

Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

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