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Main Title: Real-Time Detection of Freezing Motions in Parkinson's Patients for Adaptive Gait Phase Synchronous Cueing
Author(s): Dvorani, Ardit
Waldheim, Vivian
Jochner, Magdalena C. E.
Salchow-Hömmen, Christina
Meyer-Ohle, Jonas
Kühn, Andrea A.
Wenger, Nikolaus
Schauer, Thomas
Type: Article
Abstract: Parkinson's disease is the second most common neurodegenerative disease worldwide reducing cognitive and motoric abilities of affected persons. Freezing of Gait (FoG) is one of the severe symptoms that is observed in the late stages of the disease and considerably impairs the mobility of the person and raises the risk of falls. Due to the pathology and heterogeneity of the Parkinsonian gait cycle, especially in the case of freezing episodes, the detection of the gait phases with wearables is challenging in Parkinson's disease. This is addressed by introducing a state-automaton-based algorithm for the detection of the foot's motion phases using a shoe-placed inertial sensor. Machine-learning-based methods are investigated to classify the actual motion phase as normal or FoG-affected and to predict the outcome for the next motion phase. For this purpose, spatio-temporal gait and signal parameters are determined from the segmented movement phases. In this context, inertial sensor fusion is applied to the foot's 3D acceleration and rate of turn. Support Vector Machine (SVM) and AdaBoost classifiers have been trained on the data of 16 Parkinson's patients who had shown FoG episodes during a clinical freezing-provoking assessment course. Two clinical experts rated the video-recorded trials and marked episodes with festination, shank trembling, shuffling, or akinesia. Motion phases inside such episodes were labeled as FoG-affected. The classifiers were evaluated using leave-one-patient-out cross-validation. No statistically significant differences could be observed between the different classifiers for FoG detection (p>0.05). An SVM model with 10 features of the actual and two preceding motion phases achieved the highest average performance with 88.5 ± 5.8% sensitivity, 83.3 ± 17.1% specificity, and 92.8 ± 5.9% Area Under the Curve (AUC). The performance of predicting the behavior of the next motion phase was significantly lower compared to the detection classifiers. No statistically significant differences were found between all prediction models. An SVM-predictor with features from the two preceding motion phases had with 81.6 ± 7.7% sensitivity, 70.3 ± 18.4% specificity, and 82.8 ± 7.1% AUC the best average performance. The developed methods enable motion-phase-based FoG detection and prediction and can be utilized for closed-loop systems that provide on-demand gait-phase-synchronous cueing to mitigate FoG symptoms and to prevent complete motoric blockades.
Subject(s): Parkinson
freezing of gait
inertial measurement unit
machine learning
on-demand cueing
Issue Date: 6-Dec-2021
Date Available: 5-Jan-2022
Language Code: en
DDC Class: 610 Medizin und Gesundheit
620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Sponsor/Funder: BMBF, 16SV8168, Verbundprojekt: Mobilitätsassistent für Parkinsonpatienten - Mobil4Park -; Teilvorhaben: On-Demand Stimulationssystem mit Tele-Medizin-Funktion
DFG, 424778381, Behandlung motorischer Netzwerkstörungen mittels Neuromodulation
DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlin
Journal Title: Frontiers in Neurology
Publisher: Frontiers
Volume: 12
Article Number: 720516
Publisher DOI: 10.3389/fneur.2021.720516
EISSN: 1664-2295
TU Affiliation(s): Fak. 5 Verkehrs- und Maschinensysteme » Inst. Maschinenkonstruktion und Systemtechnik » FG Medizintechnik
Appears in Collections:Technische Universität Berlin » Publications

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