Dataset Information

Focused Vibrotactile Stimulation with Cueing Effect on Freezing of Gait in Parkinson's Disease: Two Case Reports.

ABSTRACT: Freezing of gait (FOG) is a common occurrence in patients with Parkinson's disease (PD) that leads to significant limitations in mobility and increases risk of falls. Focused vibrotactile stimulation and cueing are two methods used to alleviate motor symptoms, including FOG, in patients with PD. While effective on their own, the effect of combining both focused vibrotactile stimulation and cueing has yet to be investigated. Two patients, both with a history of PD, suffered from frequent FOG episodes that failed to respond adequately to medication. A novel vibrotactile stimulation device that delivered rhythmic kinesthetic stimuli onto the sternum successfully reduced FOG episodes in both patients and drastically improved their mobility as measured by the Timed Up and Go test. We found that a combination of focused vibrotactile stimulation and cueing was effective in reducing FOG episodes in two patients with PD. Further well-designed prospective studies are needed to confirm our observations.

SUBMITTER: Tan XS

PROVIDER: S-EPMC8490196 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

Focused Vibrotactile Stimulation with Cueing Effect on Freezing of Gait in Parkinson's Disease: Two Case Reports.

Tan Xiu Sheng XS Pierres Floyd F Dallman-Porter Alex A Hardie-Brown William W Kwon Kyum-Yil KY

Journal of movement disorders 20210908 3

Freezing of gait (FOG) is a common occurrence in patients with Parkinson's disease (PD) that leads to significant limitations in mobility and increases risk of falls. Focused vibrotactile stimulation and cueing are two methods used to alleviate motor symptoms, including FOG, in patients with PD. While effective on their own, the effect of combining both focused vibrotactile stimulation and cueing has yet to be investigated. Two patients, both with a history of PD, suffered from frequent FOG epis ...[more]

PMID: 34488302

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Project description: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.

Dataset Information

Focused Vibrotactile Stimulation with Cueing Effect on Freezing of Gait in Parkinson's Disease: Two Case Reports.

Publications

Focused Vibrotactile Stimulation with Cueing Effect on Freezing of Gait in Parkinson's Disease: Two Case Reports.

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