Detection and prediction of freezing of gait with wearable sensors in Parkinson’s disease

Zhang, Wei; Sun, Hong; Huang, Debin; Zhang, Zixuan; Li, Jinyu; Wu, Chan; Sun, Yingying; Gong, Mengyi; Wang, Zhi; Sun, Chao; Cui, Guiyun; Guo, Yuzhu; Chan, Piu

doi:10.1007/s10072-023-07017-y

Neurol Sci

2023

DOI: 10.1007/s10072-023-07017-y

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Detection and prediction of freezing of gait with wearable sensors in Parkinson’s disease

Wei Zhang,

Hong Sun,

Debin Huang

et al.

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2024

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Cited by 4 publications

References 121 publications

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Deep learning algorithms for detecting freezing of gait in Parkinson’s disease: A cross-dataset study

Sigcha,

Borzì,

Olmo

2024

Expert Systems with Applications

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Deep learning algorithms for detecting freezing of gait in Parkinson’s disease: A cross-dataset study

Sigcha,

Borzì,

Olmo

2024

Expert Systems with Applications

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A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects

Salomon,

Gazit,

Ginis

et al. 2024

Nat Commun

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Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson’s disease. During a FOG episode, patients report that their feet are suddenly and inexplicably “glued” to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification.

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Unveiling the Unpredictable in Parkinson’s Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life

Zampogna,

Borzì,

Rinaldi

et al. 2024

Bioengineering

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Background: Dyskinesias and freezing of gait are episodic disorders in Parkinson’s disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters. Methods: Seventy-one patients with Parkinson’s disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait. Results: The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage. Conclusions: A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson’s disease.

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Detection and prediction of freezing of gait with wearable sensors in Parkinson’s disease

Cited by 4 publications

References 121 publications

Deep learning algorithms for detecting freezing of gait in Parkinson’s disease: A cross-dataset study

Deep learning algorithms for detecting freezing of gait in Parkinson’s disease: A cross-dataset study

A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects

Unveiling the Unpredictable in Parkinson’s Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life

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