Detection of Freezing of Gait using Convolutional Neural Networks and Data from Lower Limb Motion Sensors

<div>Freezing of Gait is the most disabling gait disturbance in Parkinson’s disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson’s disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.</div>

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219. A proposed model for understanding the phenomenon of freezing of gait in parkinson’s disease and the potential role of functional neuroimaging

Journal of Clinical Neuroscience ◽

10.1016/j.jocn.2008.07.046 ◽

2009 ◽

Vol 16 (3) ◽

pp. 475

Author(s):

Simon J.G. Lewis

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Potential Role ◽

Functional Neuroimaging ◽

Freezing Of Gait ◽

Proposed Model

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Movement smoothness during a functional mobility task in subjects with Parkinson’s disease and freezing of gait – an analysis using inertial measurement units

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-019-0579-8 ◽

2019 ◽

Vol 16 (1) ◽

Cited By ~ 2

Author(s):

Camila Pinto ◽

Clarissa Pedrini Schuch ◽

Gustavo Balbinot ◽

Ana Paula Salazar ◽

Ewald Max Hennig ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Freezing Of Gait ◽

Functional Mobility ◽

Inertial Measurement Units ◽

Inertial Measurement ◽

Movement Smoothness ◽

Measurement Units

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Quantitative and qualitative gait assessments in Parkinson’s disease patients

Vojnosanitetski pregled ◽

10.2298/vsp130422012d ◽

2014 ◽

Vol 71 (9) ◽

pp. 809-816 ◽

Cited By ~ 6

Author(s):

Milica Djuric-Jovicic ◽

Nenad Jovicic ◽

Sasa Radovanovic ◽

Nikola Kresojevic ◽

Vladimir Kostic ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Inertial Sensor ◽

Freezing Of Gait ◽

Assessment System ◽

Sensor Data ◽

Postural Adjustment ◽

Gait Assessment ◽

Graphical Tool ◽

Gait Disturbances

Background/Aim. Postural impairments and gait disorders in Parkinson's disease (PD) affect limits of stability, impaire postural adjustment, and evoke poor responses to perturbation. In the later stage of the disease, some patients can suffer from episodic features such as freezing of gait (FOG). Objective gait assessment and monitoring progress of the disease can give clinicians and therapist important information about changes in gait pattern and potential gait deviations, in order to prevent concomitant falls. The aim of this study was to propose a method for identification of freezing episodes and gait disturbances in patients with PD. A wireless inertial sensor system can be used to provide follow-up of the treatment effects or progress of the disease. Methods. The system is simple for mounting a subject, comfortable, simple for installing and recording, reliable and provides high-quality sensor data. A total of 12 patients were recorded and tested. Software calculates various gait parameters that could be estimated. User friendly visual tool provides information about changes in gait characteristics, either in a form of spectrogram or by observing spatiotemporal parameters. Based on these parameters, the algorithm performs classification of strides and identification of FOG types. Results. The described stride classification was merged with an algorithm for stride reconstruction resulting in a useful graphical tool that allows clinicians to inspect and analyze subject?s movements. Conclusion. The described gait assessment system can be used for detection and categorization of gait disturbances by applying rule-based classification based on stride length, stride time, and frequency of the shank segment movements. The method provides an valuable graphical interface which is easy to interpret and provides clinicians and therapists with valuable information regarding the temporal changes in gait.

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Dealing with the Heterogeneous Presentations of Freezing of Gait: How Reliable are the Freezing Index and Heart Rate for Freezing Detection?

10.21203/rs.3.rs-735366/v1 ◽

2021 ◽

Author(s):

Helena Cockx ◽

Jorik Nonnekes ◽

Bastiaan Bloem ◽

Richard van Wezel ◽

Ian Cameron ◽

...

Keyword(s):

Parkinson’S Disease ◽

Heart Rate ◽

Parkinson's Disease ◽

Dual Task ◽

Freezing Of Gait ◽

Heart Rate Change ◽

Rate Change ◽

Motion Sensors ◽

Normal Gait ◽

Fog Detection

Abstract Background: Freezing of gait (FOG) is an unpredictable gait arrest that hampers the lives of 40% of people with Parkinson’s disease. Because the symptom is heterogeneous in phenotypical presentation (it can present as trembling, shuffling, or akinesia) and manifests during various circumstances (it can be triggered by e.g. turning, passing doors, and dual-tasking), it is particularly difficult to detect with motion sensors. The freezing index (FI) is one of the most frequently used accelerometer-based methods for FOG detection. However, it might not adequately distinguish FOG from voluntary stops, certainly for the akinetic type of FOG. Interestingly, a previous study showed that heart rate signals could distinguish FOG from stopping and turning movements. This study aimed to investigate for which phenotypes and evoking circumstances the FI and heart rate might provide reliable signals for FOG detection.Methods: Sixteen people with Parkinson’s disease and daily freezing completed a gait trajectory designed to provoke FOG including turns, narrow passages, starting, and stopping, with and without a cognitive or motor dual-task. We compared the FI and heart rate of 406 FOG events to baseline levels, and to stopping and normal gait events (i.e. turns and narrow passages without FOG) using mixed-effects models. We specifically evaluated the influence of different types of FOG (trembling vs akinesia) and triggering situations (turning vs narrow passages; no dual-task vs cognitive dual-task vs motor dual-task) on both outcome measures. Results: The FI increased significantly for trembling FOG, but not for akinetic FOG. Furthermore, the index increased similarly during stopping and was therefore not significantly different from FOG. In contrast, heart rate change during FOG was for all types and during all triggering situations statistically different from stopping, but not from normal gait events. Conclusion: The FI has issues to distinguish FOG from voluntary stopping, especially of the akinetic type. In contrast, the clear distinction in heart rate change between FOG and voluntary stops, which was independent of the heterogeneous presentation of FOG, might provide a solution for this issue. Therefore, we suggest that combining a heart rate monitor with a motion sensor may be promising for future FOG detection.

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