Prediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks

Abstract Background Freezing of gait (FOG) is a walking disturbance in advanced stage Parkinson’s disease (PD) that has been associated with increased fall risk and decreased quality of life. Freezing episodes can be mitigated or prevented with external intervention such as visual or auditory cues,...

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Autores principales:	Gaurav Shalin, Scott Pardoel, Edward D. Lemaire, Julie Nantel, Jonathan Kofman
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Freezing of gait Parkinson’s disease Plantar pressure Long short-term memory Deep learning Detection Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/197700bcd5684bbabd97bb04db9c1fea
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spelling	oai:doaj.org-article:197700bcd5684bbabd97bb04db9c1fea2021-11-28T12:38:36ZPrediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks10.1186/s12984-021-00958-51743-0003https://doaj.org/article/197700bcd5684bbabd97bb04db9c1fea2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12984-021-00958-5https://doaj.org/toc/1743-0003Abstract Background Freezing of gait (FOG) is a walking disturbance in advanced stage Parkinson’s disease (PD) that has been associated with increased fall risk and decreased quality of life. Freezing episodes can be mitigated or prevented with external intervention such as visual or auditory cues, activated by FOG prediction and detection systems. While most research on FOG detection and prediction has been based on inertial measurement unit (IMU) and accelerometer data, plantar-pressure data may capture subtle weight shifts unique to FOG episodes. Different machine learning algorithms have been used for FOG detection and prediction; however, long short-term memory (LSTM) deep learning methods hold an advantage when dealing with time-series data, such as sensor data. This research aimed to determine if LSTM can be used to detect and predict FOG from plantar pressure data alone, specifically for use in a real-time wearable system. Methods Plantar pressure data were collected from pressure-sensing insole sensors worn by 11 participants with PD as they walked a predefined freeze-provoking path. FOG instances were labelled, 16 features were extracted, and the dataset was balanced and normalized (z-score). The resulting datasets were classified using long short-term memory neural-network models. Separate models were trained for detection and prediction. For prediction models, data before FOG were included in the target class. Leave-one-freezer-out cross validation was used for model evaluation. In addition, the models were tested on all non-freezer data to determine model specificity. Results The best FOG detection model had 82.1% (SD 6.2%) mean sensitivity and 89.5% (SD 3.6%) mean specificity for one-freezer-held-out cross validation. Specificity improved to 93.3% (SD 4.0%) when ignoring inactive state data (standing) and analyzing the model only on active states (turning and walking). The model correctly detected 95% of freeze episodes. The best FOG prediction method achieved 72.5% (SD 13.6%) mean sensitivity and 81.2% (SD 6.8%) mean specificity for one-freezer-held-out cross validation. Conclusions Based on FOG data collected in a laboratory, the results suggest that plantar pressure data can be used for FOG detection and prediction. However, further research is required to improve FOG prediction performance, including training with a larger sample of people who experience FOG.Gaurav ShalinScott PardoelEdward D. LemaireJulie NantelJonathan KofmanBMCarticleFreezing of gaitParkinson’s diseasePlantar pressureLong short-term memoryDeep learningDetectionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENJournal of NeuroEngineering and Rehabilitation, Vol 18, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Freezing of gait Parkinson’s disease Plantar pressure Long short-term memory Deep learning Detection Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Freezing of gait Parkinson’s disease Plantar pressure Long short-term memory Deep learning Detection Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Gaurav Shalin Scott Pardoel Edward D. Lemaire Julie Nantel Jonathan Kofman Prediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks
description	Abstract Background Freezing of gait (FOG) is a walking disturbance in advanced stage Parkinson’s disease (PD) that has been associated with increased fall risk and decreased quality of life. Freezing episodes can be mitigated or prevented with external intervention such as visual or auditory cues, activated by FOG prediction and detection systems. While most research on FOG detection and prediction has been based on inertial measurement unit (IMU) and accelerometer data, plantar-pressure data may capture subtle weight shifts unique to FOG episodes. Different machine learning algorithms have been used for FOG detection and prediction; however, long short-term memory (LSTM) deep learning methods hold an advantage when dealing with time-series data, such as sensor data. This research aimed to determine if LSTM can be used to detect and predict FOG from plantar pressure data alone, specifically for use in a real-time wearable system. Methods Plantar pressure data were collected from pressure-sensing insole sensors worn by 11 participants with PD as they walked a predefined freeze-provoking path. FOG instances were labelled, 16 features were extracted, and the dataset was balanced and normalized (z-score). The resulting datasets were classified using long short-term memory neural-network models. Separate models were trained for detection and prediction. For prediction models, data before FOG were included in the target class. Leave-one-freezer-out cross validation was used for model evaluation. In addition, the models were tested on all non-freezer data to determine model specificity. Results The best FOG detection model had 82.1% (SD 6.2%) mean sensitivity and 89.5% (SD 3.6%) mean specificity for one-freezer-held-out cross validation. Specificity improved to 93.3% (SD 4.0%) when ignoring inactive state data (standing) and analyzing the model only on active states (turning and walking). The model correctly detected 95% of freeze episodes. The best FOG prediction method achieved 72.5% (SD 13.6%) mean sensitivity and 81.2% (SD 6.8%) mean specificity for one-freezer-held-out cross validation. Conclusions Based on FOG data collected in a laboratory, the results suggest that plantar pressure data can be used for FOG detection and prediction. However, further research is required to improve FOG prediction performance, including training with a larger sample of people who experience FOG.
format	article
author	Gaurav Shalin Scott Pardoel Edward D. Lemaire Julie Nantel Jonathan Kofman
author_facet	Gaurav Shalin Scott Pardoel Edward D. Lemaire Julie Nantel Jonathan Kofman
author_sort	Gaurav Shalin
title	Prediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks
title_short	Prediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks
title_full	Prediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks
title_fullStr	Prediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks
title_full_unstemmed	Prediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks
title_sort	prediction and detection of freezing of gait in parkinson’s disease from plantar pressure data using long short-term memory neural-networks
publisher	BMC
publishDate	2021
url	https://doaj.org/article/197700bcd5684bbabd97bb04db9c1fea
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Prediction and detection of freezing of gait in Parkinson’s disease from plantar pressure data using long short-term memory neural-networks

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