Predicting Freezing of Gait in Parkinsons Disease Patients Using Machine Learning

2018 ◽  
Author(s):  
Natasa K. Orphanidou ◽  
Abir Hussain ◽  
Robert Keight ◽  
Paulo Lishoa ◽  
Jade Hind ◽  
...  
Keyword(s):  
Machine Learning ◽  
Freezing Of Gait ◽  
Parkinsons Disease

scholarly journals Design of an assistive device to detect freezing episodes and provide audio/vibratory cues to avoid falls

2018 ◽  
Vol 7 (2.31) ◽  
pp. 114
Author(s):  
Vivek Chowdhury ◽  
Arka Goswami ◽  
Rakshit Nigam ◽  
P A Sridhar
Keyword(s):  
Parkinson Disease ◽  
Daily Life ◽  
Freezing Of Gait ◽  
Assistive Device ◽  
Parkinsons Disease ◽  
Gait Abnormality ◽  
The Subject

This paper primarily discusses the Freezing of Gait which is a type of gait abnormality and generally occurs in Parkinson Disease patients which cause an interruption to their life. During a FOG episode, the subject is rendered unable to continue moving or even manoeuver. These episodes give rise to the danger of the patient landing on the ground and often renders a person immobile. The aim of this device is to develop a technique to identify the effect of ‘Freezing of Gait’ in people suffering from Parkinsons Disease and to provide feedback on detection and improving self-efficiency in about their daily life.

scholarly journals Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4474 ◽  
Author(s):  
Tal Reches ◽  
Moria Dagan ◽  
Talia Herman ◽  
Eran Gazit ◽  
Natalia A. Gouskova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Ground Truth ◽  
Self Report ◽  
Therapeutic Interventions ◽  
Percent Time ◽  
Machine Learning Approach ◽  
Automated Machine Learning ◽  
Fog Detection

Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson’s disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the “ground-truth” for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.

scholarly journals Classification of Parkinson’s disease with freezing of gait based on 360° turning analysis using 36 kinematic features

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Hwayoung Park ◽  
Sungtae Shin ◽  
Changhong Youm ◽  
Sang-Myung Cheon ◽  
Myeounggon Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Stepwise Regression ◽  
Freezing Of Gait ◽  
Learning Model ◽  
Machine Learning Model ◽  
Body Kinematic ◽  
Full Body

Abstract Background Freezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson’s disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression. Methods The study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the “Off” state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression. Results In feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification. Conclusion We suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.

Multi-Feature Fusion and Machine Learning

2021 ◽  
pp. 95-118
Author(s):  
Hadeer Elziaat ◽  
Nashwa El-Bendary ◽  
Ramadan Moawad
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
Freezing Of Gait ◽  
Feature Learning ◽  
Feature Engineering ◽  
Common Symptom ◽  
Fusion Model ◽  
Feature Sets ◽  
Detection Model

Freezing of gait (FoG) is a common symptom of Parkinson's disease (PD) that causes intermittent absence of forward progression of patient's feet while walking. Accordingly, FoG momentary episodes are always accompanied with falls. This chapter presents a novel multi-feature fusion model for early detection of FoG episodes in patients with PD. In this chapter, two feature engineering schemes are investigated, namely time-domain hand-crafted feature engineering and convolutional neural network (CNN)-based spectrogram feature learning. Data of tri-axial accelerometer sensors for patients with PD is utilized to characterize the performance of the proposed model through several experiments with various machine learning (ML) algorithms. Obtained experimental results showed that the multi-feature fusion approach has outperformed typical single feature sets. Conclusively, the significance of this chapter is to highlight the impact of using feature fusion of multi-feature sets through investigating the performance of a FoG episodes early detection model.

scholarly journals A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3287 ◽  
Author(s):  
Satyabrata Aich ◽  
Pyari Pradhan ◽  
Jinse Park ◽  
Nitin Sethi ◽  
Vemula Vathsa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Motion Capture ◽  
Error Rate ◽  
Freezing Of Gait ◽  
Motion Capture System ◽  
3D Motion ◽  
Mean Error ◽  
Gait Parameters ◽  
Fog Prediction ◽  
3D Motion Capture

One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG.

scholarly journals Brief Review of Machine Learning in Neurosurgery

2018 ◽  
Vol 15 (3) ◽  
pp. 3-7
Author(s):  
Yam Bahadur Roka
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Generalized Linear Models ◽  
Probabilistic Reasoning ◽  
Linear Models ◽  
The Body ◽  
Learning From Experience ◽  
Computer Gaming ◽  
Parkinsons Disease

Learning from experience is inherent to animals and humans and when used in computer models it is termed as Machine learning (ML) which was coined by Arthur Samuel the pioneer of computer gaming and artificial intelligence in 1959. This field grew out during the search for artificial intelligence and initially was developed using neural networks, perceptrons, probabilistic reasoning and generalized linear models of statistics. ML works by either of the two methods, supervised learning or unsupervised learning. Search for “ML in neurosurgery” in Pubmed showed 308 results. There were 298 studies with abstracts, 5 clinical trials, 20 review articles and 168 articles in human studies. Of these around 113 articles were either studies of ML in other parts of the body like liver, stroke, EEG, pathology and Parkinsons disease or not involving ML and hence were excluded. Of the 55 remaining cases the majority were studies done in glioma followed by medical imaging in neurosurgery, radiotherapy, language and learning studies. ML will definitely replace many of the cumbersome physical data collection to infer and formulate ways to treat patients in the future. It can make the process of research accumulation, filter, find correlations between variables and help to make algorithms to manage and predict, that can save, time, money and speedup the recovery of the patient

scholarly journals Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 614
Author(s):  
Luigi Borzì ◽  
Ivan Mazzetta ◽  
Alessandro Zampogna ◽  
Antonio Suppa ◽  
Gabriella Olmo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Timed Up And Go ◽  
Dopaminergic Therapy ◽  
Fog Prediction ◽  
Fog Detection

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness.

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