scholarly journals Wearable-Sensor-based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5141 ◽  
Author(s):  
Pardoel ◽  
Kofman ◽  
Nantel ◽  
Lemaire
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Current Knowledge ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Machine Learning Algorithms ◽  
Model Generalization ◽  
Feature Extraction And Selection ◽  
Prediction Approach ◽  
Fog Detection

Freezing of gait (FOG) is a serious gait disturbance, common in mid- and late-stage Parkinson’s disease, that affects mobility and increases fall risk. Wearable sensors have been used to detect and predict FOG with the ultimate aim of preventing freezes or reducing their effect using gait monitoring and assistive devices. This review presents and assesses the state of the art of FOG detection and prediction using wearable sensors, with the intention of providing guidance on current knowledge, and identifying knowledge gaps that need to be filled and challenges to be considered in future studies. This review searched the Scopus, PubMed, and Web of Science databases to identify studies that used wearable sensors to detect or predict FOG episodes in Parkinson’s disease. Following screening, 74 publications were included, comprising 68 publications detecting FOG, seven predicting FOG, and one in both categories. Details were extracted regarding participants, walking task, sensor type and body location, detection or prediction approach, feature extraction and selection, classification method, and detection and prediction performance. The results showed that increasingly complex machine-learning algorithms combined with diverse feature sets improved FOG detection. The lack of large FOG datasets and highly person-specific FOG manifestation were common challenges. Transfer learning and semi-supervised learning were promising for FOG detection and prediction since they provided person-specific tuning while preserving model generalization.

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.

scholarly journals Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review

2017 ◽  
Vol 264 (8) ◽  
pp. 1642-1654 ◽  
Author(s):  
Ana Lígia Silva de Lima ◽  
Luc J. W. Evers ◽  
Tim Hahn ◽  
Lauren Bataille ◽  
Jamie L. Hamilton ◽  
...  
Keyword(s):  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Fall Detection

scholarly journals Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 948 ◽  
Author(s):  
Ivan Mazzetta ◽  
Alessandro Zampogna ◽  
Antonio Suppa ◽  
Alessandro Gumiero ◽  
Marco Pessione ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fall Risk ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Clinical Manifestations ◽  
Leg Muscles ◽  
Trunk Inclination ◽  
First Time ◽  
Underlying Pathophysiology

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art.

scholarly journals Real-Time Detection of Freezing Motions in Parkinson's Patients for Adaptive Gait Phase Synchronous Cueing

2021 ◽  
Vol 12 ◽  
Author(s):  
Ardit Dvorani ◽  
Vivian Waldheim ◽  
Magdalena C. E. Jochner ◽  
Christina Salchow-Hömmen ◽  
Jonas Meyer-Ohle ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Prediction Models ◽  
Inertial Sensor ◽  
Freezing Of Gait ◽  
Area Under The Curve ◽  
Support Vector ◽  
Average Performance ◽  
Gait Phase ◽  
Fog Detection

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.

scholarly journals Quantitative Digitography Solves the Remote Measurement Problem in Parkinson's disease

2021 ◽  
Author(s):  
Kevin B Wilkins ◽  
Matthew N. Petrucci ◽  
Yasmine M Kehnemouyi ◽  
Anca Velisar ◽  
Katie Han ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Measurement Problem ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Unmet Need ◽  
Remote Measurement ◽  
Motor Disability ◽  
Therapeutic Trials

Background: Assessment of motor signs in Parkinson's disease (PD) has required an in-person examination. However, 50% of people with PD do not have access to a neurologist. Wearable sensors can provide remote measures of some motor signs but require continuous data acquisition for several days. A major unmet need is reliable metrics of all cardinal motor signs, including rigidity, from a simple short active task that can be performed remotely or in the clinic. Objective: Investigate whether thirty seconds of repetitive alternating finger tapping (RAFT) on a portable quantitative digitography (QDG) device, which measures amplitude and timing, produces reliable metrics of all cardinal motor signs in PD Methods: Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and clinical motor assessment. Eighteen individuals were followed longitudinally with repeated assessments for an average of three years and up to six years. Results: QDG-RAFT metrics differentiated individuals with PD from controls and provided validated metrics for total motor disability (MDS-UPDRS III) and for rigidity, bradykinesia, tremor, gait impairment and freezing of gait (FOG). Additionally, QDG-RAFT tracked disease progression over several years off therapy, and differentiated akinetic rigid from tremor dominant phenotypes, as well as people with from those without FOG. Conclusions: QDG is a reliable technology, which will improve access to care, allows complex remote disease management, and accurate monitoring of disease progression over time in PD. QDG-RAFT also provides the comprehensive PD motor metrics needed for therapeutic trials.

scholarly journals Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson’s Disease Based on Wearable Sensors

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1919
Author(s):  
Bochen Li ◽  
Zhiming Yao ◽  
Jianguo Wang ◽  
Shaonan Wang ◽  
Xianjun Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Cross Validation ◽  
Freezing Of Gait ◽  
Detection Performance ◽  
Operating Efficiency ◽  
The Impact ◽  
Fog Detection

Freezing of gait (FOG) is a paroxysmal dyskinesia, which is common in patients with advanced Parkinson’s disease (PD). It is an important cause of falls in PD patients and is associated with serious disability. In this study, we implemented a novel FOG detection system using deep learning technology. The system takes multi-channel acceleration signals as input, uses one-dimensional deep convolutional neural network to automatically learn feature representations, and uses recurrent neural network to model the temporal dependencies between feature activations. In order to improve the detection performance, we introduced squeeze-and-excitation blocks and attention mechanism into the system, and used data augmentation to eliminate the impact of imbalanced datasets on model training. Experimental results show that, compared with the previous best results, the sensitivity and specificity obtained in 10-fold cross-validation evaluation were increased by 0.017 and 0.045, respectively, and the equal error rate obtained in leave-one-subject-out cross-validation evaluation was decreased by 1.9%. The time for detection of a 256 data segment is only 0.52 ms. These results indicate that the proposed system has high operating efficiency and excellent detection performance, and is expected to be applied to FOG detection to improve the automation of Parkinson’s disease diagnosis and treatment.

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

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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