scholarly journals Template-Matching-Based Detection of Freezing of Gait Using Wearable Sensors

2018 ◽  
Vol 129 ◽  
pp. 21-27 ◽  
Author(s):  
Cheng Xu ◽  
Jie He ◽  
Xiaotong Zhang ◽  
Cunda Wang ◽  
Shihong Duan
Keyword(s):  
Template Matching ◽  
Wearable Sensors ◽  
Freezing Of Gait

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 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 Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2661
Author(s):  
Cameron Diep ◽  
Johanna O’Day ◽  
Yasmine Kehnemouyi ◽  
Gary Burnett ◽  
Helen Bronte-Stewart
Keyword(s):  
Center Of Pressure ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Area Under The Curve ◽  
Measurement Unit ◽  
Visual Identification ◽  
Gait Parameters ◽  
Time Coefficient ◽  
Place Task ◽  
Stepping In Place

Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring the center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (area under the curve = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD.

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 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 Gait Parameters Measured From Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task

2021 ◽  
Author(s):  
Cameron Diep ◽  
Johanna O'Day ◽  
Yasmine Kehnemouyi ◽  
Gary Burnett ◽  
Helen Bronte-Stewart
Keyword(s):  
Center Of Pressure ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Measurement Unit ◽  
Kinematic Data ◽  
Visual Identification ◽  
Gait Parameters ◽  
Time Coefficient ◽  
Place Task ◽  
Stepping In Place

Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (AUC = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD.

scholarly journals Detection of Motor Dysfunction With Wearable Sensors in Patients With Idiopathic Rapid Eye Movement Disorder

2021 ◽  
Vol 9 ◽  
Author(s):  
Lin Ma ◽  
Shu-Ying Liu ◽  
Shan-Shan Cen ◽  
Yuan Li ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Movement Disorder ◽  
Eye Movement ◽  
Arithmetic Operation ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Motor Dysfunction ◽  
Rapid Eye Movement ◽  
Dual Task Condition ◽  
Sleep Behavior ◽  
Trunk Motion

Patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) are at high risk for conversion to synucleinopathy and Parkinson disease (PD). This can potentially be monitored by measuring gait characteristics of iRBD patients, although quantitative data are scarce and previous studies have reported inconsistent findings. This study investigated subclinical gait changes in polysomnography-proven iRBD patients compared to healthy controls (HCs) during 3 different walking conditions using wearable motor sensors in order to determine whether gait changes can be detected in iRBD patients that could reflect early symptoms of movement disorder. A total 31 iRBD patients and 20 HCs were asked to walk in a 10-m corridor at their usual pace, their fastest pace, and a normal pace while performing an arithmetic operation (dual-task condition) for 1 min each while using a wearable gait analysis system. General gait measurements including stride length, stride velocity, stride time, gait length asymmetry, and gait variability did not differ between iRBD patients and HCs; however, the patients showed decreases in range of motion (P = 0.004) and peak angular velocity of the trunk (P = 0.001) that were significant in all 3 walking conditions. iRBD patients also had a longer step time before turning compared to HCs (P = 0.035), and the difference between groups remained significant after adjusting for age, sex, and height. The decreased trunk motion while walking and increased step time before turning observed in iRBD may be early manifestations of body rigidity and freezing of gait and are possible prodromal symptoms of PD.

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