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 Allelic variant in SLC6A3 rs393795 affects cerebral regional homogeneity and gait dysfunction in patients with Parkinson’s disease

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7957
Author(s):  
Lina Wang ◽  
Yongsheng Yuan ◽  
Jianwei Wang ◽  
Yuting Shen ◽  
Yan Zhi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Allelic Variation ◽  
Inferior Frontal Gyrus ◽  
Freezing Of Gait ◽  
Clinical Manifestations ◽  
Regional Homogeneity ◽  
Allelic Variant ◽  
Analysis Of Covariance ◽  
Link Type

Aims We sought to explore the role of the SLC6A3 rs393795 allelic variant in cerebral spontaneous activity and clinical features in Parkinson’s disease (PD) via imaging genetic approach. Methods Our study recruited 50 PD and 45 healthy control (HC) participants to provide clinical, genetic, and resting state functional magnetic resonance imaging (rs-fMRI) data. All subjects were separated into 16 PD-AA, 34 PD-CA/CC, 14 HC-AA, and 31 HC-CA/CC four subgroups according to SLC6A3 rs393795 genotyping. Afterwards, main effects and interactions of groups (PD versus HC) and genotypes (AA versus CA/CC) on cerebral function reflected by regional homogeneity (ReHo) were explored using two-way analysis of covariance (ANCOVA) after controlling age and gender. Finally, Spearman’ s correlations were employed to investigate the relationships between significantly interactive brain regions and clinical manifestations in PD subgroups. Results Compared with HC subjects, PD patients exhibited increased ReHo signals in left middle temporal gyrus and decreased ReHo signals in left pallidum. Compared with CA/CC carriers, AA genotype individuals showed abnormal increased ReHo signals in right inferior frontal gyrus (IFG) and supplementary motor area (SMA). Moreover, significant interactions (affected by both disease factor and allelic variation) were detected in right inferior temporal gyrus (ITG). Furthermore, aberrant increased ReHo signals in right ITG were observed in PD-AA in comparison with PD-CA/CC. Notably, ReHo values in right ITG were negatively associated with Tinetti Mobility Test (TMT) gait subscale scores and positively related to Freezing of Gait Questionnaire (FOG-Q) scores in PD-AA subgroup. Conclusions Our findings suggested that SLC6A3 rs393795 allelic variation might have a trend to aggravate the severity of gait disorders in PD patients by altering right SMA and IFG function, and ultimately result in compensatory activation of right ITG. It could provide us with a new perspective for exploring deeply genetic mechanisms of gait disturbances in PD.

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

The Relative Contribution of Physical and Cognitive Fall Risk Factors in People With Parkinson’s Disease

2013 ◽  
Vol 28 (3) ◽  
pp. 282-290 ◽  
Author(s):  
Serene S. Paul ◽  
Catherine Sherrington ◽  
Colleen G. Canning ◽  
Victor S. C. Fung ◽  
Jacqueline C. T. Close ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Muscle Strength ◽  
Fall Prevention ◽  
Fall Risk ◽  
Freezing Of Gait ◽  
Prevention Strategies ◽  
Relative Contribution ◽  
Leg Muscle

Background. In order to develop multifaceted fall prevention strategies for people with Parkinson’s disease (PD), greater understanding of the impact of physical and cognitive performance on falls is required. Objective. We aimed to identify the relative contribution of a comprehensive range of physical and cognitive risk factors to prospectively-measured falls in a large sample of people with PD and develop an explanatory multivariate fall risk model in this group. Methods. Measures of PD signs and symptoms, freezing of gait, balance, mobility, proprioception, leg muscle strength, and cognition were collected on 205 community-dwelling people with PD. Falls were monitored prospectively for 6 months using falls diaries. Results. A total of 120 participants (59%) fell during follow-up. Freezing of gait ( P < .001), dyskinesia ( P = .02), impaired anticipatory and reactive balance ( P < .001), impaired cognition ( P = .002), reduced leg muscle strength ( P = .006), and reduced proprioception ( P = .04) were significantly associated with future falls in univariate analyses. Freezing of gait (risk ratio [RR] = 1.03, 95% confidence interval [CI] = 1.00-1.05, P = .02), impaired anticipatory (RR = 1.01, 95% CI = 1.00-1.02, P = .03) and reactive (RR = 1.26, 95% CI = 1.01-1.58, P = .04) balance, and impaired orientation (RR = 1.28, 95% CI = 1.01-1.62, P = .04) maintained significant associations with falls in multivariate analysis. Conclusion. The study findings elucidate important physical and cognitive determinants of falls in people with PD and may assist in developing efficacious fall prevention strategies for this high-risk group.

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 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.

Sign in / Sign up

Export Citation Format

Share Document