Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals
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, wh...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/4/948 |
| _version_ | 1817996584911110144 |
|---|---|
| author | Ivan Mazzetta Alessandro Zampogna Antonio Suppa Alessandro Gumiero Marco Pessione Fernanda Irrera |
| author_facet | Ivan Mazzetta Alessandro Zampogna Antonio Suppa Alessandro Gumiero Marco Pessione Fernanda Irrera |
| author_sort | Ivan Mazzetta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T02:24:40Z |
| format | Article |
| id | doaj.art-97cd91b80be84d0fb1f9858734cbf2b6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T02:24:40Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-97cd91b80be84d0fb1f9858734cbf2b62022-12-22T02:17:56ZengMDPI AGSensors1424-82202019-02-0119494810.3390/s19040948s19040948Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial SignalsIvan Mazzetta0Alessandro Zampogna1Antonio Suppa2Alessandro Gumiero3Marco Pessione4Fernanda Irrera5Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, ItalyDepartment of Human Neurosciences, Sapienza University of Rome, 00185 Rome, ItalyDepartment of Human Neurosciences, Sapienza University of Rome, 00185 Rome, ItalySTMicroelectronics, 20864 Agrate Brianza MI , ItalySTMicroelectronics, 20864 Agrate Brianza MI , ItalyDepartment of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, ItalyWe 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.https://www.mdpi.com/1424-8220/19/4/948wearable sensorssensor fusioninertial signalsurface electromyographygait analysisParkinson’s diseasetelemedicine |
| spellingShingle | Ivan Mazzetta Alessandro Zampogna Antonio Suppa Alessandro Gumiero Marco Pessione Fernanda Irrera Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals Sensors wearable sensors sensor fusion inertial signal surface electromyography gait analysis Parkinson’s disease telemedicine |
| title | Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals |
| title_full | Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals |
| title_fullStr | Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals |
| title_full_unstemmed | Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals |
| title_short | Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals |
| title_sort | wearable sensors system for an improved analysis of freezing of gait in parkinson s disease using electromyography and inertial signals |
| topic | wearable sensors sensor fusion inertial signal surface electromyography gait analysis Parkinson’s disease telemedicine |
| url | https://www.mdpi.com/1424-8220/19/4/948 |
| work_keys_str_mv | AT ivanmazzetta wearablesensorssystemforanimprovedanalysisoffreezingofgaitinparkinsonsdiseaseusingelectromyographyandinertialsignals AT alessandrozampogna wearablesensorssystemforanimprovedanalysisoffreezingofgaitinparkinsonsdiseaseusingelectromyographyandinertialsignals AT antoniosuppa wearablesensorssystemforanimprovedanalysisoffreezingofgaitinparkinsonsdiseaseusingelectromyographyandinertialsignals AT alessandrogumiero wearablesensorssystemforanimprovedanalysisoffreezingofgaitinparkinsonsdiseaseusingelectromyographyandinertialsignals AT marcopessione wearablesensorssystemforanimprovedanalysisoffreezingofgaitinparkinsonsdiseaseusingelectromyographyandinertialsignals AT fernandairrera wearablesensorssystemforanimprovedanalysisoffreezingofgaitinparkinsonsdiseaseusingelectromyographyandinertialsignals |