scholarly journals Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1471 ◽  
Author(s):  
Tobias Steinmetzer ◽  
Michele Maasch ◽  
Ingrid Bönninger ◽  
Carlos M. Travieso
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wavelet Transformation ◽  
Inertial Sensors ◽  
Arm Movement ◽  
Objective Evaluation ◽  
Timed Up And Go ◽  
Arm Swing ◽  
Age Related ◽  
Wearable Inertial Sensors

Due to increasing life expectancy, the number of age-related diseases with motor dysfunctions (MD) such as Parkinson’s disease (PD) is also increasing. The assessment of MD is visual and therefore subjective. For this reason, many researchers are working on an objective evaluation. Most of the research on gait analysis deals with the analysis of leg movement. The analysis of arm movement is also important for the assessment of gait disorders. This work deals with the analysis of the arm swing by using wearable inertial sensors. A total of 250 records of 39 different subjects were used for this task. Fifteen subjects of this group had motor dysfunctions (MD). The subjects had to perform the standardized Timed Up and Go (TUG) test to ensure that the recordings were comparable. The data were classified by using the wavelet transformation, a convolutional neural network (CNN), and weight voting. During the classification, single signals, as well as signal combinations were observed. We were able to detect MD with an accuracy of 93.4% by using the wavelet transformation and a three-layer CNN architecture.

scholarly journals Empowering Patients in Self-Management of Parkinson's Disease through Cooperative ICT Systems

2016 ◽  
pp. 251-277 ◽  
Author(s):  
Erika Rovini ◽  
Dario Esposito ◽  
Carlo Maremmani ◽  
Paolo Bongioanni ◽  
Filippo Cavallo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensors ◽  
Objective Evaluation ◽  
Integrated Devices ◽  
Quantitative Metrics ◽  
Medical Effectiveness ◽  
Health And Disease ◽  
Wearable Inertial Sensors ◽  
Cloud Technologies

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies.

scholarly journals Empowering Patients in Self-Management of Parkinson's Disease Through Cooperative ICT Systems

2018 ◽  
pp. 637-663
Author(s):  
Erika Rovini ◽  
Dario Esposito ◽  
Carlo Maremmani ◽  
Paolo Bongioanni ◽  
Filippo Cavallo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensors ◽  
Objective Evaluation ◽  
Integrated Devices ◽  
Quantitative Metrics ◽  
Medical Effectiveness ◽  
Health And Disease ◽  
Wearable Inertial Sensors ◽  
Cloud Technologies

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies.

scholarly journals Longitudinal assessment of falls in patients with Parkinson’s disease using inertial sensors and the Timed Up and Go test

2018 ◽  
Vol 5 ◽  
pp. 205566831775081 ◽  
Author(s):  
Barry R Greene ◽  
Brian Caulfield ◽  
Dronacharya Lamichhane ◽  
William Bond ◽  
Jessica Svendsen ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensors ◽  
Longitudinal Assessment ◽  
Timed Up And Go

scholarly journals Data-Driven Models for Objective Grading Improvement of Parkinson’s Disease

2020 ◽  
Vol 48 (12) ◽  
pp. 2976-2987
Author(s):  
Abdul Haleem Butt ◽  
Erika Rovini ◽  
Hamido Fujita ◽  
Carlo Maremmani ◽  
Filippo Cavallo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fuzzy Inference ◽  
Inertial Sensors ◽  
Objective Assessment ◽  
Data Driven ◽  
Inference System ◽  
Regression Algorithms ◽  
Severity Levels ◽  
Wearable Inertial Sensors

AbstractParkinson’s disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients’ quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.

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