Paper Info
Title
Feature Extraction In Sit-To-Stand Task Using M-Imu Sensors And Evaluatiton In Parkinson'S Disease
Abstract
This work proposes a broad analysis for the detection of the most relevant features for the sit-to-stand task analysis, in Parkinson's disease (PD) patients and healthy subjects (H). A group of sixteen PD patients and thirteen H subjects have been analyzed, using one magneto-inertial sensor, while the physician administers the UPDRS clinical scale. The PD group has been examined before and after the pharmacological therapy (respectively, OFF and ON phase), in order to monitor the different states of the PD, which implies changes in motor control. By calculating the features of this task, it has been possible to choose the most reliable indexes, already used in this task in order to identify differences in the score assigned through sensors. In addition to that, it has also been possible to find differences in the features' values which the clinical scale and the physician cannot identify. Our study highlights how wearable motion sensors can detect statistically significant differences between OFF/ON phase and H subjects that the clinical evaluation can not. We conclude that our method provides a deep analysis of the sit-to-stand task with only one M-IMU, allowing to check PD patient status, providing a method for home care monitoring.
Year
Venue
Field
2018
2018 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA)
Sit to stand,Parkinson's disease,Task analysis,Wearable computer,Computer science,Motor control,Feature extraction,Inertial measurement unit,Motion sensors,Physical medicine and rehabilitation
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Jacopo Tosi181.23
Susanna Summa200.68
Fabrizio Taffoni35813.31
Lazzaro di Biase400.68
Massimo Marano500.34
Angelo Cascio Rizzo600.34
Mario Tombini700.34
Emiliano Schena810643.86
Domenico Formica98826.60
Giovanni Di Pino10257.95