Feature Extraction in Sit-to-Stand Task Using M-IMU Sensors and Evaluatiton in Parkinson's Disease

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.