@conference{
	11580_98686,
	author = {Carissimo, C. and Ferrigno, L. and Golluccio, G. and Marino, A. and Cerro, G.},
	title = {Parkinson's disease aided diagnosis: online symptoms detection by a low-cost wearable Inertial Measurement Unit},
	year = {2022},
	publisher = {Institute of Electrical and Electronics Engineers Inc.},
	booktitle = {2022 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2022 - Conference Proceedings},
	abstract = {The usage of mini-devices in medicine for continuous non-invasive monitoring of neurodegenerative diseases is rapidly increasing. Among most common diseases belonging to such category, Parkinson's is one of the main disorders, especially in aged population. It is characterized by several symptoms whose comprehensive and accurate analysis can lead to a punctual and effective diagnosis. This task is generally accomplished by an expert medical doctor but, especially in first stage, the aid of an automatic tool can help to catch even very low symptomatology. A promising solution to detect most motor issues related to Parkinson's disease is represented by Inertial Measurement Units (IMUs), typically including accelerometers, magnetometers and gyroscopes. Their metrological features, such as accuracy, sensitivity and immunity to external disturbances are critical to get a fully functional and discriminant device. Furthermore, the capability to extrapolate pathological states from measurements is a very attractive feature to automatize early warning and fast medical interventions. To accomplish for both tasks, in this paper a measuring platform containing an IMU is presented and metrologically characterized; moreover, classification tests for typical impairments due to Parkinson's disease are proposed. Although improvements in the procedure and measurement quality are on the way, the current status allows to state its suitability for the required application framework.},
	keywords = {IMU sensor; machine learning; Parkinson's disease; tremor measurement; wearable},
	doi = {10.1109/MeMeA54994.2022.9856546},
	isbn = {978-1-6654-8299-8},	
	pages = {1--6}
}

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Among most common diseases belonging to such category, Parkinson's is one of the main disorders, especially in aged population. It is characterized by several symptoms whose comprehensive and accurate analysis can lead to a punctual and effective diagnosis. This task is generally accomplished by an expert medical doctor but, especially in first stage, the aid of an automatic tool can help to catch even very low symptomatology. A promising solution to detect most motor issues related to Parkinson's disease is represented by Inertial Measurement Units (IMUs), typically including accelerometers, magnetometers and gyroscopes. Their metrological features, such as accuracy, sensitivity and immunity to external disturbances are critical to get a fully functional and discriminant device. Furthermore, the capability to extrapolate pathological states from measurements is a very attractive feature to automatize early warning and fast medical interventions. To accomplish for both tasks, in this paper a measuring platform containing an IMU is presented and metrologically characterized; moreover, classification tests for typical impairments due to Parkinson's disease are proposed. Although improvements in the procedure and measurement quality are on the way, the current status allows to state its suitability for the required application framework.","keywords":"IMU sensor; machine learning; Parkinson's disease; tremor measurement; wearable","doi":"10.1109/MeMeA54994.2022.9856546","isbn":"978-1-6654-8299-8","pages":"1–6","bibtex":"@conference{\n\t11580_98686,\n\tauthor = {Carissimo, C. and Ferrigno, L. and Golluccio, G. and Marino, A. and Cerro, G.},\n\ttitle = {Parkinson's disease aided diagnosis: online symptoms detection by a low-cost wearable Inertial Measurement Unit},\n\tyear = {2022},\n\tpublisher = {Institute of Electrical and Electronics Engineers Inc.},\n\tbooktitle = {2022 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2022 - Conference Proceedings},\n\tabstract = {The usage of mini-devices in medicine for continuous non-invasive monitoring of neurodegenerative diseases is rapidly increasing. Among most common diseases belonging to such category, Parkinson's is one of the main disorders, especially in aged population. It is characterized by several symptoms whose comprehensive and accurate analysis can lead to a punctual and effective diagnosis. This task is generally accomplished by an expert medical doctor but, especially in first stage, the aid of an automatic tool can help to catch even very low symptomatology. A promising solution to detect most motor issues related to Parkinson's disease is represented by Inertial Measurement Units (IMUs), typically including accelerometers, magnetometers and gyroscopes. Their metrological features, such as accuracy, sensitivity and immunity to external disturbances are critical to get a fully functional and discriminant device. Furthermore, the capability to extrapolate pathological states from measurements is a very attractive feature to automatize early warning and fast medical interventions. To accomplish for both tasks, in this paper a measuring platform containing an IMU is presented and metrologically characterized; moreover, classification tests for typical impairments due to Parkinson's disease are proposed. Although improvements in the procedure and measurement quality are on the way, the current status allows to state its suitability for the required application framework.},\n\tkeywords = {IMU sensor; machine learning; Parkinson's disease; tremor measurement; wearable},\n\tdoi = {10.1109/MeMeA54994.2022.9856546},\n\tisbn = {978-1-6654-8299-8},\t\n\tpages = {1--6}\n}\n","author_short":["Carissimo, C.","Ferrigno, L.","Golluccio, G.","Marino, A.","Cerro, G."],"key":"11580_98686","id":"11580_98686","bibbaseid":"carissimo-ferrigno-golluccio-marino-cerro-parkinsonsdiseaseaideddiagnosisonlinesymptomsdetectionbyalowcostwearableinertialmeasurementunit-2022","role":"author","urls":{},"keyword":["IMU sensor; machine learning; Parkinson's disease; tremor measurement; wearable"],"metadata":{"authorlinks":{}}},"bibtype":"conference","biburl":"https://bibbase.org/network/files/iQ2HaEmdn2R3wWnNx","dataSources":["a9jNc2NyyC7d58MTq","YjpAZwNicNHANadbW","9HNP8ouNgGHQfHxfN","tzXxPwGeD2XbcAv9F","DiYuYK6M663hXLXf2","Bf4QxZjro3K8gnTY3","8fppziYBdC9WhS8pc","WjpYhzMSpr7LSfxSN","ij6Z4HQTWDPBgDgEB","BMiF4XZ8BddcYmbYN","LYsdTrM6sSKfnGPrC","SeTjrFSn38FEagCDn","ou5bJ5rQojNquKTfQ"],"keywords":["imu sensor; machine learning; parkinson's disease; tremor measurement; wearable"],"search_terms":["parkinson","disease","aided","diagnosis","online","symptoms","detection","low","cost","wearable","inertial","measurement","unit","carissimo","ferrigno","golluccio","marino","cerro"],"title":"Parkinson's disease aided diagnosis: online symptoms detection by a low-cost wearable Inertial Measurement Unit","year":2022}