Filippo Cavallo1, Alessandra Moschetti2, Dario Esposito3, Carlo Maremmani4, Erika Rovini5. 1. The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, 56025, Pontedera, Italy. Electronic address: filippo.cavallo@santannapisa.it. 2. The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, 56025, Pontedera, Italy. Electronic address: alessandra.moschetti@santannapisa.it. 3. Co-Robotics srl, Via Franchi, 39, 56033, Capannoli (PI), Italy. Electronic address: dario.esposito@corobotics.eu. 4. U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Viale Mattei, 21, 54100, Massa, Italy. Electronic address: carlo.maremmani@uslnordovest.toscana.it. 5. The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, 56025, Pontedera, Italy. Electronic address: erika.rovini@santannapisa.it.
Abstract
INTRODUCTION: Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. For example, idiopathic hyposmia (IH), which is a reduced olfactory sensitivity, is typical in >95% of PD patients and is a preclinical marker for the pathology. METHODS: In this work, a wearable inertial device, named SensHand V1, was used to acquire motion data from the upper limbs during the performance of six tasks selected by MDS-UPDRS III. Three groups of people were enrolled, including 30 healthy subjects, 30 IH people, and 30 PD patients. Forty-eight parameters per side were computed by spatiotemporal and frequency data analysis. A feature array was selected as the most significant to discriminate among the different classes both in two-group and three-group classification. Multiple analyses were performed comparing three supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes, on three different datasets. RESULTS: Excellent results were obtained for healthy vs. patients classification (F-Measure 0.95 for RF and 0.97 for SVM), and good results were achieved by including subjects with hyposmia as a separate group (0.79 accuracy, 0.80 precision with RF) within a three-group classification. Overall, RF classifiers were the best approach for this application. CONCLUSION: The system is suitable to support an objective PD diagnosis. Further, combining motion analysis with a validated olfactory screening test, a two-step non-invasive, low-cost procedure can be defined to appropriately analyze people at risk for PD development, helping clinicians to identify also subtle changes in motor performance that characterize PD onset.
INTRODUCTION:Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. For example, idiopathic hyposmia (IH), which is a reduced olfactory sensitivity, is typical in >95% of PDpatients and is a preclinical marker for the pathology. METHODS: In this work, a wearable inertial device, named SensHand V1, was used to acquire motion data from the upper limbs during the performance of six tasks selected by MDS-UPDRS III. Three groups of people were enrolled, including 30 healthy subjects, 30 IH people, and 30 PDpatients. Forty-eight parameters per side were computed by spatiotemporal and frequency data analysis. A feature array was selected as the most significant to discriminate among the different classes both in two-group and three-group classification. Multiple analyses were performed comparing three supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes, on three different datasets. RESULTS: Excellent results were obtained for healthy vs. patients classification (F-Measure 0.95 for RF and 0.97 for SVM), and good results were achieved by including subjects with hyposmia as a separate group (0.79 accuracy, 0.80 precision with RF) within a three-group classification. Overall, RF classifiers were the best approach for this application. CONCLUSION: The system is suitable to support an objective PD diagnosis. Further, combining motion analysis with a validated olfactory screening test, a two-step non-invasive, low-cost procedure can be defined to appropriately analyze people at risk for PD development, helping clinicians to identify also subtle changes in motor performance that characterize PD onset.
Authors: Mark N Warden; Susan Searles Nielsen; Alejandra Camacho-Soto; Roman Garnett; Brad A Racette Journal: PLoS One Date: 2021-08-26 Impact factor: 3.240