Feasibility of Home-Based Automated Assessment of Postural Instability and Lower Limb Impairments in Parkinson’s Disease
Claudia Ferraris,
Roberto Nerino,
Antonio Chimienti,
Giuseppe Pettiti,
Nicola Cau,
Veronica Cimolin,
Corrado Azzaro,
Lorenzo Priano,
Alessandro Mauro
Affiliations
Claudia Ferraris
Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Roberto Nerino
Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Antonio Chimienti
Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Giuseppe Pettiti
Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Nicola Cau
Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy
Veronica Cimolin
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
Corrado Azzaro
Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy
Lorenzo Priano
Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy
Alessandro Mauro
Istituto Auxologico Italiano, IRCCS, Department of Neurology and NeuroRehabilitation, S. Giuseppe Hospital, 28824 Piancavallo, Oggebbio (Verbania), Italy
A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson’s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects’ performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson’s disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework.
