Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

RESEARCH ARTICLE Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Rodrı́guez-Martı́n D, Samà A, PérezLópez C, Català A, Moreno Arostegui JM, Cabestany J, et al. (2017) Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer. PLoS ONE 12(2): e0171764. doi:10.1371/journal. pone.0171764 Editor: Sergio Gómez, Universitat Rovira i Virgili, SPAIN Received: July 22, 2016 Accepted: January 25, 2017 Published: February 15, 2017 Copyright: © 2017 Rodrı́guez-Martı́n et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Due to ethical restrictions imposed by Spanish Agency for Drugs and Medical Devices (Spain), Clinical Research Ethics Committee-Galway University Hospitals (Ireland), Health Department Maccabi Healthcare Services (Israel) and Ethics Committee Fondazione Santa Lucia (Italy), related to approved consent procedure and protecting privacy, all relevant data are available upon request (Joan Cabestany, email: and Daniel Rodrı́guezMartin, email: ). Daniel Rodrı́guez-Martı́n1☯*, Albert Samà1,2☯, Carlos Pérez-López1,2, Andreu Català1,2, Joan M. Moreno Arostegui1,2, Joan Cabestany1,2, Àngels Bayés3, Sheila Alcaine3, Berta Mestre3, Anna Prats3, M. Cruz Crespo3, Timothy J. Counihan4, Patrick Browne4, Leo R. Quinlan5, Gearóid ÓLaighin5, Dean Sweeney5, Hadas Lewy6, Joseph Azuri6,7, Gabriel Vainstein6, Roberta Annicchiarico8, Alberto Costa8, Alejandro Rodrı́guezMolinero2,5¤ 1 Universitat Politècnica de Catalunya – BarcelonaTech (UPC), Technical Research Centre for Dependency Care and Autonomous Living (CETPD), Vilanova i la Geltrú, Spain, 2 Sense4Care, Barcelona, Spain, 3 Unidad de Parkinson y trastornos del movimiento (UParkinson), Barcelona, Spain, 4 School of Medicine, National University of Ireland Galway (NUIG), Galway, Ireland, 5 Electrical & Electronic Engineering Department, National University of Ireland Galway (NUIG), Galway, Ireland, 6 Maccabi Healthcare Services, Tel Aviv, Israel, 7 Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel, 8 IRCCS Fondazione Santa Lucia, Rome, Italy ☯ These authors contributed equally to this work. ¤ Current address: Clinical Research Unit, Consorci Sanitari del Garraf, Vilanova i la Geltrú, Spain * Abstract Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine PLOS ONE | DOI:10.1371/journal.pone.0171764 February 15, 2017 1 / 26 SVM-based algorithm to detect freezing of gait at home Funding: This project has been performed within the framework of the "Freezing in Parkinson’s Disease: Improving Quality of Life with an Automatic Control System" (MASPARK) project which is funded by La Fundació La Marató de TV3 20140431 [http://www.tv3.cat/marato/es/ projectes_financats_2013]. This work also forms part of the framework of the FP7 project "Personal Health Device for the Remote and Autonomous Management of Parkinson’s Disease" (REMPARK) ICT-287677, which is funded by the European Community [http://www.rempark.eu/]. Competing interests: Albert Samà, Carlos PérezLópez, Andreu Català, Juan Manuel Moreno, Joan Cabestany and Alejandro Rodrı́guez-Molinero are shareholders of Sense4Care, which is a spin-off company. Sense4Care may commercialize the results of this research device in a near future. These authors declare that the possible commercialization of the product is a research outcome, not being the design, the analysis, the interpretation of the results or the conclusions affected by commercial interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials. learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy. Introduction Parkinson’s disease (PD) is a neurodegenerative disease that principally affects the motor system. According to the Global Declaration for Parkinson’s Disease, PD affects up to 6.3 million people worldwide [1]. Among the many PD symptoms, Freezing of Gait (FoG) is one of the most incapacitating and is usually present in the more advanced phase of the disease [2,3]. FoG is commonly described by PD patients as if their feet were “glued to the floor” resulting in loss of postural balance frequently causing falls [4,5]. In addition to the motor complications arising from FoG, it can also lead to non-motor complications including social isolation, depression, and anxiety. [6,7]. The precise tracking of the occurrence of FoG is challenging for clinicians. The acquisition of such information can greatly help in optimising therapies including pharmacotherapy, and physiotherapy, that are known to be beneficial for reducing FoG episodes [4]. Furthermore, by acquiring accurate information on FoG frequency and severity in conjunction with the full repertoire of other motor and non-motor symptomatology, this would provide to the neurolo (...truncated)