title: Metric learning for Parkinsonian identification from IMU gait measurements.
abstract: Diagnosis of people with mild Parkinson's symptoms is difficult. Nevertheless, variations in gait pattern can be utilised to this purpose, when measured via Inertial Measurement Units (IMUs). Human gait, however, possesses a high degree of variability across individuals, and is subject to numerous nuisance factors. Therefore, off-the-shelf Machine Learning techniques may fail to classify it with the accuracy required in clinical trials. In this paper we propose a novel framework in which IMU gait measurement sequences sampled during a 10m walk are first encoded as hidden Markov models (HMMs) to extract their dynamics and provide a fixed-length representation. Given sufficient training samples, the distance between HMMs which optimises classification performance is learned and employed in a classical Nearest Neighbour classifier. Our tests demonstrate how this technique achieves accuracy of 85.51% over a 156 people with Parkinson's with a representative range of severity and 424 typically developed adults, which is the top performance achieved so far over a cohort of such size, based on single measurement outcomes. The method displays the potential for further improvement and a wider application to distinguish other conditions.
authors.affiliation: Artificial Intelligence and Vision Group, Department of Computing and Communication Technologies, Oxford Brookes University, Wheatley Campus, Oxford OX33 1HX, UK. Electronic address: fabio.cuzzolin@brookes.ac.uk.
authors.forename: Fabio
authors.initials: F
authors.lastname: Cuzzolin
authors.affiliation: Torr Vision Group, Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK.
authors.forename: Michael
authors.initials: M
authors.lastname: Sapienza
authors.affiliation: Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK. Electronic address: pesser@brookes.ac.uk.
authors.forename: Patrick
authors.initials: P
authors.lastname: Esser
authors.affiliation: Artificial Intelligence and Vision Group, Department of Computing and Communication Technologies, Oxford Brookes University, Wheatley Campus, Oxford OX33 1HX, UK. Electronic address: suman.saha-2014@brookes.ac.uk.
authors.forename: Suman
authors.initials: S
authors.lastname: Saha
authors.affiliation: Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK. Electronic address: marloes.franssen-2011@brookes.ac.uk.
authors.forename: Miss Marloes
authors.initials: MM
authors.lastname: Franssen
authors.affiliation: Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK. Electronic address: jcollett@brookes.ac.uk.
authors.forename: Johnny
authors.initials: J
authors.lastname: Collett
authors.affiliation: Movement Science Group, Oxford Institute of Nursing, Midwifery, and Allied Health Research, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, UK. Electronic address: hdawes@brookes.ac.uk.
authors.forename: Helen
authors.initials: H
authors.lastname: Dawes
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D005684:Gait / Q000502:physiology*; D020233:Gait Disorders, Neurologic / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000503:physiopathology*; D016138:Walking / Q000502:physiology*
keywords: Gait; Hidden Markov models; Inertial Measurement Unit; Machine Learning algorithms; Metric learning; Parkinson's