title: A smart device inertial-sensing method for gait analysis.
abstract: The purpose of this study was to establish and cross-validate a method for analyzing gait patterns determined by the center of mass (COM) through inertial sensors embedded in smart devices. The method employed an extended Kalman filter in conjunction with a quaternion rotation matrix approach to transform accelerations from the object onto the global frame. Derived by double integration, peak-to-trough changes in vertical COM position captured by a motion capture system, inertial measurement unit, and smart device were compared in terms of averaged and individual steps. The inter-rater reliability and levels of agreement for systems were discerned through intraclass correlation coefficients (ICC) and Bland-Altman plots. ICCs corresponding to inter-rater reliability were good-to-excellent for position data (ICCs,.80-.95) and acceleration data (ICCs,.54-.81). Levels of agreements were moderate for position data (LOA, 3.1-19.3%) and poor for acceleration data (LOA, 6.8%-17.8%). The Bland-Altman plots, however, revealed a small systematic error, in which peak-to-trough changes in vertical COM position were underestimated by 2.2mm; the Kalman filter׳s accuracy requires further investigation to minimize this oversight. More importantly, however, the study׳s preliminary results indicate that the smart device allows for reliable COM measurements, opening up a cost-effective, user-friendly, and popular solution for remotely monitoring movement. The long-term impact of the smart device method on patient rehabilitation and therapy cannot be underestimated: not only could healthcare expenditures be curbed (smart devices being more affordable than today's motion sensors), but a more refined grasp of individual functioning, activity, and participation within everyday life could be attained.
authors.affiliation: Movement Science Group, Faculty of Healthy & Life Sciences, Oxford Brookes University, Oxford, United Kingdom. Electronic address: dax.steins-2011@brookes.ac.uk.
authors.forename: Dax
authors.initials: D
authors.lastname: Steins
authors.affiliation: Computer Laboratory, University of Cambridge, Cambridge, United Kingdom. Electronic address: ian.sheret@cl.cam.ac.uk.
authors.forename: Ian
authors.initials: I
authors.lastname: Sheret
authors.affiliation: Movement Science Group, Faculty of Healthy & Life Sciences, Oxford Brookes University, Oxford, United Kingdom; Department of Clinical Neurology, University of Oxford, Oxford, United Kingdom; Cardiff University, Wales, United Kingdom. Electronic address: hdawes@brookes.ac.uk.
authors.forename: Helen
authors.initials: H
authors.lastname: Dawes
authors.affiliation: Movement Science Group, Faculty of Healthy & Life Sciences, Oxford Brookes University, Oxford, United Kingdom. Electronic address: pesser@brookes.ac.uk.
authors.forename: Patrick
authors.initials: P
authors.lastname: Esser
authors.affiliation: Movement Science Group, Faculty of Healthy & Life Sciences, Oxford Brookes University, Oxford, United Kingdom. Electronic address: jcollett@brookes.ac.uk.
authors.forename: Johnny
authors.initials: J
authors.lastname: Collett
mesh_terms: D000054:Acceleration; D000328:Adult; D001696:Biomechanical Phenomena; D005684:Gait / Q000502:physiology*; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D012399:Rotation; D055815:Young Adult
keywords: Accelerometer; Gait analysis; Inertial measurement unit; Kalman filter; Smartphones; mHealth