title: Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection.
abstract: A robust method for identifying movement in the free-living environment is needed to objectively measure physical activity. The purpose of this study was to validate the identification of postural orientation and movement from acceleration data against visual inspection from video recordings. Using tri-axial accelerometers placed on the waist and thigh, static orientations of standing, sitting, and lying down, as well as dynamic movements of walking, jogging and transitions between postures were identified. Additionally, subjects walked and jogged at self-selected slow, comfortable, and fast speeds. Identification of tasks was performed using a combination of the signal magnitude area, continuous wavelet transforms and accelerometer orientations. Twelve healthy adults were studied in the laboratory, with two investigators identifying tasks during each second of video observation. The intraclass correlation coefficients for inter-rater reliability were greater than 0.95 for all activities except for transitions. Results demonstrated high validity, with sensitivity and positive predictive values of greater than 85% for sitting and lying, with walking and jogging identified at greater than 90%. The greatest disagreement in identification accuracy between the algorithm and video occurred when subjects were asked to fidget while standing or sitting. During variable speed tasks, gait was correctly identified for speeds between 0.1m/s and 4.8m/s. This study included a range of walking speeds and natural movements such as fidgeting during static postures, demonstrating that accelerometer data can be used to identify orientation and movement among the general population.
authors.affiliation: Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: lugade.vipul@mayo.edu.
authors.forename: Vipul
authors.initials: V
authors.lastname: Lugade
authors.affiliation: Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: fortune.emma@mayo.edu.
authors.forename: Emma
authors.initials: E
authors.lastname: Fortune
authors.affiliation: Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: morrow.melissa@mayo.edu.
authors.forename: Melissa
authors.initials: M
authors.lastname: Morrow
authors.affiliation: Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA. Electronic address: kaufman.kenton@mayo.edu.
authors.forename: Kenton
authors.initials: K
authors.lastname: Kaufman
mesh_terms: D000054:Acceleration; D061725:Accelerometry / Q000295:instrumentation*; D000328:Adult; D000465:Algorithms; D005260:Female; D005684:Gait / Q000502:physiology; D006801:Humans; D007590:Jogging / Q000502:physiology; D007753:Laboratories; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009068:Movement; D011187:Posture; D014741:Video Recording; D016138:Walking / Q000502:physiology; D055815:Young Adult
keywords: Acceleration; Accuracy; Gait velocity; Movement analysis
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