title: A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology-The ADAPT Study Data-Set.
abstract: Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects' movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects' movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen's Kappa, corrected kappa, Krippendorff's alpha and Fleiss' kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.
authors.affiliation: Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway. alankb@ntnu.no.
authors.forename: Alan Kevin
authors.initials: AK
authors.lastname: Bourke
authors.affiliation: Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway. espen.ihlen@ntnu.no.
authors.forename: Espen Alexander F
authors.initials: EA
authors.lastname: Ihlen
authors.affiliation: Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway. ronny.bergquist@ntnu.no.
authors.forename: Ronny
authors.initials: R
authors.lastname: Bergquist
authors.affiliation: Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway. per.b.wik@ntnu.no.
authors.forename: Per Bendik
authors.initials: PB
authors.lastname: Wik
authors.affiliation: Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway. beatrix.vereijken@ntnu.no.
authors.forename: Beatrix
authors.initials: B
authors.lastname: Vereijken
authors.affiliation: Department of Neuroscience, Faculty of Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway. jorunn.helbostad@ntnu.no.
authors.forename: Jorunn L
authors.initials: JL
authors.lastname: Helbostad
mesh_terms: D000368:Aged; D000465:Algorithms; D015444:Exercise; D006801:Humans; D009068:Movement; D013672:Technology
keywords: activity; algorithm; classification; inertial-sensor; validation