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      • Department of Electrical and Electronics Engineering
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      Investigating inter-subject and inter-activity variations in activity recognition using wearable motion sensors

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      Author
      Barshan, B.
      Yurtman, A.
      Date
      2016
      Source Title
      Computer Journal
      Print ISSN
      0010-4620
      Publisher
      Oxford University Press
      Volume
      59
      Issue
      9
      Pages
      1345 - 1362
      Language
      English
      Type
      Article
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      Abstract
      This work investigates inter-subject and inter-activity variability of a given activity dataset and provides some new definitions to quantify such variability. The definitions are sufficiently general and can be applied to a broad class of datasets that involve time sequences or features acquired using wearable sensors. The study is motivated by contradictory statements in the literature on the need for user-specific training in activity recognition. We employ our publicly available dataset that contains 19 daily and sports activities acquired from eight participants who wear five motion sensor units each. We pre-process recorded activity time sequences in three different ways and employ absolute, Euclidean and dynamic time warping distance measures to quantify the similarity of the recorded signal patterns. We define and calculate the average inter-subject and inter-activity distances with various methods based on the raw and pre-processed time-domain data as well as on the raw and pre-processed feature vectors. These definitions allow us to identify the subject who performs the activities in the most representative way and pinpoint the activities that show more variation among the subjects. We observe that the type of pre-processing used affects the results of the comparisons but that the different distance measures do not alter the comparison results as much. We check the consistency of our analysis and results by highlighting some of our activity recognition rates based on an exhaustive set of sensor unit, sensor type and subject combinations. We expect the results to be useful for dynamic sensor unit/type selection, for deciding whether to perform user-specific training and for designing more effective classifiers in activity recognition.
      Keywords
      Accelerometer
      Activity recognition and classification
      Dynamic time warping
      Feature extraction
      Feature reduction
      Gyroscope
      Inertial sensors
      Inter-activity variation
      Inter-subject variation
      Magnetometers
      Motion capture
      Motion sensors
      Wearable sensing
      Permalink
      http://hdl.handle.net/11693/36569
      Published Version (Please cite this version)
      http://dx.doi.org/10.1093/comjnl/bxv093
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      • Department of Electrical and Electronics Engineering 3339

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