Improving Classification of Sit, Stand, and Lie in a ... - IEEE Xplore
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Improving Classification of Sit, Stand, and Lie in a ... - IEEE Xplore
Improving Classification of Sit, Stand, and Lie in a. Smartphone Human Activity Recognition System. Nicole A. Capela. Mechanical Engineering, University.
This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.
Improving Classification of Sit, Stand, and Lie in a Smartphone Human Activity Recognition System Nicole A. Capela
Edward D. Lemaire
Natalie Baddour
Mechanical Engineering, University of Ottawa, Ottawa, Canada Ottawa Hospital Research Institute Ottawa, Canada
Ottawa Hospital Research Institute Ottawa, Canada Faculty of Medicine, University of Ottawa, Ottawa, Canada
Dept. of Mechanical Engineering University of Ottawa Ottawa, Canada
Abstract— Human Activity Recognition (HAR) allows healthcare specialists to obtain clinically useful information about a person’s mobility. When characterizing immobile states with a smartphone, HAR typically relies on phone orientation to differentiate between sit, stand, and lie. While phone orientation is effective for identifying when a person is lying down, sitting and standing can be misclassified since pelvis orientation can be similar. Therefore, training a classifier from this data is difficult. In this paper, a hierarchical classifier that includes the transition phases into and out of a sitting state is proposed to improve sitstand classification. For evaluation, young (age 26 ± 8.9 yrs) and senior (age 73 ± 5.9yrs) participants wore a Blackberry Z10 smartphone on their right front waist and performed a continuous series of 16 activities of daily living. Z10 accelerometer and gyroscope data were processed with a custom HAR classifier that used previous state awareness and transition identification to classify immobile states. Immobile state classification results were compared with (WT) and without (WOT) transition identification and previous state awareness. The WT classifier had significantly greater sit sensitivity and Fscore (p
