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Open AccessFeature PaperArticle

Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities

1
Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia
2
UCD School of Electrical and Electronic Engineering, University College Dublin, Belfield, 4 Dublin, Ireland
3
UCD Centre for Biomedical Engineering, University College Dublin, Belfield, 4 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(13), 2845; https://doi.org/10.3390/s19132845
Received: 14 May 2019 / Revised: 18 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)
Features were developed which accounted for the changing orientation of the inertial measurement unit (IMU) relative to the body, and demonstrably improved the performance of models for human activity recognition (HAR). The method is proficient at separating periods of standing and sedentary activity (i.e., sitting and/or lying) using only one IMU, even if it is arbitrarily oriented or subsequently re-oriented relative to the body; since the body is upright during walking, learning the IMU orientation during walking provides a reference orientation against which sitting and/or lying can be inferred. Thus, the two activities can be identified (irrespective of the cohort) by analyzing the magnitude of the angle of shortest rotation which would be required to bring the upright direction into coincidence with the average orientation from the most recent 2.5 s of IMU data. Models for HAR were trained using data obtained from a cohort of 37 older adults (83.9 ± 3.4 years) or 20 younger adults (21.9 ± 1.7 years). Test data were generated from the training data by virtually re-orienting the IMU so that it is representative of carrying the phone in five different orientations (relative to the thigh). The overall performance of the model for HAR was consistent whether the model was trained with the data from the younger cohort, and tested with the data from the older cohort after it had been virtually re-oriented (Cohen’s Kappa 95% confidence interval [0.782, 0.793]; total class sensitivity 95% confidence interval [84.9%, 85.6%]), or the reciprocal scenario in which the model was trained with the data from the older cohort, and tested with the data from the younger cohort after it had been virtually re-oriented (Cohen’s Kappa 95% confidence interval [0.765, 0.784]; total class sensitivity 95% confidence interval [82.3%, 83.7%]). View Full-Text
Keywords: quaternion; smartphone; feature engineering; human activity recognition; sensor fusion quaternion; smartphone; feature engineering; human activity recognition; sensor fusion
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Del Rosario, M.B.; Lovell, N.H.; Redmond, S.J. Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities. Sensors 2019, 19, 2845.

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