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

Michael B Del Del Rosario,Nigel H Lovell,Stephen J Redmond

doi:10.3390/s19132845

Michael B Del Del Rosario, Nigel H Lovell + Show 1 more

Open Access

https://doi.org/10.3390/s19132845

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Abstract

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%]).

Highlights

Wearable movement sensors, i.e., sensors that incorporate inertial measurement units (IMUs) and barometric altimeters, have been championed as tools that will positively impact health care [1]
Wearables have been adopted for the longitudinal monitoring of physical activity, which can be used to identify those at risk of developing type-2 diabetes [9] and obesity [10]
Note in the sections that follow, (i) quaternion multiplication (⊗) and conjugation (∗ ) are defined in [58]; (ii) vectors are bold-faced (i.e., b); (iii) quaternions are bold-faced, italicized and normalized unless explicitly stated (i.e., q = q/||q||); (iv) vectors expressed in the sensor frame, or estimated in the global frame of reference, will be denoted with the superscripts s b, and g b, respectively; (v) a function will be denoted as f (. . . ) with arguments inside the brackets

Summary

Introduction

I.e., sensors that incorporate inertial measurement units (IMUs) and barometric altimeters, have been championed as tools that will positively impact health care [1]. These technologies have demonstrated their utility in the remote monitoring of patient rehabilitation [2], as well as the clinical analysis of gait [3], from which parameters can be extracted to predict falls in the elderly [4,5].

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