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Open AccessFeature PaperArticle

Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions

1
Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, Ankara 06800, Turkey
2
Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(8), 2725; https://doi.org/10.3390/s18082725
Received: 5 July 2018 / Revised: 10 August 2018 / Accepted: 15 August 2018 / Published: 19 August 2018
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
Wearable motion sensors are assumed to be correctly positioned and oriented in most of the existing studies. However, generic wireless sensor units, patient health and state monitoring sensors, and smart phones and watches that contain sensors can be differently oriented on the body. The vast majority of the existing algorithms are not robust against placing the sensor units at variable orientations. We propose a method that transforms the recorded motion sensor sequences invariantly to sensor unit orientation. The method is based on estimating the sensor unit orientation and representing the sensor data with respect to the Earth frame. We also calculate the sensor rotations between consecutive time samples and represent them by quaternions in the Earth frame. We incorporate our method in the pre-processing stage of the standard activity recognition scheme and provide a comparative evaluation with the existing methods based on seven state-of-the-art classifiers and a publicly available dataset. The standard system with fixed sensor unit orientations cannot handle incorrectly oriented sensors, resulting in an average accuracy reduction of 31.8%. Our method results in an accuracy drop of only 4.7% on average compared to the standard system, outperforming the existing approaches that cause an accuracy degradation between 8.4 and 18.8%. We also consider stationary and non-stationary activities separately and evaluate the performance of each method for these two groups of activities. All of the methods perform significantly better in distinguishing non-stationary activities, our method resulting in an accuracy drop of 2.1% in this case. Our method clearly surpasses the remaining methods in classifying stationary activities where some of the methods noticeably fail. The proposed method is applicable to a wide range of wearable systems to make them robust against variable sensor unit orientations by transforming the sensor data at the pre-processing stage. View Full-Text
Keywords: activity recognition and monitoring; patient health and state monitoring; wearable sensing; orientation-invariant sensing; motion sensors; accelerometer; gyroscope; magnetometer; pattern classification activity recognition and monitoring; patient health and state monitoring; wearable sensing; orientation-invariant sensing; motion sensors; accelerometer; gyroscope; magnetometer; pattern classification
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MDPI and ACS Style

Yurtman, A.; Barshan, B.; Fidan, B. Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions. Sensors 2018, 18, 2725.

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