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      • Department of Electrical and Electronics Engineering
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      Activity recognition invariant towearable sensor unit orientation using differential rotational transformations represented by quaternions

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      Author(s)
      Yurtman, Aras
      Barshan, Billur
      Fidan B.
      Date
      2018
      Source Title
      Sensors (Switzerland)
      Print ISSN
      1424-8220
      Publisher
      MDPI AG
      Volume
      18
      Issue
      8
      Pages
      2725-1 - 2725-27
      Language
      English
      Type
      Article
      Item Usage Stats
      450
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      158
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      Abstract
      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.
      Keywords
      Accelerometer
      Activity recognition and monitoring
      Gyroscope
      Magnetometer
      Motion sensors
      Orientation-invariant sensing
      Patient health and state monitoring
      Pattern classification
      Wearable sensing
      Permalink
      http://hdl.handle.net/11693/50438
      Published Version (Please cite this version)
      https://doi.org/10.3390/s18082725
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      • Department of Electrical and Electronics Engineering 4011
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