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Sensors 2015, 15(9), 23095-23109; doi:10.3390/s150923095

Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors

Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy
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Author to whom correspondence should be addressed.
Academic Editor: Ki H. Chon
Received: 24 July 2015 / Revised: 26 August 2015 / Accepted: 8 September 2015 / Published: 11 September 2015
(This article belongs to the Special Issue Smartphone-Based Sensors for Non-Invasive Physiological Monitoring)
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Abstract

Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. For monitoring and fitness applications, it is crucial to develop methods able to segment each activity cycle, e.g., a gait cycle, so that the successive classification step may be more accurate. To increase detection accuracy, pre-processing is often used, with a concurrent increase in computational cost. In this paper, the effect of pre-processing operations on the detection and classification of locomotion activities was investigated, to check whether the presence of pre-processing significantly contributes to an increase in accuracy. The pre-processing stages evaluated in this study were inclination correction and de-noising. Level walking, step ascending, descending and running were monitored by using a shank-mounted inertial sensor. Raw and filtered segments, obtained from a modified version of a rule-based gait detection algorithm optimized for sequential processing, were processed to extract time and frequency-based features for physical activity classification through a support vector machine classifier. The proposed method accurately detected >99% gait cycles from raw data and produced >98% accuracy on these segmented gait cycles. Pre-processing did not substantially increase classification accuracy, thus highlighting the possibility of reducing the amount of pre-processing for real-time applications. View Full-Text
Keywords: inertial measurement unit; gait event detection; dynamic segmentation; pre-processing; physical activity; classification inertial measurement unit; gait event detection; dynamic segmentation; pre-processing; physical activity; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Fida, B.; Bernabucci, I.; Bibbo, D.; Conforto, S.; Schmid, M. Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors. Sensors 2015, 15, 23095-23109.

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