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Sensors
Volume 15
Issue 9
10.3390/s150923095
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Article

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

by
Benish Fida
*,
Ivan Bernabucci
,
Daniele Bibbo
,
Silvia Conforto
and
Maurizio Schmid
Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Ki H. Chon
Sensors 2015, 15(9), 23095-23109; https://doi.org/10.3390/s150923095
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.
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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. https://doi.org/10.3390/s150923095

AMA 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(9):23095-23109. https://doi.org/10.3390/s150923095

Chicago/Turabian Style

Fida, Benish, Ivan Bernabucci, Daniele Bibbo, Silvia Conforto, and Maurizio Schmid. 2015. "Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors" Sensors 15, no. 9: 23095-23109. https://doi.org/10.3390/s150923095

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