title: Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors.
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.
authors.affiliation: Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy. fida.benish@uniroma3.it.
authors.forename: Benish
authors.initials: B
authors.lastname: Fida
authors.affiliation: Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy. ivan.bernabucci@uniroma3.it.
authors.forename: Ivan
authors.initials: I
authors.lastname: Bernabucci
authors.affiliation: Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy. daniele.bibbo@uniroma3.it.
authors.forename: Daniele
authors.initials: D
authors.lastname: Bibbo
authors.affiliation: Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy. silvia.conforto@uniroma3.it.
authors.forename: Silvia
authors.initials: S
authors.lastname: Conforto
authors.affiliation: Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy. Maurizio.schmid@uniroma3.it.
authors.forename: Maurizio
authors.initials: M
authors.lastname: Schmid
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000328:Adult; D000465:Algorithms; D005684:Gait / Q000502:physiology; D006802:Human Activities / Q000145:classification*; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: classification; dynamic segmentation; gait event detection; inertial measurement unit; physical activity; pre-processing