title: Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer.
abstract: Accuracy of systems able to recognize in real time daily living activities heavily depends on the processing step for signal segmentation. So far, windowing approaches are used to segment data and the window size is usually chosen based on previous studies. However, literature is vague on the investigation of its effect on the obtained activity recognition accuracy, if both short and long duration activities are considered. In this work, we present the impact of window size on the recognition of daily living activities, where transitions between different activities are also taken into account. The study was conducted on nine participants who wore a tri-axial accelerometer on their waist and performed some short (sitting, standing, and transitions between activities) and long (walking, stair descending and stair ascending) duration activities. Five different classifiers were tested, and among the different window sizes, it was found that 1.5 s window size represents the best trade-off in recognition among activities, with an obtained accuracy well above 90%. Differences in recognition accuracy for each activity highlight the utility of developing adaptive segmentation criteria, based on the duration of the activities.
authors.affiliation: Engineering Department, Roma Tre University, Via Vito Volterra, 62, 00146 Rome, Italy. Electronic address: fida.benish@uniroma3.it.
authors.forename: Benish
authors.initials: B
authors.lastname: Fida
authors.affiliation: Engineering Department, Roma Tre University, Via Vito Volterra, 62, 00146 Rome, Italy.
authors.forename: Ivan
authors.initials: I
authors.lastname: Bernabucci
authors.affiliation: Engineering Department, Roma Tre University, Via Vito Volterra, 62, 00146 Rome, Italy.
authors.forename: Daniele
authors.initials: D
authors.lastname: Bibbo
authors.affiliation: Engineering Department, Roma Tre University, Via Vito Volterra, 62, 00146 Rome, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Conforto
authors.affiliation: Engineering Department, Roma Tre University, Via Vito Volterra, 62, 00146 Rome, Italy.
authors.forename: Maurizio
authors.initials: M
authors.lastname: Schmid
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods*; D000203:Activities of Daily Living; D000328:Adult; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D065823:Parabrachial Nucleus; D010363:Pattern Recognition, Automated / Q000379:methods*; D011187:Posture / Q000502:physiology; D013997:Time Factors; D016138:Walking / Q000502:physiology; D055815:Young Adult
keywords: Accelerometer; Classification; Physical activity recognition; Segmentation; Window size