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dc.contributor.authorAwais, Muhammad
dc.contributor.authorPalmerini, Luca
dc.contributor.authorBourke, Alan
dc.contributor.authorIhlen, Espen Alexander F.
dc.contributor.authorHelbostad, Jorunn L.
dc.contributor.authorChiari, Lorenzo
dc.date.accessioned2020-04-16T14:12:57Z
dc.date.available2020-04-16T14:12:57Z
dc.date.created2017-01-18T14:37:02Z
dc.date.issued2016
dc.identifier.citationSensors. 2016, 16 .en_US
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/11250/2651364
dc.description.abstractThe popularity of using wearable inertial sensors for physical activity classification has dramatically increased in the last decade due to their versatility, low form factor, and low power requirements. Consequently, various systems have been developed to automatically classify daily life activities. However, the scope and implementation of such systems is limited to laboratory-based investigations. Furthermore, these systems are not directly comparable, due to the large diversity in their design (e.g., number of sensors, placement of sensors, data collection environments, data processing techniques, features set, classifiers, cross-validation methods). Hence, the aim of this study is to propose a fair and unbiased benchmark for the field-based validation of three existing systems, highlighting the gap between laboratory and real-life conditions. For this purpose, three representative state-of-the-art systems are chosen and implemented to classify the physical activities of twenty older subjects (76.4 ± 5.6 years). The performance in classifying four basic activities of daily life (sitting, standing, walking, and lying) is analyzed in controlled and free living conditions. To observe the performance of laboratory-based systems in field-based conditions, we trained the activity classification systems using data recorded in a laboratory environment and tested them in real-life conditions in the field. The findings show that the performance of all systems trained with data in the laboratory setting highly deteriorates when tested in real-life conditions, thus highlighting the need to train and test the classification systems in the real-life setting. Moreover, we tested the sensitivity of chosen systems to window size (from 1 s to 10 s) suggesting that overall accuracy decreases with increasing window size. Finally, to evaluate the impact of the number of sensors on the performance, chosen systems are modified considering only the sensing unit worn at the lower back. The results, similarly to the multi-sensor setup, indicate substantial degradation of the performance when laboratory-trained systems are tested in the real-life setting. This degradation is higher than in the multi-sensor setup. Still, the performance provided by the single-sensor approach, when trained and tested with real data, can be acceptable (with an accuracy above 80%).en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titlePerformance Evaluation of State of the Art Systems for Psysical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Studyen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber15en_US
dc.source.volume16en_US
dc.source.journalSensorsen_US
dc.identifier.doi10.3390/s16122105
dc.identifier.cristin1431032
dc.description.localcodeThis 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.en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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