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dc.contributor.authorBonetto, Riccardo
dc.contributor.authorSoldan, Mattia
dc.contributor.authorLanaro, Alberto
dc.contributor.authorMilani, Simone
dc.contributor.authorRossi, Michele
dc.date.accessioned2019-12-23T10:57:39Z
dc.date.available2019-12-23T10:57:39Z
dc.date.issued2019-11-19
dc.identifier.urihttp://hdl.handle.net/10754/660750
dc.description.abstractSmartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications. Wearables are suitable for indoor and outdoor scenarios, can be placed on many parts of the human body and can integrate a large number of sensors capable of gathering physiological and behavioral biometric information. Here, we are concerned with gait analysis systems that extract meaningful information from a user's movements to identify anomalies and changes in their walking style. The solution that is put forward is subject-specific, as the designed feature extraction and classification tools are trained on the subject under observation. A smartphone mounted on an ad-hoc made chest support is utilized to gather inertial data and video signals from its built-in sensors and rear-facing camera. The collected video and inertial data are preprocessed, combined and then classified by means of a Recurrent Neural Network (RNN) based Sequence-to-Sequence (Seq2Seq) model, which is used as a feature extractor, and a following Convolutional Neural Network (CNN) classifier. This architecture provides excellent results, being able to correctly assess anomalies in 100% of the cases, for the considered tests, surpassing the performance of support vector machine classifiers.
dc.description.sponsorshipThis work has been supported by the University of Padova through the project CPDA 151221 “IoT-SURF”. Any opinions, findings, and conclusions herein are those of the authors and do not necessarily represent those of the funding institution.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1911.08608
dc.rightsArchived with thanks to arXiv
dc.titleSeq2Seq RNN based Gait Anomaly Detection from Smartphone Acquired Multimodal Motion Data
dc.typePreprint
dc.contributor.departmentIVUL Lab, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
dc.eprint.versionPre-print
dc.contributor.institutionInstitute of Communication Technology, Technische Universitat Dresden, 01062, Dresden, Germany
dc.contributor.institutionM31, 35131, Padova, Italy
dc.contributor.institutionDepartment of Information Engineering, University of Padova, 35131, Padova, Italy
dc.identifier.arxivid1911.08608
kaust.personSoldan, Mattia
refterms.dateFOA2019-12-23T10:58:17Z


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