Efficiency of deep neural networks for joint angle modeling in digital gait assessment

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dc.authorid0000-0002-4916-1898
dc.contributor.authorAlcaraz, Javier Conte
dc.contributor.authorMoghaddamnia, Sanam
dc.contributor.authorPeissig, Jürgen
dc.date.accessioned2021-04-05T08:04:58Z
dc.date.available2021-04-05T08:04:58Z
dc.date.issued2021
dc.departmentTAÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractReliability and user compliance of the applied sensor system are two key issues of digital healthcare and biomedical informatics. For gait assessment applications, accurate joint angle measurements are important. Inertial measurement units (IMUs) have been used in a variety of applications and can also provide significant information on gait kinematics. However, the nonlinear mechanism of human locomotion results in moderate estimation accuracy of the gait kinematics and thus joint angles. To develop “digital twins” as a digital counterpart of body lower limb joint angles, three-dimensional gait kinematic data were collected. This work investigates the estimation accuracy of different neural networks in modeling lower body joint angles in the sagittal plane using the kinematic records of a single IMU attached to the foot. The evaluation results based on the root mean square error (RMSE) show that long short-term memory (LSTM) networks deliver superior performance in nonlinear modeling of the lower limb joint angles compared to other machine learning (ML) approaches. Accordingly, deep learning based on the LSTM architecture is a promising approach in modeling of gait kinematics using a single IMU, and thus can reduce the required physical IMUs attached on the subject and improve the practical application of the sensor system.
dc.identifier.citationAlcaraz, J. C., Moghaddamnia, S., & Peissig, J. (2021). Efficiency of deep neural networks for joint angle modeling in digital gait assessment. EURASIP Journal on Advances in Signal Processing, 2021(1), 1-20.
dc.identifier.doi10.1186/s13634-020-00715-1
dc.identifier.endpage20en_US
dc.identifier.issn1687-6180
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85100690242
dc.identifier.scopusqualityQ2
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12846/572
dc.identifier.volume2021en_US
dc.identifier.wosWOS:000616311900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorMoghaddamnia, Sanam
dc.language.isoen
dc.publisherSpringer Open
dc.relation.ispartofEURASIP Journal on Advances in Signal Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Neural Networken_US
dc.subjectDigital Gait Analysisen_US
dc.subjectMachine Learningen_US
dc.subjectNonlinear Modelingen_US
dc.subjectInertial Measurement Uniten_US
dc.subjectDerin Sinir Ağıen_US
dc.subjectDijital Yürüyüş Analizien_US
dc.subjectDoğrusal Olmayan Modellemeen_US
dc.subjectTiefes Neuronalesen_US
dc.subjectNetzwerk Digitale Ganganalyseen_US
dc.subjectMaschinelles Lernenen_US
dc.subjectNichtlineare Modellierungen_US
dc.subjectTrägheitsmesseinheiten_US
dc.titleEfficiency of deep neural networks for joint angle modeling in digital gait assessment
dc.typeArticle

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