Monitoring the rehabilitation progress using a DCNN and kinematic data for digital healthcare

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dc.authorid0000-0002-4916-1898
dc.contributor.authorAlcaraz, Javier Conte
dc.contributor.authorMoghaddamnia, Sanam
dc.contributor.authorPenner, Maxim
dc.contributor.authorPeissig, Jürgen
dc.date.accessioned2021-05-17T09:07:29Z
dc.date.available2021-05-17T09:07:29Z
dc.date.issued2021
dc.departmentTAÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractMonitoring the progress of patients during the rehabilitation process after an operation is beneficial for adjusting care and medical treatment in order to improve the patient's quality of life. The supervised methods used for this in the literature need data labeling, which is a time and cost-intensive procedure. In this paper, we propose Deep Convolutional Neural Network (DCNN) for monitoring the progress of the rehabilitation, utilizing the kinematic data from a Wearable Sensor System (WSS). The WSS provides three-dimensional linear acceleration and angular velocity from multiple body parts such as the lower back and lower limbs during walking at any speed on level ground. Twelve patients with hip unilateral arthroplasty completed two weeks of gait training after the operation. The classification results of different Inertial Measurement Unit (IMU) placements revealed that the IMU placed at thigh achieved the highest accuracy. The proposed DCNN achieved up to 98% classification accuracy for the rehabilitation progress monitoring. This approach provides an objective and evidence-based way of understanding clinically important changes in human movement patterns in response to exercise therapy.
dc.identifier.citationAlcaraz, J. C., Moghaddamnia, S., Penner, M., & Peissig, J. (2021). Monitoring the Rehabilitation Progress Using a DCNN and Kinematic Data for Digital Healthcare. In 2020 28th European Signal Processing Conference (EUSIPCO) (pp. 1333-1337). IEEE.
dc.identifier.doi10.23919/Eusipco47968.2020.9287324
dc.identifier.endpage1337en_US
dc.identifier.scopus2-s2.0-85099276267
dc.identifier.scopusqualityN/A
dc.identifier.startpage1333en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12846/585
dc.identifier.wosWOS:000632622300268
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorMoghaddamnia, Sanam
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.ispartof28th European Signal Processing Conference (EUSIPCO 2020)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGait Rehabilitationen_US
dc.subjectProgress Monitoringen_US
dc.subjectMachine Learningen_US
dc.subjectDigital Healthcare and Therapy Controlen_US
dc.subjectYürüyüş Rehabilitasyonuen_US
dc.subjectDijital Sağlık ve Terapi Kontrolüen_US
dc.subjectGangrehabilitationen_US
dc.subjectFortschrittsüberwachungen_US
dc.subjectMaschinelles Lernenen_US
dc.subjectDigitale Gesundheits- und Therapiekontrolleen_US
dc.titleMonitoring the rehabilitation progress using a DCNN and kinematic data for digital healthcare
dc.typeConference Object

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