Towards environment-aware fall risk assessment: Classifying walking surface conditions using IMU-Based Gait Data and deep learning

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dc.authorid0000-0002-1432-8360en_US
dc.contributor.authorYıldız, Abdulnasır
dc.date.accessioned2024-03-06T12:06:44Z
dc.date.available2024-03-06T12:06:44Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractFall risk assessment (FRA) helps clinicians make decisions about the best preventative measures to lower the risk of falls by identifying the different risks that are specific to an individual. With the development of wearable technologies such as inertial measurement units (IMUs), several free-living FRA methods based on fall predictors derived from IMU-based data have been introduced. The performance of such methods could be improved by increasing awareness of the individuals’ walking environment. This study aims to introduce and analyze a 25-layer convolutional neural network model for classifying nine walking surface conditions using IMU-based gait data, providing a basis for environment-aware FRAs. A database containing data collected from thirty participants who wore six IMU sensors while walking on nine surface conditions was employed. A systematic analysis was conducted to determine the effects of gait signals (acceleration, magnetic field, and rate of turn), sensor placement, and signal segment size on the method’s performance. Accuracies of 0.935 and 0.969 were achieved using a single and dual sensor, respectively, reaching an accuracy of 0.971 in the best-case scenario with optimal settings. The findings and analysis can help to develop more reliable and interpretable fall predictors, eventually leading to environment-aware FRA methods.en_US
dc.identifier.citationYıldız, A. (2023). Towards environment-aware fall risk assessment: Classifying walking surface conditions using IMU-Based Gait Data and deep learning. Brain Sciences, 13(10), 1-19.en_US
dc.identifier.doi10.3390/brainsci13101428
dc.identifier.endpage19en_US
dc.identifier.issn2076-3425
dc.identifier.issue10en_US
dc.identifier.pmid37891797
dc.identifier.scopus2-s2.0-85174928349
dc.identifier.scopusqualityQ3
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.mdpi.com/2076-3425/13/10/1428
dc.identifier.urihttps://hdl.handle.net/11468/13533
dc.identifier.volume13en_US
dc.identifier.wosWOS:001094191000001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorYıldız, Abdulnasır
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofBrain Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectFall risk analysisen_US
dc.subjectInertial measurement unitsen_US
dc.subjectIrregular walking surfacesen_US
dc.subjectWalking surface detectionen_US
dc.titleTowards environment-aware fall risk assessment: Classifying walking surface conditions using IMU-Based Gait Data and deep learningen_US
dc.titleTowards environment-aware fall risk assessment: Classifying walking surface conditions using IMU-Based Gait Data and deep learning
dc.typeArticleen_US

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