Towards fully instrumented and automated assessment of motor function tests

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dc.contributor.authorDe Luca, Valeria-
dc.contributor.authorMuaremi, Amir-
dc.contributor.authorGiggins, Oonagh M.- IEEEen_US
dc.description2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Nevada, United States of America, 4-7 Marchen_US
dc.description.abstractQuantitative assessment of mobility and motor function is critical to our understanding and treatment of musculoskeletal and neurological diseases. Instrumented tests augment traditional approaches by moving from a single, often subjective, performance metric to multiple objective measures. In this study, we investigated ways of automatically capturing motor performance by leveraging data from a network of six wearable sensors worn at five different locations by 17 healthy volunteers while performing a battery of motor function tests. We developed a framework to segment motor tasks, e.g. walking and standing up, from 3D acceleration and angular velocity data, and extracted features. Results were compared to clinical test scores and manual annotations of the data. For the best performing sensors, we achieved a rate of correct classification of 82 to 100% and mean temporal accuracy of 0.1 to 0.6 s. We provided guidelines on sensor placement to maximize accuracy of the motor assessment, and a better interpretation of the data using our unsupervised subject-specific approach.en_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectPhysical activityen_US
dc.subjectActivity monitoringen_US
dc.subjectMotor function testsen_US
dc.titleTowards fully instrumented and automated assessment of motor function testsen_US
dc.typeJournal Articleen_US
dc.statusPeer revieweden_US
dc.neeo.contributorDe Luca|Valeria|aut|-
dc.neeo.contributorGiggins|Oonagh M.|aut|-
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