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Activity Recognition over Temporal Distance using Supervised Learning in the Context of Dementia Diagnostics

dc.contributor.authorStaab, Sergio
dc.contributor.authorBröning, Lukas
dc.contributor.authorLuderschmidt, Johannes
dc.contributor.authorMartin, Ludger
dc.contributor.editorMühlhäuser, Max
dc.contributor.editorReuter, Christian
dc.contributor.editorPfleging, Bastian
dc.contributor.editorKosch, Thomas
dc.contributor.editorMatviienko, Andrii
dc.contributor.editorGerling, Kathrin|Mayer, Sven
dc.contributor.editorHeuten, Wilko
dc.contributor.editorDöring, Tanja
dc.contributor.editorMüller, Florian
dc.contributor.editorSchmitz, Martin
dc.description.abstractIn the case of neurological diseases, the progression of the disease can be detected by monitoring movements and activities. Documenting such monitoring requires time-consuming work, which can hardly be covered in the context of a constantly decreasing availability of nursing staff. In cooperation with two dementia residential communities, we incrementally develop a process that supports the nursing staff by providing an approach for a semiautomated documentation. This paper presents an approach to aggregate individual activities over a care period using smartwatches in combination with supervised learning algorithms. A smartwatch offers the opportunity to integrate sensor technology into a patient’s daily routine without disturbing them, as many patients already wear watches. We are investigating promising combinations of sensor technologies and supervised learning algorithms, collecting data from the accelerometer, heart rate sensor, gyroscope, gravity and position sensor at 20 Hz and sending it to a web server. The activities are then classified multiple times using Fast Forest, Logistic Regression and Support Vector Machines over a maintenance layer. We present an activity classification prototype over time distance for automated activity recognition, which, after a number of classifications and the likelihood of these, suggests to the nurse a statement of activities over the respective time period of a nursing shift, in the form of a completed documentation. In addition, the work provides an interpretation of how the knowledge gained can be used to recognise motor skills in the course of caring for patients with neurological diseases.de
dc.relation.ispartofMensch und Computer 2022 - Tagungsband
dc.relation.ispartofseriesMensch und Computer
dc.subjectHuman Motion Analysis
dc.subjectMachine Learning
dc.subjectHealth Informatics
dc.titleActivity Recognition over Temporal Distance using Supervised Learning in the Context of Dementia Diagnosticsde
dc.typeText/Conference Paper
gi.citation.publisherPlaceNew York
gi.conference.date4.-7. September 2022
gi.conference.sessiontitleMCI-SE04: Artificial Intelligence