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dc.contributor.advisorBremdal, Bernt Arild
dc.contributor.advisorDanielsen, Asbjørn
dc.contributor.authorDegtiarev, Aleksei
dc.date.accessioned2018-08-21T13:10:14Z
dc.date.available2018-08-21T13:10:14Z
dc.date.issued2018-08-17
dc.description.abstractFalls of elderly people are big health burden, especially for long-term consequence. Yet we already have research, describing how exactly elderly fall and reasons of falls. We aimed to develop means that could not only detect falls and send alerts to relatives and doctors to conquer one of the biggest fears of elderly to fall and do not have the ability to call for help, but also tried to implement fall prevention system. This system based on “relatively safe walking patterns” that our system tries to detect during the walk. During the work we used SensorTag 2.0 CC2650 sensors, iPhone and Apple Watch to collect motion data (Gyroscope, Accelerometer and Magnetometer) and compared the accuracy of each device. As we chosen iPhone and Apple Watch to use Core ML framework to integrate the neural network model we generated using Keras into prototype app. The iPhone app perfectly detects falls, but it needs to collect data more accurately, to improve the machine learning model to improve the work of prediction falls. The Apple Watch app does not work acceptable, despite well prepared Keras model and requires revision.en_US
dc.identifier.urihttps://hdl.handle.net/10037/13510
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2018 The Author(s)
dc.subject.courseIDSHO6264
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550en_US
dc.subjectCC2650en_US
dc.subjectSensorTag 2.0en_US
dc.subjectiPhone, Apple Watch, iOS, watchOSen_US
dc.subjectCore ML, Core Mo- tion, Core Bluetoothen_US
dc.subjectAccelerometeren_US
dc.subjectMagnetometeren_US
dc.subjectGyroscopeen_US
dc.subjectMotion captureen_US
dc.subjectData analysisen_US
dc.subjectNeural networken_US
dc.subjectBluetooth Low Energyen_US
dc.subjectMachine learningen_US
dc.subjectWalkersen_US
dc.titleDetection and prediction of falls among elderly people using walkersen_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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