Print Email Facebook Twitter Facilitating healthcare using smartwatches Title Facilitating healthcare using smartwatches Author Cavalini, Ricardo (TU Delft Electrical Engineering, Mathematics and Computer Science) Hassan, Ibrahim (TU Delft Electrical Engineering, Mathematics and Computer Science) Pouwels, Thomas (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Abdi, Bahareh (mentor) van der Veen, A.J. (mentor) van Puffelen, R.M.A. (graduation committee) Sarro, Pasqualina M (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Circuits and Systems Date 2022-06-21 Abstract This report entails one of two subsystems in a joint project to provide a web-based platform for smartwatch data acquisition, for applications in healthcare. In this work, we design and implement algorithms for human activity recognition using various machine learning approaches and test them on data acquired online as well as using our own developed platform. Together with the web-based platform, this provides a solid base for more research using data gathered from smartwatches. The human activity recognition is implemented first using a classical machine learning approachwith feature extraction and a random forest classifier. Next, both convolutional neural network and a recurrent neural network are implemented using Tensorflow [1]. We further perform several tests to investigate: (i) the optimal segment size with respect to classification accuracy, (ii) the effect of filtering and preprocessing on the classification results, and (iii) the best classifier for activity detection. Subject HealthcaresmartwatchArtifical Intelligence To reference this document use: http://resolver.tudelft.nl/uuid:a7e051e5-0e2e-4a98-bc09-6999195f510f Part of collection Student theses Document type bachelor thesis Rights © 2022 Ricardo Cavalini, Ibrahim Hassan, Thomas Pouwels Files PDF BAP_Thesis_Classification.pdf 2.34 MB Close viewer /islandora/object/uuid:a7e051e5-0e2e-4a98-bc09-6999195f510f/datastream/OBJ/view