Facilitating healthcare using smartwatches

Bachelor thesis (2022)

Authors

R. Cavalini Electrical Engineering, Mathematics and Computer Science
I. Hassan Electrical Engineering, Mathematics and Computer Science
T. Pouwels Electrical Engineering, Mathematics and Computer Science

Contributors

Bahareh Abdi Electrical Engineering Education - EEMCS (supervisor 1)
A.J. van der Veen Signal Processing Systems - EEMCS (supervisor 1)
R.M.A. van Puffelen Electronic Instrumentation - EEMCS (supervisor 2)
Pasqualina M Sarro Electronic Components, Technology and Materials - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Ricardo Cavalini, Ibrahim Hassan, Thomas Pouwels
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Published Date

21-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

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 approach
with 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.