Using Machine Learning for Activity Recognition in Running Exercise
Abstract: Human activity recognition (HAR) is a growing area within machine learning as the possible applications are vast, especially with the growing amount of collectable sensor data as Internet of Things-devices are becoming more accessible. This project aims to contribute to HAR by developing two supervised machine learning algorithms that are able to distinguish between four different human activities. We collected data from the tri-axial accelerometer in two different smartphones while doing these activities, and put together a dataset. The algorithms that were used was a convolutional neural network (CNN) and a support vector machine (SVM), and they were applied to the dataset separately. The results show that it is possible to accurately classify the activities using the algorithms and that a short time window of 3 seconds is enough to classify the activities with an accuracy of over 99% with both algorithms. The SVM outperformed the CNN slightly. We also discuss the result and continuations of this study.
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