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Improving human activity recognition using embedded smartphone sensors

Oldengarm, B.M.J. (2020) Improving human activity recognition using embedded smartphone sensors.

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Abstract:This paper describes the process and results of finding the most accurate set of features of the data captured by embedded smartphone sensors to recognize six different activities of daily living. The sensor data of the gyroscope and the accelerometer are processed and trained in the J48 and Naive Bayes classifiers to recognize laying, standing, sitting, walking, going upstairs and going downstairs. Starting with 272 features, around half of these are eliminated by using a Ranker method based on the information gain. Afterwards a Wrapper Subset Evaluator is applied and results in the most accurate set of features for the six activities in both classifiers. By training the classifiers with these sets of best features the accuracy improved up to 28.92%, resulting in an overall accuracy for all activities ranging from 95.32% up to 99.97%.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
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