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A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices

Department of Computer Science, University Carlos III of Madrid, 28911 Leganés, Madrid, Spain
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Sensors 2019, 19(3), 521; https://doi.org/10.3390/s19030521
Received: 19 December 2018 / Revised: 16 January 2019 / Accepted: 22 January 2019 / Published: 26 January 2019
(This article belongs to the Special Issue From Sensors to Ambient Intelligence for Health and Social Care)
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Abstract

We have compared the performance of different machine learning techniques for human activity recognition. Experiments were made using a benchmark dataset where each subject wore a device in the pocket and another on the wrist. The dataset comprises thirteen activities, including physical activities, common postures, working activities and leisure activities. We apply a methodology known as the activity recognition chain, a sequence of steps involving preprocessing, segmentation, feature extraction and classification for traditional machine learning methods; we also tested convolutional deep learning networks that operate on raw data instead of using computed features. Results show that combination of two sensors does not necessarily result in an improved accuracy. We have determined that best results are obtained by the extremely randomized trees approach, operating on precomputed features and on data obtained from the wrist sensor. Deep learning architectures did not produce competitive results with the tested architecture. View Full-Text
Keywords: deep learning; activity recognition; mHealth; safe environment; active aging; ambient-assisted living deep learning; activity recognition; mHealth; safe environment; active aging; ambient-assisted living
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Baldominos, A.; Cervantes, A.; Saez, Y.; Isasi, P. A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices. Sensors 2019, 19, 521.

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