Evaluating state-of-the-art classifiers for human activity recognition using smartphones
Evaluating state-of-the-art classifiers for human activity recognition using smartphones
- Author(s): A. Lentzas ; A. Agapitos ; D. Vrakas
- DOI: 10.1049/cp.2019.0098
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- Author(s): A. Lentzas ; A. Agapitos ; D. Vrakas Source: 3rd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2019), 2019 page (6 pp.)
- Conference: 3rd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2019)
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- DOI: 10.1049/cp.2019.0098
- ISBN: 978-1-83953-088-3
- Location: London, UK
- Conference date: 25 March 2019
- Format: PDF
Human activity recognition using smartphones and wearables is a field gathering a lot of attention. Although a plethora of systems have been proposed in the literature, comparing their results is not an easy task. As a universal evaluation framework is absent, direct comparison is not feasible. This paper compares state-of-the-art classifiers already used on mobile human activity recognition, under the same conditions. In addition, an Android application was developed and the method yielding the best results was evaluated in real world in a semi-supervised environment. Results shown that deep learning techniques have better performance and could be transferred to a phone without many modifications.
Inspec keywords: smart phones; learning (artificial intelligence); pattern classification; mobile computing
Subjects: Knowledge engineering techniques; Other topics in statistics; Mobile, ubiquitous and pervasive computing; Data handling techniques
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