Learning from life-logging data by hybrid HMM: A case study on active states prediction

J. Ni, T. Lambrou, X. Ye

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

1 Citation (Scopus)

Abstract

In this paper, we have proposed employing a hybrid classifier-hidden Markov model (HMM) as a supervised learning approach to recognize daily active states from sequential life-logging data collected from wearable sensors. We generate synthetic data from real dataset to cope with noise and incompleteness for training purpose and, in conjunction with HMM, propose using a multiobjective genetic programming (MOGP) classifier in comparison of the support vector machine (SVM) with variant kernels. We demonstrate that the system with either algorithm works effectively to recognize personal active states regarding medical reference. We also illustrate that MOGP yields generally better results than SVM without requiring an ad hoc kernel.
Original languageEnglish
Title of host publicationProceedings of the 12th IASTED International Conference on Biomedical Engineering
Subtitle of host publicationBioMed 2016
PublisherACTA Press
Pages70-74
Volume832
DOIs
Publication statusPublished - 2016
Event12th IASTED International Conference on Biomedical Engineering: BioMed 2016 - Innsbruck, Austria
Duration: 15 Feb 201616 Feb 2016

Conference

Conference12th IASTED International Conference on Biomedical Engineering
Period15/02/1616/02/16

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