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.
|Title of host publication||Proceedings of the 12th IASTED International Conference on Biomedical Engineering|
|Subtitle of host publication||BioMed 2016|
|Publication status||Published - 2016|
|Event||12th IASTED International Conference on Biomedical Engineering: BioMed 2016 - Innsbruck, Austria|
Duration: 15 Feb 2016 → 16 Feb 2016
|Conference||12th IASTED International Conference on Biomedical Engineering|
|Period||15/02/16 → 16/02/16|