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Learning from Life-Logging Data by Hybrid HMM: A Case Study on Active States Prediction
- Source :
- Biomedical Engineering.
- Publication Year :
- 2016
- Publisher :
- ACTAPRESS, 2016.
-
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.
- Subjects :
- Computer science
business.industry
Supervised learning
Wearable computer
Pattern recognition
Genetic programming
030229 sport sciences
02 engineering and technology
Markov model
Machine learning
computer.software_genre
Synthetic data
Support vector machine
03 medical and health sciences
ComputingMethodologies_PATTERNRECOGNITION
0302 clinical medicine
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Hidden Markov model
computer
Classifier (UML)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Biomedical Engineering
- Accession number :
- edsair.doi...........787d42f15e125bbc810cb37f3c15bde4