Using Gravity to Estimate Accelerometer Orientation
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Evolutionary rule-based systems for imbalanced data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Secured WSN-integrated cloud computing for u-life care
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
IEEE Transactions on Information Technology in Biomedicine
Human activity recognition from accelerometer data using a wearable device
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A bag-of-features-based framework for human activity representation and recognition
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Modeling human activity semantics for improved recognition performance
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
A rule-based approach to activity recognition
KICSS'10 Proceedings of the 5th international conference on Knowledge, information, and creativity support systems
IEEE Transactions on Information Technology in Biomedicine
Weighted Fuzzy Rule Interpolation Based on GA-Based Weight-Learning Techniques
IEEE Transactions on Fuzzy Systems
Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
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Activity recognition is an emerging field of research that enables a large number of human-centric applications in the u-healthcare domain. Currently, there are major challenges facing this field, including creating devices that are unobtrusive and handling uncertainties associated with dynamic activities. In this paper, we propose a novel Evolutionary Fuzzy Model (EFM) to measure the uncertainties associated with dynamic activities and relax the domain knowledge constraints which are imposed by domain experts during the development of fuzzy systems. Based on the time and frequency domain features, we define the fuzzy sets and estimate the natural grouping of data through expectation maximization of the likelihoods. A聽Genetic Algorithm (GA) is investigated and designed to determine the optimal fuzzy rules. To evaluate the EFM, we performed experiments on seven daily life activities of ten human subjects. Our experiments show significant improvement of 9聽% in class-accuracy and 11聽% in the F-measures of recognized activities compared to existing counterparts. The practical solution to dynamic activity recognition problems is expected to be an EFM, due to EFM's utilization of smartphones and natural way of handling uncertainties.