Minimum Entropy Approach for Multisensor Data Fusion
SPWHOS '97 Proceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics (SPW-HOS '97)
Prototypical case mining from biomedical literature for bootstrapping a case base
Applied Intelligence
Case-based retrieval to support the treatment of end stage renal failure patients
Artificial Intelligence in Medicine
Case-based reasoning in the health sciences: What's next?
Artificial Intelligence in Medicine
Sensor fusion of a railway bridge load test using neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The 4 diabetes support system: a case study in CBR research and development
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
K-NN based interpolation to handle artifacts for heart rate variability analysis
ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Today, clinicians/experts often do the diagnosis of stress, sleepiness and tiredness on the basis of information collected from several physiological sensor signals. Since there are large individual variations when analyzing the sensor measurements and systems with single sensor, they could easily be vulnerable to uncertain noises/interferences in such domain; multiple sensors could provide more robust and reliable decision. Therefore, this paper presents a classification approach i.e. Multivariate Multiscale Entropy Analysis-Case-Based Reasoning (MMSE-CBR) that classifies physiological parameters of wheel loader operators by combining CBR approach with a data level fusion method named Multivariate Multiscale Entropy (MMSE). The MMSE algorithm supports complexity analysis of multivariate biological recordings by aggregating several sensor measurements e.g., Inter-beat-Interval (IBI) and Heart Rate (HR) from Electrocardiogram (ECG), Finger Temperature (FT), Skin Conductance (SC) and Respiration Rate (RR). Here, MMSE has been applied to extract features to formulate a case by fusing a number of physiological signals and the CBR approach is applied to classify the cases by retrieving most similar cases from the case library. Finally, the proposed approach i.e. MMSE-CBR has been evaluated with the data from professional drivers at Volvo Construction Equipment, Sweden. The results demonstrate that the proposed system that fuses information at data level could classify 'stressed' and 'healthy' subjects 83.33% correctly compare to an expert's classification. Furthermore, with another data set the achieved accuracy (83.3%) indicates that it could also classify two different conditions 'adapt' (training) and 'sharp' (real-life driving) for the wheel loader operators. Thus, the new approach of MMSE-CBR could support in classification of operators and may be of interest to researchers developing systems based on information collected from different sensor sources.