A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks
| Autor: | Muhammad Muzammal, Ali Hassan Sodhro, Romana Talat, Sandeep Pirbhulal |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Signal processing
business.industry Computer science Wearable computer Estimator 020206 networking & telecommunications 02 engineering and technology Sensor fusion computer.software_genre Random forest Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Wireless 020201 artificial intelligence & image processing Data mining business Wireless sensor network computer Classifier (UML) Software Information Systems |
| Zdroj: | Information Fusion. 53:155-164 |
| ISSN: | 1566-2535 |
| DOI: | 10.1016/j.inffus.2019.06.021 |
| Popis: | Wireless Body Sensor Network (BSNs) are wearable sensors with varying sensing, storage, computation, and transmission capabilities. When data is obtained from multiple devices, multi-sensor fusion is desirable to transform potentially erroneous sensor data into high quality fused data. In this work, a data fusion enabled Ensemble approach is proposed to work with medical data obtained from BSNs in a fog computing environment. Daily activity data is obtained from a collection of sensors which is fused together to generate high quality activity data. The fused data is later input to an Ensemble classifier for early heart disease prediction. The ensembles are hosted in a Fog computing environment and the prediction computations are performed in a decentralised manners. The results from the individual nodes in the fog computing environment are then combined to produce a unified output. For the classification purpose, a novel kernel random forest ensemble is used that produces significantly better quality results than random forest. An extensive experimental study supports the applicability of the solution and the obtained results are promising, as we obtain 98% accuracy when the tree depth is equal to 15, number of estimators is 40, and 8 features are considered for the prediction task. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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