Journal
IEEE SENSORS JOURNAL
Volume 19, Issue 20, Pages 8979-8989Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2018.2872894
Keywords
Magnetic sensor; radar sensing; assisted living; feature selection; neural networks; machine learning
Funding
- Doctoral Training Award
- U.K. Engineering and Physical Science Research Council EPSRC [INSHEP EP/R041679/1]
- EPSRC [EP/R041679/1, 1805516] Funding Source: UKRI
Ask authors/readers for more resources
With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyze and verify the validation of fusion method for different classifiers, a support vector machine with a quadratic kernel, and an artificial neural network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available