dr. A. John (Arlene)

Arlene John is an Assistant Professor at the Biomedical Signals and Systems (BSS) group.

Arlene did her bachelor studies in Electrical and Electronics engineering at the National Institute of Technology, Calicut, India, in 2017. In the summer of 2016, she was a Research Intern with the Indian Institute of Science, Bangalore, India. After her graduation, she was with Bosch India Ltd., as a Project Manager in Technical Sales, and in engineering and strategy development for hybrid electric vehicles. Further, she completed her Ph.D. at the School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland in 2022. Her research topic focussed on the development of data fusion frameworks for wearable health monitoring devices. From March–June 2019, she was a Senior Visiting Researcher at the Beijing University of Technology, Beijing, China. She also worked as a Machine Learning Intern with Qualcomm Cork, Ireland in 2021. After her Ph.D., she worked as Machine Learning Mathematics Engineer at ASML Netherlands B.V. until April 2023.

Her research interests include biomedical signal processing, machine learning and inference, explainable AI, and multisensor data fusion,.

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Biomedical Signals and Systems (BSS)

John, A., Padinjarathala, A., Doheny, E., Cardiff, B., & John, D. (2023). An evaluation of ECG data fusion algorithms for wearable IoT sensors. Information Fusion, 96, 237-251. https://doi.org/10.1016/j.inffus.2023.03.017

John, A., Kumar Nundy, K., Cardiff, B., & John, D. (2021). SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1961-1964) https://doi.org/10.1109/EMBC46164.2021.9631037

John, A., Cardiff, B., & John, D. (2021). A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). (Proceedings IEEE International Symposium on Circuits and Systems (ISCAS); Vol. 2021). IEEE. https://doi.org/10.1109/ISCAS51556.2021.9401300

John, A., Cardiff, B., & John, D. (2021). A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors. ArXiv.org.

John, A., Kumar Nundy, K., Cardiff, B., & John, D. (2021). Multimodal Multiresolution Data Fusion Using Convolutional Neural Networks for IoT Wearable Sensing. IEEE Transactions on Biomedical Circuits and Systems, 15(6), 1161-1173. https://doi.org/10.1109/TBCAS.2021.3134043

John, A., Redmond, S., Cardiff, B., & John, D. (2021). A multimodal data fusion technique for heartbeat detection in wearable IoT sensors. IEEE Internet of Things Journal, 9(3), 2071-2082. https://doi.org/10.1109/JIOT.2021.3093112

John, A., Sadasivan, J., & Seelamantula, C. S. (2021). Adaptive Savitzky-Golay Filtering in Non-Gaussian Noise. IEEE transactions on signal processing, 69, 5021-5036. https://doi.org/10.1109/TSP.2021.3106450

John, A., Ullah, S., Kumar, A., Cardiff, B., & John, D. (2020). An Approximate Binary Classifier for Data Integrity Assessment in IoT Sensors. In 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS) https://doi.org/10.1109/icecs49266.2020.9294859

John, A., Cardiff, B., & John, D. (2020). A Generalized Signal Quality Estimation Method for IoT Sensors. In 2020 IEEE International Symposium on Circuits and Systems (ISCAS) https://doi.org/10.1109/ISCAS45731.2020.9180546

John, A., C Panicker, R., Cardiff, B., Lian, Y., & John, D. (2020). Binary Classifiers for Data Integrity Detection in Wearable IoT Edge Devices. IEEE Open Journal of Circuits and Systems, 1, 88-99. https://doi.org/10.1109/OJCAS.2020.3009520

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