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,.
A. John, S. K. Nundy, B. Cardiff, and D. John, ``Multimodal Multiresolution Data Fusion Using Convolutional Neural Networks for Wearable Sensing'', in IEEE Transactions on Biomedical Circuits and Systems, 2021.
A. John, S. K. Nundy, B. Cardiff, and D. John, ``SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches'', in IEEE Engineering in Medicine and Biology Conference, 2021.
A. John, J. Sadasivan, and C. S. Seelamantula, ``Adaptive Savitzky-Golay filtering in non-Gaussian noise'', in IEEE Transactions on Signals and Systems, 2021.
A. John, S. J. Redmond, B. Cardiff, and D. John ``A multimodal data fusion technique for heartbeat detection in wearable IoT sensors'', in IEEE Internet of Things Journal, 2021.
A. John, B. Cardiff, and D. John, ``A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in Wearable Sensors'', in IEEE International Symposium on Circuits and Systems, 2021.
A. John, S. Ullah, A. Kumar, B. Cardiff, and D. John. ``An Approximate Binary Classifier for Data Integrity Assessment in IoT Sensors'', in IEEE International Conference on Emerging Circuits and Systems, 2020.
A. John, B. Cardiff, and D. John, ``A Generalized Signal Quality Estimation Method for IoT Sensors'', in IEEE International Symposium on Circuits and Systems, 2020.
A. John, R.C. Panicker, B. Cardiff, Y. Lian, and D. John, ``Binary Classifiers for Data Integrity Detection in Wearable IoT Edge Devices'', in IEEE Open Journal of Circuits and Systems, 2020.
N.S. Krishna, A. George, A. John, A. P. Sudheer, ``Controller Design for a Skid-Steered Robot and Mapping for Surveillance Applications'', in Proceedings of the Advances in Robotics, 2017.
A. John, A. E. Vijayan, A. P. Sudheer, ``Electromyography based control of robotic arm using entropy and zero crossing rate'', in Proceedings of 2015 Conference on Advances In Robotics, 2015.
A. E. Vijayan, A. John, A. P. Sudheer, ``Surface electromyography based finger flexion recognition'', in IEEE International Conference on Computational Intelligence & Communication Technology, 2015.
A. E. Vijayan, A. John, D. Sen, ``Efficient implementation of 8-bit vedic multipliers for image processing application'', in IEEE International Conference on Contemporary Computing and Informatics, 2014.