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Arlene John is an Assistant Professor at the Biomedical Signals and Systems (BSS) group. Arlene completed her Bachelor’s degree in Electrical and Electronics Engineering at the National Institute of Technology, Calicut, India, in 2017. In the summer of 2016, she worked as a Research Intern at the Indian Institute of Science, Bangalore, India. After graduation, she joined Bosch India Ltd. as a Project Manager in Technical Sales and later worked in engineering and strategy development for hybrid electric vehicles.
She obtained her Ph.D. from the School of Electrical and Electronic Engineering, University College Dublin, Ireland, in 2022, where her research focused on developing data fusion frameworks for wearable health monitoring devices. From March to June 2019, she was a Senior Visiting Researcher at Beijing University of Technology, China, and in 2021, she worked as a Machine Learning Intern at Qualcomm, Cork, Ireland. After her Ph.D., she joined ASML Netherlands B.V. as a Machine Learning Mathematics Engineer, where she worked until April 2023.
Her research interests include explainable AI, multisensor data fusion, and biomedical signal processing and modeling.
Expertise Computer Science Internet of Things Models Convolutional Neural Network Detection Noise-to-Signal Ratio Medicine and Dentistry Postoperative Complication Breathing Rate Engineering Organisations My research is at the intersection of explainable artificial intelligence and life sciences, through which I aim to shape the future of sustainable and equitable digital healthcare.
Most of the code and models I develop are published on my github: https://github.com/arlenejohn
I also curate a podcast on Spotify to better communicate my research, where every episode is narrated by an advanced language model: https://open.spotify.com/show/1JWZF0jL5KCnQeIoLWcSO9
The Springer monograph based on my PhD thesis: https://www.amazon.com/Learning-Signal-Processing-Methods-Multisensor-Fusion/dp/3031967232
Publications Multimodal fusion for robust respiratory rate estimation in wearable sensing (2025) Information Fusion, 123 . Article 103253. John, A. , Wang, H., Cardiff, B., K Parhi, K. & John, D.https://doi.org/10.1016/j.inffus.2025.103253 Sensor Fusion Using 1D-CNNs in Atrial Fibrillation Detection and Decision Support (2025) In 2025 IEEE Medical Measurements & Applications (MeMeA) . IEEE. John, A. https://doi.org/10.1109/MeMeA65319.2025.11068069 Unsupervised Detection of Postoperative Complications in Home-Monitored Patients: Preliminary Results (2025) In 2025 IEEE International Symposium on Medical Measurements and Applications (MeMeA) . IEEE. Fatime, O. D., Waanders, I., Lips, D. J., Nane, G. F., van Keulen, M., Witteveen, A. & John, A. https://doi.org/10.1109/MeMeA65319.2025.11068044 Explainable AI (XAI) for Arrhythmia Detection in ECG (2025) [Contribution to conference › Abstract] 14th Supporting Health by Technology Conference 2025 . Beck, J. & John, A. Predicting Psychological States using Machine Learning and Digital Biomarkers from wearable data (2025) [Contribution to conference › Abstract] 14th Supporting Health by Technology Conference 2025 . Wu, L., John, A. & Piano Simoes, J. Reporting, representation and subgroup analysis in studies assessing consumer wearable validity: a scoping review (2025) [Contribution to conference › Abstract] 14th Supporting Health by Technology Conference 2025 . Schipper, R., Fatime, O. D., Roosink, M., Winkens, L., John, A. , Witteveen, A., Hazebroek, E. & Berendsen, A. Unsupervised Change Point Detection for Early Complication Identification in Post-Surgical Oncology Patients (2025) [Contribution to conference › Abstract] 14th Supporting Health by Technology Conference 2025 . Fatime, O. D., Waanders, I., Lips, D. J., Witteveen, A., Nane, G. F., van Keulen, M. & John, A. MLFusion: Multilevel Data Fusion using CNNS for atrial fibrillation detection (2025) Computers in biology and medicine, 189 . Article 109901. John, A. , K Parhi, K., Cardiff, B. & John, D.https://doi.org/10.1016/j.compbiomed.2025.109901 Predicting Positive Psychological States using Machine Learning and Digital Biomarkers from Everyday Wearable Data (2025) [Working paper › Preprint]. MedRxiv. Wu, L., John, A. & Piano Simoes, J. https://doi.org/10.1101/2025.04.26.25326492 Missing Data of the Philips HealthDot: insights into monitoring at home and level of physical activity (2025) [Contribution to conference › Abstract] 10th Dutch Biomedical Engineering Conference, BME 2025 . Waanders, I., Vassen, H., Lips, D. J., Witteveen, A. & John, A. Change Point Detection for Continuous Physiological Monitoring using Wearables (2025) [Contribution to conference › Paper] 10th Dutch Biomedical Engineering Conference, BME 2025 (Accepted/In press). Tjepkema, W., John, A. , Vernooij, L., Epa Ranasinghe, C. M., van Beijnum, B.-J. F. & Breteler, M. Dynamic Predictive Models for Side Effects Following Cancer or Cancer Treatment: A Systematic Review (2025) [Contribution to conference › Abstract] 10th Dutch Biomedical Engineering Conference, BME 2025 . Fatime, O. D., Schipper, R., Berendsen, A., Nane, G. F., van Keulen, M., Witteveen, A. & John, A. Investigating Approximate Computing to design an energy-efficient deep learning architecture for anomaly detection from ECG signals (2025) [Contribution to conference › Abstract] 10th Dutch Biomedical Engineering Conference, BME 2025 . Capitani, D., Gillani, G. & John, A. Multilevel Data Fusion for atrial fibrillation detection in wearables (2025) [Contribution to conference › Poster] 10th Dutch Biomedical Engineering Conference, BME 2025 . John, A. , Cardiff, B. & John, D. QUICC: Quantification of Urgent Indicators for early detection of Complications after Colorectal resection: Proof of principle study on early detection of postoperative complications using continuous heart rate and respiratory rate measurements, in both in-hospital and at home settings (2025) [Contribution to conference › Abstract] 10th Dutch Biomedical Engineering Conference, BME 2025 . Waanders, I., Vassen, H., Lips, D. J., John, A. & Witteveen, A. Research profiles I coordinate the Medical Sensors and Measurements course in the Biomedical Engineering Bachelor’s program (Quarter 3).
Affiliated study programs Courses academic year 2025/2026 Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
Courses academic year 2024/2025 
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