Shaoxiong Sun

Shaoxiong Sun

Lecturer/Assistant Professor at University of Sheffield, UK

About Me

I am a Lecturer in Pervasive Data Science in the School of Computer Science at the University of Sheffield. Previously, I worked as a Senior Research Associate in Data Science for Mobile Health at King’s College London. I earned my PhD through a joint programme between Eindhoven University of Technology (TU/e) and Philips Research in the Netherlands, where my research focused on physiological monitoring. I obtained my BEng and MEng in Electrical Engineering from Harbin Institute of Technology and Dalian University of Technology, China, respectively.

I have authored over 30 peer-reviewed publications and hold two granted patents in the United States and Europe. I have served on the Technical Program Committees of IEEE CHASE and IEEE BSN, as well as UbiComp/ISWC, and I am a founding member of the IEEE Internet of Wearable Things Working Group. I also serve as an Associate Editor for the IEEE Journal of Biomedical and Health Informatics and on the Editorial Board of BMC Global and Public Health. I am a Fellow of the Higher Education Academy and a member of the EPSRC Peer Review College.

My research interests primarily revolve around physiological and behavioural monitoring, leveraging advanced signal processing and machine learning methodologies. In physiological monitoring, I focus on estimating vital parameters such as blood pressure, heart rate, and respiration rate through the analysis of biomedical signals, particularly photoplethysmography (PPG). In behavioural monitoring, my work centres on assessing mobility and social interaction patterns using wearable devices and smartphones. I have developed innovative technologies aimed at assisting individuals with a range of medical conditions, including depression, multiple sclerosis, epilepsy, ADHD, and COVID-19.

My research interests include:

  • Health data science
  • Bedside and remote patient monitoring
  • Wearable computing
  • Cardiovascular monitoring

👥 Team

PhD Students

  • Xiaofei Zhang (2025 – present): Mixed-effects deep learning for patient monitoring. (co-supervised with Prof. Vita Lanfranchi)
  • Lucy M Cheesman (2025 – present): Developing novel digital biomarkers for disease progression in multiple sclerosis. (co-supervised with Prof. Vita Lanfranchi)
  • Yuchen Wang (2026 – present): Deep learning for machinery and human health monitoring. (co-supervised with Dr. Xingyi Song)
  • Henry Probo Santoso (2026 - present): Edge AI for remote patient monitoring. (co-supervised with Dr. Nur Ahmadi)
  • Konrad Kawka (2024 – present): Developing novel digital biomarkers for mobility monitoring in multiple sclerosis. (co-supervised with Prof. Vita Lanfranchi)
  • Kacper F Sikorski (2025 – present): Non-invasive continuous glucose monitoring via photoplethysmography (PPG). (co-supervised with Dr Mohammad Eissa and Prof. Mohammed Benaissa)

Visiting Scholars

  • Ling Zhang (2026-present): Machine learning for remote mental health monitoring.

[Join us] I am looking for self-motivated PhD students, visiting students, and interns. If you are interested, please send your CV to (shaoxiong dot sun at sheffield dot ac dot uk).

📝 Publications

Citations = 1372, h-index = 20 (Google Scholar)

Selected Journal Articles

  • Zhang J., Zhang J., Shull P., Park C., Sun, S., Najafi B., Wang C. (2026). Daily activity patterns from wearable accelerometry predict physical frailty and concern about falling. npj Digital Medicine.

  • Zhang, Y., Folarin, A.A., Ranjan Y. et al. (2025). Assessing seasonal and weather effects on depression and physical activity using mobile health data. npj Mental Health Research, 4(11).

  • Stewart C., Ranjan Y., Conde P., Sun, S., Zhang Y., et al. (2024). Physiological presentation and risk factors of long COVID in the UK using smartphones and wearable devices: a longitudinal, citizen science, case–control study. Lancet Digital Health, 6(9):e640–e650.

  • Zhang D., Peng Z., Sun, S., van Pul C., Shan C., et al. (2024). Characterising the motion and cardiorespiratory interaction of preterm infants can improve the classification of their sleep state. Acta Paediatrica, 113(6):1236–1245.

  • Sun, S., Folarin A.A., Ranjan Y., et al. (2023). Challenges in the analysis of mobile health data from smartphones and wearable devices to predict depression symptom severity. JMIR, 25:e45233.

  • Sun, S., Denyer H., Sankesara H., et al. (2023). Remote administration of ADHD-sensitive cognitive tasks: a pilot study. Journal of Attention Disorders, 27(9):1040–1050.

  • Sun, S., Bresch E., Muehlsteff J., Schmitt L., Long X., Bezemer R., Aarts R.M. (2023). Systolic blood pressure estimation using ECG and PPG in patients undergoing surgery. Biomedical Signal Processing and Control, 79(1):104040.

  • Sun, S., Folarin A.A., Ranjan Y., et al. (2022). The utility of wearable devices in assessing ambulatory impairments of patients with multiple sclerosis in free-living conditions. Computer Methods and Programs in Biomedicine, 227:107204.

  • Sun, S., Folarin A.A., Ranjan Y., et al. (2020). Using smartphones and wearable devices to monitor behavioural changes during COVID-19. JMIR, 22(9):e19992.

  • Sun, S., Peeters W.H., Bezemer R., Long X., Paulussen I., Aarts R.M., Noordergraaf G.J. (2018). Finger and forehead PPG-derived pulse-pressure variation and the benefits of baseline correction. Journal of Clinical Monitoring and Computing, 33(1):65–75.

  • Sun, S., Peeters W.H., Bezemer R., et al. (2017). On algorithms for calculating arterial pulse pressure variation during major surgery. Physiological Measurement, 38:2101–2121.

  • Sun, S., Bezemer R., Long X., Muehlsteff J., Aarts R.M. (2016). Systolic blood pressure estimation using PPG and ECG during physical exercise. Physiological Measurement, 37:2154–2169.

Patents

  • Sun, S., Peeters W.H., Bezemer R. Device, system and method for determining pulse pressure variation of a subject. PCT — Granted in the US, UK, and Germany.

  • Sun, S., Peeters W.H., Bezemer R., Long X., Aarts R.M. A sensor system and sensing method for use in assessment of circulatory volume. PCT — Granted in the US, UK, and Germany.

🎓 Education

  • 2013.09 - 2018.05, PhD, Electrical Engineering, Eindhoven University of Technology, Netherlands
  • 2010.09 - 2013.06, MSc, Signal and Information Processing, Dalian University of Technology, China (exempt from entrance exam)
  • 2006.08 - 2010.07, BSc, Electronic Information Engineering, Harbin Institute of Technology, China,

💬 Invited Talks

  • 2026.08, Medicine-Engineering Interdisciplinary Seminar, Hefei University of Technology, China (Remote), invited by Prof. Xuenan Liu
  • 2026.07, Clinical Trial Units, University of Alberta, Canada, invited by Prof. Giovanni Ferrara
  • 2026.03, AI in Medicine Seminar, UT Southwestern Medical Center, USA, invited by Dr. Ti Bai
  • 2025.07, School of Biomedical Engineering, Sun Yat-sen University, China, invited by Prof. Changhong Wang

🏭 Industrial Experience

📖 Academic Services

  • Associate Editor, IEEE Journal of Biomedical and Health Informatics (JBHI)
  • Editorial Board, BMC Global and Public Health, Digital Medicine Letter
  • Guest Editor, 4 journals including Frontiers in Digital Health
  • Founding Member and Area Chair, The IEEE IoWT Working Group
  • Grant Reviewer, UK Medical Research Council, UK MS Society
  • Journal Reviewer, 15+ academic journals including IEEE JBHI, IEEE TBME, npj Digital Medicine, Lancet Digital Health, and JMIR
  • Technical Programme Committee Member, IEEE/ACM CHASE, IEEE BSN, Ubicomp
  • Conference Reviewer, Interspeech, Ubicomp, EMBC

🎖 Honors and Awards

  • 2023 Department Travel Grant, King’s College London
  • 2018 UbiComp/ISWC Junior Faculty/Postdoc Travel Grant, UbiComp/ISWC
  • 2010-2013 Graduate Excellence Scholarship, Dalian University of Technology
  • 2011 Second Prize, Mindray Cup Innovation Design Contest, Mindray Medical Corp.
  • 2008 Excellent Student Leader, Harbin Institute of Technology
  • 2007 Academic Excellence Scholarship, Harbin Institute of Technology