Dr Shaoxiong Sun

School of Computer Science

Lecturer in Pervasive Data Science

Communications and Marketing Coordinator

Shaoxiong.Sun@sheffield.ac.uk

Regent Court (CS)

Full contact details

Dr Shaoxiong Sun
School of Computer Science
Regent Court (CS)
211 Portobello
Sheffield
S1 4DP

Profile

Dr. Sun's research interests primarily revolve around physiological and behavioural monitoring, leveraging advanced signal processing and machine learning methodologies. In the realm of physiological monitoring, he focuses on estimating vital parameters such as blood pressure, heart rate, and respiration rate through the analysis of biomedical signals, such as photoplethysmography (PPG). In behavioural monitoring, his work centres on assessing mobility and social interaction patterns using wearable devices and smartphones.

Dr Sun has developed innovative technologies aimed at assisting individuals with a range of medical conditions, including depression, multiple sclerosis, epilepsy, ADHD, and COVID-19.

Research interests

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

Publications

Journal articles

Zhang Y, Folarin AA, Ranjan Y, Cummins N, Rashid Z, Conde P, Stewart C, Sun S, Vairavan S, Matcham F , Oetzmann C et al (2025) Assessing seasonal and weather effects on depression and physical activity using mobile health data. npj Mental Health Research, 4. View this article in WRRO
Stewart C, Ranjan Y, Conde P, Sun S, Zhang Y, Rashid Z, Sankesara H, Cummins N, Laiou P, Bai X , Dobson RJB 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. The Lancet Digital Health, 6(9), e640-e650.
Zhang Y, Folarin AA, Sun S, Cummins N, Ranjan Y, Rashid Z, Stewart C, Conde P, Sankesara H, Laiou P , Matcham F et al (2024) Longitudinal assessment of seasonal impacts and depression associations on circadian rhythm using multimodal wearable sensing: retrospective analysis. Journal of Medical Internet Research, 26(1). View this article in WRRO
Zhang D, Peng Z, Sun S, van Pul C, Shan C, Dudink J, Andriessen P, Aarts RM & Long X (2024) Characterising the motion and cardiorespiratory interaction of preterm infants can improve the classification of their sleep state. Acta Paediatrica, 113(6), 1236-1245. View this article in WRRO
Sun S, Folarin AA, Zhang Y, Cummins N, Garcia-Dias R, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P , Sankesara H et al (2023) Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis.. J Med Internet Res, 25, e45233.
Sun S, Denyer H, Sankesara H, Deng Q, Ranjan Y, Conde P, Rashid Z, Bendayan R, Asherson P, Bilbow A , Groom M et al (2023) Remote administration of ADHD-sensitive cognitive tasks: a pilot study. Journal of Attention Disorders, 27(9), 1040-1050. View this article in WRRO
Zhang Y, Pratap A, Folarin AA, Sun S, Cummins N, Matcham F, Vairavan S, Dineley J, Ranjan Y, Rashid Z , Conde P et al (2023) Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study.. NPJ Digit Med, 6(1), 25.
de Angel V, Adeleye F, Zhang Y, Cummins N, Munir S, Lewis S, Laporta Puyal E, Matcham F, Sun S, Folarin AA , Ranjan Y et al (2023) The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement.. JMIR Ment Health, 10, e42866.
Sun S, Bresch E, Muehlsteff J, Schmitt L, Long X, Bezemer R, Paulussen I, Noordergraaf GJ & Aarts RM (2023) Systolic blood pressure estimation using ECG and PPG in patients undergoing surgery. Biomedical Signal Processing and Control, 79, 104040-104040.
Sun S, Folarin AA, Zhang Y, Cummins N, Liu S, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P , Sankesara H et al (2022) The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions. Computer Methods and Programs in Biomedicine, 227, 107204-107204.
Laiou P, Biondi A, Bruno E, Viana PF, Winston JS, Rashid Z, Ranjan Y, Conde P, Stewart C, Sun S , Zhang Y et al (2022) Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence.. Biomedicines, 10(10).
Zhang Y, Folarin AA, Sun S, Cummins N, Vairavan S, Qian L, Ranjan Y, Rashid Z, Conde P, Stewart C , Laiou P et al (2022) Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis.. JMIR Mhealth Uhealth, 10(10), e40667.
Zhang Y, Folarin AA, Sun S, Cummins N, Vairavan S, Bendayan R, Ranjan Y, Rashid Z, Conde P, Stewart C , Laiou P et al (2022) Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study.. JMIR Ment Health, 9(3), e34898.
Laiou P, Kaliukhovich DA, Folarin AA, Ranjan Y, Rashid Z, Conde P, Stewart C, Sun S, Zhang Y, Matcham F , Ivan A et al (2022) The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones.. JMIR Mhealth Uhealth, 10(1), e28095.
Liu S, Han J, Puyal EL, Kontaxis S, Sun S, Locatelli P, Dineley J, Pokorny FB, Costa GD, Leocani L , Guerrero AI et al (2022) Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder. Pattern Recognition, 123, 108403-108403.
Leightley D, Lavelle G, White KM, Sun S, Matcham F, Ivan A, Oetzmann C, Penninx BWJH, Lamers F, Siddi S , Haro JM et al (2021) Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology. BMC Psychiatry, 21(1).
Zhang Y, Folarin AA, Sun S, Cummins N, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F , Oetzmann C et al (2021) Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study.. JMIR Mhealth Uhealth, 9(7), e29840.
Snoeijer BT, Burger M, Sun S, Dobson RJB & Folarin AA (2021) Measuring the effect of Non-Pharmaceutical Interventions (NPIs) on mobility during the COVID-19 pandemic using global mobility data.. NPJ Digit Med, 4(1), 81.
Zhang Y, Folarin AA, Sun S, Cummins N, Bendayan R, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P , Matcham F et al (2021) Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study.. JMIR Mhealth Uhealth, 9(4), e24604.
Sun S, Folarin AA, Ranjan Y, Rashid Z, Conde P, Stewart C, Cummins N, Matcham F, Dalla Costa G, Simblett S , Leocani L et al (2020) Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19.. J Med Internet Res, 22(9), e19992.
Sun S, Peeters WH, Bezemer R, Long X, Paulussen I, Aarts RM & Noordergraaf GJ (2019) Finger and forehead photoplethysmography-derived pulse-pressure variation and the benefits of baseline correction. Journal of Clinical Monitoring and Computing, 33(1), 65-75.
Sun S, Peeters WH, Bezemer R, Long X, Paulussen I, Aarts RM & Noordergraaf GJ (2017) On algorithms for calculating arterial pulse pressure variation during major surgery. Physiological Measurement, 38(12), 2101-2121.
Sun S, Bezemer R, Long X, Muehlsteff J & Aarts RM (2016) Systolic blood pressure estimation using PPG and ECG during physical exercise. Physiological Measurement, 37(12), 2154-2169.

Conference proceedings

Stewart CL, Rashid Z, Ranjan Y, Sun S, Dobson RJB & Folarin AA (2018) RADAR-base. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp 1735-1743)

Research group: Pervasive Computing