Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Senior Lecturer



+44 (0)20 7594 0806benny.lo Website




B414BBessemer BuildingSouth Kensington Campus





Dr. Benny Lo is a Senior Lecturer of the Hamlyn Centre, and the Department of Surgery and Cancer, Imperial College London. He also serves as an Associate Editor of the IEEE Journal on Biomedical and Health Informatics, and the Chair of IEEE EMBS Wearable Biomedical Sensors and Systems Technical Committee . He is one of the pioneers in Body Sensor Networks (BSN) research, and helped building the foundation of the BSN research through the development of the platform technologies, introduction of novel sensors, approaches and theories for different pervasive applications, and organising conferences and tutorials. 

His current research focuses on pervasive sensing, computer vision, machine learning, Body Sensor Networks (BSN), Internet of Things (IoT) and Wearable Robots and their applications in healthcare, sports and wellbeing.

In collaboration with Coursera, Dr Lo has launched a MOOC specialisation on Advanced App Development on Android covering topics on computer graphics and virtual reality app developments. 



Gu X, Guo Y, Yang G-Z, et al., 2021, Cross-domain self-supervised complete geometric representation learning for real-scanned point cloud based pathological gait analysis, Ieee Journal of Biomedical and Health Informatics, ISSN:2168-2194

Yang X, Zhang Y, Lo B, et al., 2021, DBAN: Adversarial Network With Multi-Scale Features for Cardiac MRI Segmentation, Ieee Journal of Biomedical and Health Informatics, Vol:25, ISSN:2168-2194, Pages:2018-2028

Jiang S, Kang P, Song X, et al., 2021, Emerging wearable interfaces and algorithms for hand gesture recognition: a survey., Ieee Reviews in Biomedical Engineering, Vol:PP, ISSN:1941-1189


Hu M, Kassanos P, Keshavarz M, et al., 2021, Electrical and Mechanical Characterization of Carbon-Based Elastomeric Composites for Printed Sensors and Electronics

Han J, Gu X, Lo B, Semi-supervised contrastive learning for generalizable motor imagery eeg classification, 17th IEEE International Conference on Wearable and Implantable Body Sensor Networks, IEEE

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