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. 



Jobarteh ML, McCrory MA, Lo B, et al., 2020, Development and validation of objective, passive dietary assessment Method for estimating food and nutrient intake in households in Low and Middle-Income Countries (LMICs): a study protocol, Current Developments in Nutrition, Vol:4, ISSN:2475-2991, Pages:1-11

Zhang Y, Guo Y, Yang P, et al., 2020, Epilepsy Seizure Prediction on EEG Using Common Spatial Pattern and Convolutional Neural Network., Ieee J Biomed Health Inform, Vol:24, Pages:465-474

Zhang Y, Zhang Y, Lo B, et al., 2020, Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection, Expert Systems, Vol:37, ISSN:0266-4720, Pages:1-13

Lo FP-W, Sun Y, Qiu J, et al., 2020, Point2Volume: A vision-based dietary assessment approach using view synthesis, Ieee Transactions on Industrial Informatics, Vol:16, ISSN:1551-3203, Pages:577-586

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