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. 



Zhang C, Liu S, Han F, et al., 2021, Hybrid manifold-deep convolutional neural network for sleep staging., Methods

Chen X, Jiang S, Li Z, et al., 2021, A Pervasive Respiratory Monitoring Sensor for COVID-19 Pandemic, Ieee Open Journal of Engineering in Medicine and Biology, Vol:2, Pages:11-16

Zhang D, Lo FP-W, Zheng J-Q, et al., 2020, Data-Driven Microscopic Pose and Depth Estimation for Optical Microrobot Manipulation, Acs Photonics, Vol:7, ISSN:2330-4022, Pages:3003-3014

Di Camillo B, Nicosia G, Buffa F, et al., 2020, Guest Editorial Data Science in Smart Healthcare: Challenges and Opportunities, Ieee Journal of Biomedical and Health Informatics, Vol:24, ISSN:2168-2194, Pages:3041-3043

Zhang G, Mei Z, Zhang Y, et al., 2020, A Noninvasive Blood Glucose Monitoring System Based on Smartphone PPG Signal Processing and Machine Learning, Ieee Transactions on Industrial Informatics, Vol:16, ISSN:1551-3203, Pages:7209-7218

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