Recent years, the advancements in low-power and miniaturized electronics lead to the prosperity of wearable technology. People tend to wear various wearable devices on their body for better health, entertainment, and convenience. Particularly, equipped with diverse specialized sensors, wearable devices act as a sensing and computing platform to understand human behaviours and health conditions. Human monitoring is favoured of continuous and long-term operation, while there exist two crucial challenges that impede its realization. Firstly, wearable devices are usually powered by batteries with limited energy capacity, which poses a stringent requirement on the energy efficiency of the devices. Secondly, a monitoring service generally focuses on the detection of a particular action or biomarker, whereas the sensor measurements could be easily polluted by other accompanying and natural activities of the wearer, in the long-term setting.
In this talk, the speaker will present his research on addressing these two challenges. Specifically, he proposed simultaneous energy harvesting and sensing to improve the energy efficiency and novel sensing mechanism to deal with the interference from human motion artifacts. These efforts lift the practicality of 7x24 wearable-based human sensing in real-world scenarios.
About the Speaker
Dr Dong Ma is currently a senior postdoctoral researcher at the Department of Computer Science and Technology, University of Cambridge, advised by Professor Cecilia Mascolo. Prior to that, he earned his PhD at the University of New South Wales, Australia in 2019. His research interests rotate around cyber-physical systems, including ubiquitous computing, pervasive sensing, vibration communication, energy harvesting, and mobile healthcare, covering the end-to-end system design, implementation, and evaluation. He has published several papers on prestigious venues in the field such as MobiCom, MobiSys, INFOCOM, and TMC.
He is a tenure-track faculty candidate for the Human-Machine Collaborative Systems, Pervasive Sensing & Systems cluster..
