Cai Mingjing | 蔡明京

Associate Professor

Xidian University | 西安电子科技大学

Google Scholar ORCID Publons

I am currently an Associate Professor with Advanced Manufacturing Technology Innovation Center, Institute of Technology, Xidian University. Under the supervision of Prof. Longhan Xie, I obtained my Bachelor and Master Degree from South China University of Technology in 2013 and 2016, respectively. In 2020, under the supervision of Prof. Wei-Hsin Liao, I received the Ph.D. degree in Mechanical and Automation Engineering from The Chinese University of Hong Kong.

In the era of Internet of Things (IoT), I am committing myself to addressing bottlenecked issues of self-powered IoT systems. My research interests include Energy Harvesting, Self-powered IoT Systems, Wearable Devices, and Robotics.

Office: B7-706

Embedded energy harvester for batterless wearables

Wearable devices, such as smart watches and wristbands, require sustainable power supply. However, chemical batteries cannot satisfy this requirement due to finite energy capacity. Harvesting energy from human limb swinging renders a promising solution to this issue. Motivated by this, we have developed embedded energy harvesters for the wearables and IoT sensors. Our energy harvesters can efficiently scavenge kinetic energy from human motion to provide sustainable power for the wearables. With our patented technologies, our energy harvesters improve output power and power density by an order of magnitude compared with the counterparts. It has attracted great attention from the industry, and we are working with our industry partners to commercialize this project.

Smart energy harvester for power generation and walking assistance

Capturing kinetic energy of human motion provides sustainable power for the wearables. However, would it increase human effort when harvesting kinetic energy? In this project, we validated that the energy harvester can generate electricity while reducing metabolic expenditure of the user. For this purpose, a smart energy harvester has been developed to identify and capture negative work of human ankle. The negative work identification system does not require power supply since it is designed based on human gait characteristics. We demonstrated that our energy harvester generates maximum power of 0.67 W, but reduces metabolic expenditure by 0.84 W. This technology has great potential in exoskeleton, rehabilitation robots, and individual soldier equipment, etc.