San Francisco State University joins Sensing Solution University collaboration Program
Sony Developer World and Sony’s Semiconductor Solutions Group (SSS) announce their collaboration with San Francisco State University (SFSU), School of Engineering, as part of the Sensing Solution University collaboration Program (SSUP). The SSUP program offers real collaboration opportunities for a network of partners, from university labs to teaching facilities. It encourages and supports research projects in the area of sensing solutions and promotes innovation and education.
The aim of the SFSU team is to utilize Sony’s Spresense microcontroller board to develop efficient deep learning algorithms. By doing so, the team wishes to improve the EMG-based neural-controlled bionic arm technology by introducing deep learning compression techniques. In this project, high-density surface electromyography (HD-EMG) signals will be collected using electrode grids from forearm muscles for hand gesture recognition. The SFSU team will develop efficient deep learning models for HD-EMG signal processing and compress the deep learning model to run optimally on the Spresense edge board to control a bionic arm.
“This project proposes a joint innovation with the human-machine interface and efficient deep learning techniques. The outcomes of this project will show the great potential of the Spresense edge board in bionic prostheses design, biomedical signal processing, and machine learning application development. The developed Neural2 system is particularly impactful for AI-powered human-machine interface applications, such as human-assisting robots, rehabilitation devices, virtual input devices, and augmented reality” said Professor Zhuwei Qin, Assistant Professor at the School of Engineering of SFSU.
The SFSU research group includes:
- Dr. Zhuwei Qin, Assistant Professor at the School of Engineering and Director of the Mobile and Intelligent Computing Laboratory (MIC Lab) at SFSU. His research interests are in the broad area of efficient mobile computing, deep learning acceleration, distributed edge computing, and interpretable deep learning.
- Dr. Xiaorong Zhang, Associate Professor at the School of Engineering and Director of the Intelligent Computing and Embedded Systems Laboratory (ICE Lab) at SFSU. She has extensive research experience in human-machine interfaces (HMIs), embedded systems, and neurorehabilitation technologies. She is a recipient of the National Science Foundation CAREER Award, which supports her work in developing the next generation of HMIs for EMG-based neurorehabilitation.
- Philip Liang, currently pursuing a graduate degree in Electrical and Computer Engineering (ECE) from the School of Engineering at SFSU. He previously obtained a BSc. degree in Electrical and Electronics Engineering from the University of California, Los Angeles (UCLA). His research focuses on efficient deep learning for human-machine interfaces.
- Zhenyu Lin, undergraduate student in the Department of Computer Science at SFSU. His research focuses on computational acceleration for deep learning on resource-constrained mobile/edge devices and the robustness issue of neural networks in medical rehabilitation applications.