March 25, 2018
March 25, 2018
March 27, 2018
Undergraduate research experience has been identified as an effective approach for engaging science, technology, engineering, and mathematics (STEM) students and increasing their retention rates. Community colleges enroll almost half of the nation’s undergraduate students and play a significant role in STEM education. Thus it is important to develop strategies to provide community college students with research opportunities and experiences. With support from the Department of Education Minority Science and Engineering Improvement Program (MSEIP), a cooperative internship program between a community college and a public comprehensive university has been developed to engage community college students in leading-edge engineering research. In summer 2017, five sophomore students from the community college participated in a ten-week computer engineering research internship project in a research lab at the four-year university. This internship project aimed to develop a low-cost, portable, and flexible human-machine interface for real-time gesture recognition. Electromyography (EMG) is a technique for measuring the electrical activity generated by skeletal muscles. EMG pattern recognition (PR) is an intelligent method for deciphering neuromuscular information from EMG signals to identify users’ intended movements. The human-machine interface developed by the interns provides real-time processing speed and sufficient storage capacity for computationally complex EMG PR algorithms by integrating mobile and cloud computing techniques. Specifically, a mobile Android application was developed which provides easy interface with a commercial EMG sensing armband Myo (Thalmic Labs) and a modular software engine seamlessly integrating a variety of signal processing modules, from data acquisition through pattern recognition, to real-time evaluation and control. In addition, an interface between the Android application and the Amazon Web Services Cloud Server was built which allows real-time cloud computing and storage. Real-time experiments were conducted on able-bodied subjects for hand gesture recognition to evaluate the accuracy, response time, and usability of the developed system. The project provided a great opportunity for the student interns to gain valuable research experience in human-machine interfaces and to improve their skills in teamwork, time management, as well as scientific writing and presentation. It also helped the students strengthening their confidence and interest in pursuing a STEM profession.
Chang, K., & Abad, K., & Colin, R. J., & Tolentino, C., & Malloy, C., & David, A., & Enriquez, A. G., & Pong, W., & Jiang, Z., & Chen, C., & Teh, K. S., & Mahmoodi, H., & Jiang, H., & Zhang, X. (2018, March), Engaging Community College Students in Emerging Human-Machine Interfaces Research through Design and Implementation of a Mobile Application for Gesture Recognition Paper presented at 2018 ASEE Zone IV Conference, Boulder, Colorado. https://peer.asee.org/29610
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2018 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015