Engaging Community College Students in Emerging Human-Machine Interfaces Research through Design and Implementation of a Mobile Application for Gesture Recognition
Engaging Community College Students in Emerging Human-Machine Interfaces Research through Design and Implementation of a Mobile Application for Gesture Recognition
复制标题
通过设计和实现手势识别移动应用程序让社区大学生参与新兴人机界面研究
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Xiaorong Zhang
中科院分区：
文献类型：
--
作者：
Kattia Chang;Karina Abad;Ricardo Jesus Colin;Charles P. Tolentino;C. Malloy;Alex David;A. Enriquez;W. Pong;Zhaoshuo Jiang;Cheng Chen;K. Teh;H. Mahmoodi;Hao Jiang;Xiaorong Zhang
本科研究经历已被认为是吸引科学、技术、工程和数学 (STEM) 学生并提高其保留率的有效方法。社区学院招收了全国近一半的本科生，在 STEM 教育中发挥着重要作用。因此，制定策略为社区学院的学生提供研究机会和经验非常重要。在教育部少数族裔科学与工程改进计划（MSEIP）的支持下，社区学院与公立综合性大学之间开发了一项合作实习计划，以吸引社区学院的学生参与前沿工程研究。 2017 年夏天，社区学院的五名大二学生在这所四年制大学的研究实验室参加了为期十周的计算机工程研究实习项目。该实习项目旨在开发一种低成本、便携式、灵活的人机界面，用于实时手势识别。实习生开发的人机界面通过集成移动和云计算技术，为计算复杂的肌电图（EMG）模式识别算法提供实时处理速度和足够的存储容量。对身体健全的受试者进行了手势识别实时实验，以评估所开发系统的准确性、响应时间和可用性。该项目为学生实习生提供了一个很好的机会，让他们获得人机界面方面的宝贵研究经验，并提高他们的团队合作、时间管理以及科学写作和演示的技能。它还帮助学生增强了追求 STEM 职业的信心和兴趣。
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 leadingedge 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. The human-machine interface developed by the interns provides real-time processing speed and sufficient storage capacity for computationally complex electromyogram (EMG) pattern recognition algorithms by integrating mobile and cloud computing techniques. 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.