Assembly Training Using Commodity Physiological Sensors

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2016-01-01
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Hoover, Melynda
MacAllister, Anastasia
Holub, Joseph
Winer, Eliot
Davies, Paul
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Gilbert, Stephen
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Mechanical Engineering
The Department of Mechanical Engineering at Iowa State University is where innovation thrives and the impossible is made possible. This is where your passion for problem-solving and hands-on learning can make a real difference in our world. Whether you’re helping improve the environment, creating safer automobiles, or advancing medical technologies, and athletic performance, the Department of Mechanical Engineering gives you the tools and talent to blaze your own trail to an amazing career.
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Virtual Reality Applications Center
At VRAC, our mission is clear: “To elevate the synergy between humans and complex interdisciplinary systems to unprecedented levels of performance”. Through our exceptional Human Computer Interaction (HCI) graduate program, we nurture the next generation of visionaries and leaders in the field, providing them with a comprehensive understanding of the intricate relationship between humans and technology. This empowers our students to create intuitive and transformative user experiences that bridge the gap between innovation and practical application.
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Psychology
The Department of Psychology may prepare students with a liberal study, or for work in academia or professional education for law or health-services. Graduates will be able to apply the scientific method to human behavior and mental processes, as well as have ample knowledge of psychological theory and method.
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Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
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Abstract

Wearable technology is a thriving industry with projections for continued growth in the next decade and numerous unexplored applications. The U.S. Military has been on the forefront of this technology by supporting the research and development of these devices for today’s warfighters. Smartwatches with sensors that detect physiological responses, like heart rate, have particularly interesting applications to warfighters. These devices have the potential to detect user stress during many different tasks from field operations to maintenance. Specifically, this paper will analyze the use of commodity sensors for evaluating and improving Augmented Reality (AR) work instructions. These AR work instructions have been shown to improve accuracy and efficiency in assembly tasks, which is crucial to the maintenance of military fleets. The study described in this paper compares two different wrist sensors, the Apple Watch and the Empatica E4. The Apple Watch is a popular, low-cost commodity wrist sensor, while the Empatica E4 is a higher cost, medical grade sensor. Participants wore both sensors while assembling a mock aircraft wing using work instructions delivered through an AR system. During the study, data such as errors, completion time, and several self-reported measures were recorded in addition to heart rate. After the study was completed, the heart rate data was extracted from the devices and analyzed. The results showed that the Apple Watch was less reliable because of its lower sample rate and gaps in data possibly due to user hand movement. Alternatively, the Empatica E4 was able to identify heart rate differences in steps of high and low difficulty with a lower standard deviation within steps. Based on these results, it was determined that the Empatica E4 was a more viable sensor for evaluating AR work instructions and that commodity sensors most likely need improvement before use in an industrial / military setting.

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This proceeding is published as Hoover, Melynda, Anastasia MacAllister, Joseph Holub, Stephen B. Gilbert, Eliot H. Winer, and Paul Davies "Assembly Training Using Commodity Physiological Sensors." In Proceedings of the 2016 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC). Volume 2016, Paper no. 16159. Arlington, VA: National Training and Simulation Association. Posted with permission.

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Fri Jan 01 00:00:00 UTC 2016