Assembly Training Using Commodity Physiological Sensors

Abstract

<p>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.</p>