AR is a very interesting and close technology to me. The recent advancements in this field by major players in the market has led to development in this technology by leaps and bounds. The AR industry has seen plenty of ups and downs. First introduced in the late 60’s by Harvard professor and computer scientist Ivan Sutherland, it was called the ‘Sword of Damocles’. While the term AR and AR devices have been present for a long time, it wasn’t until 2014 when Google launched the first AR glasses called Google Glass for the general masses. This device met with a huge public outcry and legislative action due to their nature of not being attractive and allegedly recording everything, which many people did not like (CNN, 2013). Microsoft soon followed Google’s suit and launched its own AR device named HoloLens which was more advanced than Google but came with a hefty price tag. The sector that largely benefitted from the advent of AR has been large scale industries where they have integrated such AR technology with their own technologies. This synergy between industries and AR have led to wide popularization of the AR technology.
I take the opportunity in this post to highlight some of the prevalent problems in today’s industries and how this synergy between AR and the workplace can be an answer to those problems.
The problem statements are presented from two perspectives: Industry’s and User’s. Certain issues are more centric to industries and others pertain to users/operators.
Problem 1: Industry - Amount of Resources
One of the core responsibilities of a company towards its employees is ensuring proper training of the employee. The training part requires a significant amount of resources, be it material, revenue or time. Certain companies also train and certify a certain portion of their workforce for a job specific task. Welding is one of those tasks and training employees in a welding task requires a significant amount of resources and safety measures. One wrong weld and the whole metal plate is wasted (Okimoto et al., 2015). Another example in case can be that for a civil and construction company. The ability of Construction Management (CM) students to solve problems is impacted by their lack of understanding of dynamic and complex spatial and temporal constraints. With a full understanding of the spatio-temporal constraints during the construction phase of a building, the CM students could definitely perform better and improve their productivity levels (Blinn et al., 2015). For such measures, a significant amount of resources is used up in training of the workforce and that just adds to the cost to the company. So, to avoid unnecessary costs to the company, there needs to be a unique and effective way to train employees while minimising the cost to the company.
Problem 2: Industry - Number of Errors
Certain tasks require a high level of skill as well as precision. Even the minutest of measurement error can lead to rework of the entire piece of job. This situation leads to an increased rework time for a company and lower production numbers. Eventually this leads to loss of revenue for the company. The root cause of such errors are personnel’s lack of understanding and limited information regarding practical 3 dimensional objects and workspace (Maurtua et al., 2007). The training scenarios are generally very 2 dimensional and are not able to paint the whole picture in the minds of operators as to how a task is performed. This leads to an information gap and higher number of errors (Hou et al., 2013).
Problem 3: User - Cognitive Workload
The majority of the workforce on shop floors are semi-literate (Sum, 1999). That means that tasks beyond a certain level of cognitive ability are hard for them to comprehend. This leads to an information gap between the demand and supply for a company and eventually leads to loss of inventory. Information or instruction given to them are often verbal or displayed on an LED board that conveys to them the requirements of the company, but does not convey any information as to how to fulfill those requirements. In the event of such occurrences, the performance of those operators automatically decreases and this leads to an increased time taken to complete a task.
Problem 4: User - Musculoskeletal Disorders (MSDs)
Certain tasks are repetitive or require operators to work in a certain posture for extended duration of time. Such tasks pose a high risk of diagnosing those operators with musculoskeletal disorders (Hales Bernard, 1996). Musculoskeletal disorders are injuries or disorders that affect the human body in a negative way and normal functioning of certain limbs or muscles are not possible in such situations. According to the US Bureau of Labor Statistics, MSDs are the single largest category of workplace injuries. They are responsible for around 30% of all workers’ compensation costs. The long term implications of such injuries are detrimental to the health of employees.
The scope of this article is going to be focusing on the problems that pertain to users/employees and how AR technology can be used to alleviate those problems in a work environment.
How AR can Resolve these Issues
Studies have found that incorporating AR in the workplace environment does facilitate a more informative and engaged workspace. According to Funk (2016), AR technology has been widely used in industry for manual assembly tasks. The prominent reason for that being context-awareness among users while using AR as a tool to help them in their tasks. In addition to the tasks, AR has also shown positive signs for providing cognitive support to operators while performing the tasks. The recent developments in sensor technology that can detect interactions accurately have facilitated the use of AR as a cognitive support tool among industries. In another study conducted by Hou et al. (2013), it is seen that integrating 3 dimensional images of virtual objects in real workspace using AR technology facilitates workers to implement correct assembly procedures and reduce errors in those tasks. The results demonstrated that using AR for assembly tasks resulted in reduced learning curves for novices, shorter task completion time, lower number of errors committed and lower total task load. All of these findings point towards a lower cognitive load on operators when performing tasks in collaboration with AR technology. It is evident from these studies that information when presented in a 3-D space tends to be more intuitive than when it is presented in a 2-D space. The integration of AR with industry is surely going to present new opportunities for employers to find unique and effective ways to train and convey information to the workforce that is easily perceived by an average employee.
Musculoskeletal Disorders (MSDs)
Studies have shown that low attention to human factors leads to unnatural postures and risky actions executed by workers while performing their routine jobs. This can result in lower performance and to something even worse, MSDs. These lead to a significant amount of health and economic impact on both the employee and the industry. In a study by Mengoni et al. (2018), the authors proposed a novel Spatial Augmented Reality (SAR) technology. This technology conveyed technical instructions during assembly tasks, provides alerts in cases of risks for humans’ safety and identifies correct postures for performing the relevant tasks. This technology was compared with an LED-monitor based system. The results demonstrated that in addition to being able to support assembly tasks in a more efficient way than the LED-monitor system, the SAR technology also helped improve operator awareness about the MSD risk levels they are exposed to while performing the task. In another study conducted by Aromaa et al. (2018), AR interface was evaluated for incorrect postures that might result in MSDs while performing maintenance tasks. The results showed that none of the participants adopted postures that were severe for their well-being or could cause MSDs in the long run. It can be seen from these studies that by integrating AR technology with the industries, we can alleviate the prevalent problems of MSDs in the workforce. AR technology can facilitate employees adopting correct postures while performing their tasks and eventually ensure longevity in service and better overall performance.
- Extensive usage of AR in industries: Industries need to step up and start adopting AR on a larger scale for supporting, training and raising awareness among their workforce. AR technology can solve a large number of problems that go largely unnoticed but still make up a chunk of bottlenecks in industries.
- Usage of AR as a workplace assistant: AR has proved to be a helpful technology when it comes to being used as a workplace assistant. In addition to having an impact on individuals’s working memory (Squires, 2017), it can be used for longstanding testing and measuring cognitive load among employees for varied tasks.
- Promoting good workplace ergonomics: Using AR to provide feedback and corrective measures for bad posture at the workplace can significantly reduce the employees’ risks of injuries and employers’ compensation costs.
Aromaa, S., Väätänen, A., Kaasinen, E., Uimonen, M., Siltanen, S. (2018, October). Human Factors and Ergonomics Evaluation of a Tablet Based Augmented Reality System in Maintenance Work. In Proceedings of the 22nd International Academic Mindtrek Conference (pp. 118-125).
Blinn, N., Robey, M., Shanbari, H., Issa, R. R. (2015). Using augmented reality to enhance construction management educational experiences. In Proceedings 32nd CIB W078 Workshop, Eindhoven, The Netherlands.
CNN article (https://www.cnn.com/2013/12/10/tech/mobile/negative-google-glass-reactions/index.html)
Funk, M. (2016). Augmented reality at the workplace: a context-aware assistive system using in-situ projection.
Hales, T. R., Bernard, B. P. (1996). Epidemiology of work-related musculoskeletal disorders. The orthopedic clinics of North America, 27(4), 679-709.
Hou, L., Wang, X., Bernold, L., Love, P. E. (2013). Using animated augmented reality to cognitively guide assembly. Journal of Computing in Civil Engineering, 27(5), 439-451.
Maurtua, I., Unceta, M., Pérez, M. A. (2007, July). Experimenting wearable solutions for workers’ training in manufacturing. In International Conference on Human-Computer Interaction (pp. 663-671). Springer, Berlin, Heidelberg.
Mengoni, M., Ceccacci, S., Generosi, A., Leopardi, A. (2018). Spatial augmented reality: An application for human work in smart manufacturing environment. Procedia Manufacturing, 17, 476-483.
Okimoto, M. L. L., Okimoto, P. C., Goldbach, C. E. (2015). User experience in augmented reality applied to the welding education. Procedia Manufacturing, 3, 6223-6227.
Squires, D. R. (2017). Working Memory Augmented Reality's Trajectory: A Literature Review of AR in Education, Online Learning, Workforce Training, and Working Memory Research. Journal of Educational Technology, 14(3), 55-63.
Sum, A. (1999). Literacy in the labor force. United States. Department of Education. National Center for Education Statistics.
US Bureau of Labor Statistics (https://www.bls.gov/iif/oshsum.htm)