Bridging the Data Gap: Using Robots to Simulate Human Exposures
By Ed Rutkowski
NASHVILLE, Tennessee (May 25, 2022)—One of Jennifer Shin’s first projects for ExxonMobil was measuring dock workers’ exposures to hydrocarbons during transfer of a petroleum product. As she explained Monday morning in a technical session at AIHce EXP 2022, the task was carried out only once or twice a year. If she wasn’t available to take samples on the appointed day, or if someone neglected to tell her when the task would be performed, she had to wait months before getting another chance. In addition, the limitations of data collection methods meant that she might get only one or two samples. Was this enough data to make confident assessments of worker exposures?
Since transferring to ExxonMobil Biomedical Sciences Inc. in 2012, Shin and her colleagues at EMBSI have been exploring ways to use robots to generate more high-quality personal exposure data. Shin and her team partnered with a robotics group at the University of Texas at Austin, which developed a robotic arm that could hold a spray can and mimic the motions of human workers performing two different spraying tasks.
“We felt that robots offer a unique advantage,” Shin explained, because they can perform tasks repeatedly and allow the collection of potentially endless samples. “Using robots, you have an efficient, systematic way to collect data. Basically, it’s very controlled.” She added that using robots eliminates any ethical or privacy concerns that may apply when trying to measure human exposures.
The sampling equipment Shin used included photoionization detectors, whole air sampling canisters, and charcoal tubes. Measured parameters included the volume of the experimental area, room ventilation and airflow, temperature and relative humidity, sampling duration, the amount of product released, and the amount in the air. The results, Shin said, showed that exposures were low—and that the use of robots has the potential to fill some of the data gaps that have long plagued OEHS professionals.
Ed Rutkowski is editor in chief of The Synergist.
Related: Read Jennifer Shin’s SynergistNOW blog post on the use of machine learning to improve research on worker health.
View more Synergist coverage of the conference on the highlights page on AIHA’s website.