Researchers from FAMU-FSU College of Engineering and the Florida State University Department of Statistics are teaming up in a National Science Foundation-funded study that could help people perform better in manufacturing and other industries that depend on energy. man.
Chiwoo Park, an associate professor at FAMU-FSU College of Engineering, and Anuj Srivastava, a professor in the Department of Statistics at FSU’s College of Arts and Sciences, are developing motion and time analysis to measure the various movements of the human body during labor. The NSF is funding the study with $375,425.
Human performance varies due to many factors, including an individual’s level of training, error, and fatigue. Through this research, the team hopes to create standardized work practices and identify optimal ways for people to complete tasks.
Park and Srivastava have published previous research on this topic in Operational researchand their NSF study will build on this work by using artificial intelligence to collect metrics faster and more accurately than manual methods.
“Motion and time analysis traditionally involves many labor-intensive steps, such as synchronization with stopwatches and manual data analysis,” Park said. “We want to leverage modern motion sensor technology and artificial intelligence to automate these manual steps so that analysis can be performed regularly for process analysis and improvement.”
Data analysis for human operations is essential in applications such as ergonomics, healthcare, and service industries that involve repetitive manual tasks. Grocery stores, theme parks, factories, offices and hospitals are just a few of the workplaces where improved analytics could have an impact.
Analyzing the shapes that make up a person’s body is difficult because camera angle, image quality, and other clutter in the scene can make it difficult to capture data. Different people have different movement styles, gait patterns, and work rates that need to be considered in the analysis.
“We are developing statistical techniques that help capture typical motion patterns and relate them to industrial performance metrics,” Srivastava said. “Cross-disciplinary collaborations like this are driving the latest innovations in scientific research and data science, so it’s exciting to have this opportunity.”