- Assistant Professor, Systems and Industrial Engineering
- Assistant Professor, BIO5 Institute
Sol Lim is an assistant professor of Systems and Industrial Engineering at the University of Arizona. Her research combines principles from human factors and ergonomics (HF/E) and data analytics to develop new methods that can be used to improve human performance and well-being in daily lives, occupational settings, and constructed environments for diverse populations.
- Ph.D. Industrial and Operations Engineering
- University of Michigan, Ann Arbor, Michigan, United States
- Combining wearable sensing and predictive modeling for biomechanical exposure assessment in specific material handling tasks.
- M.S. Biomedical Engineering
- University of Michigan, Ann Arbor, Michigan, United States
- M.S. Industrial Engineering
- Seoul National University, Seoul, Korea, Republic of
- B.S. Clothing and Textiles
- Yonsei University, Seoul, Korea, Republic of
- Systems and Industrial Engineering, University of Arizona (2019 - Ongoing)
- Best Student Paper Award
- Occupational Ergonomics Technical Group at Human Factors and Ergonomics Society (HFES), Fall 2019
- Inaugural Outstanding Team Grant Proposal (1st place) award
- NIOSH Funded Research Capacity Building Workshop, Fall 2018
- Best poster award (1st place) in Industrial, Operations, and Financial Engineering session
- University of Michigan Engineering Graduate Symposium, Fall 2017
- COHSE Directors’ Award
- Regional Research Symposium, NIOSH Education and Research Center (ERC), Fall 2017
- People’s Choice Award
- Regional Research Symposium, NIOSH Education and Research Center (ERC), Fall 2016
- Industrial and Operations Engineering Departmental Fellowship
- University of Michigan, Fall 2015
- Pilot Project Research Training Program (PPRT) award
- Pilot project research training programaward from the National Institute for Occupational Safety and Health (NIOSH), Fall 2015
- Jungsong Foundation Scholarship
- Jungsong Foundation, Fall 2013
Licensure & Certification
- Graduate Certificate in Data Science, Michigan Institute for Data Science (MIDAS) (2018)
Ergonomics & Human factors, wearable technology, predictive modeling, occupational health & safety, healthcare ergonomics, biomechanics in disability and inclusive design
Directed ResearchSIE 492 (Spring 2021)
ResearchSIE 900 (Spring 2021)
Directed ResearchSIE 492 (Fall 2020)
Hum Fact+Ergonomics/DsgnSIE 410A (Fall 2020)
Independent StudySIE 599 (Fall 2020)
ResearchSIE 900 (Fall 2020)
Senior Dsgn Projects IISIE 498B (Fall 2020)
Directed ResearchSIE 492 (Spring 2020)
Independent StudySIE 599 (Spring 2020)
Senior Design Projects ISIE 498A (Spring 2020)
Directed ResearchSIE 492 (Fall 2019)
Hum Fact+Ergonomics/DsgnSIE 410A (Fall 2019)
- Lim, S., & D'Souza, C. (2021). Wheeled Mobility Use on Accessible Fixed-Route Transit: A Field Study in Environmental Docility. International Journal of Environmental Research and Public Health, 18(6)(2840). doi:https://doi.org/10.3390/ijerph18062840
- Lim, S., & D'Souza, C. (2020). A Narrative Review on Contemporary and Emerging Uses of Inertial Sensing in Occupational Ergonomics. International Journal of Industrial Ergonomics, 76. doi:https://doi.org/10.1016/j.ergon.2020.102937
- Lim, S., & D'Souza, C. (2020). Measuring Effects of Two-Handed Side and Anterior Load Carriage on Thoracic-Pelvic Coordination using Wearable Gyroscopes. Sensors, 20(18), 1-28. doi:https://doi.org/10.3390/s20185206
- Lim, S., & D'Souza, C. (2019). Statistical prediction of load carriage mode and magnitude from inertial sensor derived gait kinematics. Applied Ergonomics, 76, 1-11. doi:https://doi.org/10.1016/j.apergo.2018.11.007
- Grudinschi, M., Norland, K., Lee, S. W., & Lim, S. (2020, October). Task Analysis on Yoga Practice Toward the Wearable Sensor-based Learning System for Users with Visual Impairment. In 64th Annual Meeting of the Human Factors and Ergonomics Society (HFES).
- Lim, S., & D'Souza, C. (2020, October). Classifying Lifting-Lowering Height and Load Level using Inertial Sensor-based Kinematics: An Initial Study. In 64th Annual Meeting of the Human Factors and Ergonomics Society (HFES).
- Lim, S., & D'Souza, C. (2019, October). Gender and Parity in Statistical Prediction of Anterior Carry Hand-Loads from Inertial Sensor Data. In 63rd Annual Meeting of the Human Factors and Ergonomics Society (HFES).
- Lim, S., & D’Souza, C. (2018). Inertial Sensor-based Measurement of Thoracic-Pelvic Coordination Predicts Hand-Load Levels in Two-handed Anterior Carry. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62.
- Lim, S., Luo, Y., Ebert, S., Jones, M. L., Varban, O., & D’Souza, C. (2018). Preliminary Study of Obstacle Clearance and Compensatory Movements in Individuals with High Body Mass Index. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62.
- Lim, S., & D'Souza, C. (2017, October). Statistical Prediction of Hand Force Exertion Levels in a Simulated Push Task using Posture Kinematics. In Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual Meeting, 61, 1031-1035.More infoThis study explored the use of body posture kinematics derived from wearable inertial sensors to estimate force exertion levels in a two-handed isometric pushing and pulling task. A prediction model was developed grounded on the hypothesis that body postures predictably change depending on the magnitude of the exerted force. Five body postural angles, viz., torso flexion, pelvis flexion, lumbar flexion, hip flexion, and upper arm inclination, collected from 15 male participants performing simulated isometric pushing and pulling tasks in the laboratory were used as predictor variables in a statistical model to estimate handle height (shoulder vs. hip) and force intensity level (low vs. high). Individual anthropometric and strength measurements were also included as predictors. A Random Forest algorithm implemented in a two-stage hierarchy correctly classified 77.2% of the handle height and force intensity levels. Results represent early work in coupling unobtrusive, wearable instrumentation with statistical learning techniques to model occupational activities and exposures to biomechanical risk factors .
- Lim, S., Case, A., & D'Souza, C. (2016, September). Comparative Analysis of Inertial Sensor to Optical Motion Capture System Performance in Push-Pull Exertion Postures. In Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual Meeting, 60, 970-974.More infoThis study examined interactions between inertial sensor (IS) performance and physical task demand on posture kinematics in a two-handed force exertion task. Fifteen male individuals participated in a laboratory experiment that involved exerting a two-handed isometric horizontal force on an instrumented height-adjustable handle. Physical task demand was operationalized by manipulating vertical handle height, target force magnitude, and force direction. These factors were hypothesized to influence average estimates of torso flexion angle measured using inertial sensors and an optical motion capture (MC) system, as well as the root mean squared errors (RMSE) between instrumentation computed over a 3s interval of the force exertion task. Results indicate that lower handle heights and higher target force levels were associated with increased torso and pelvic flexion in both, push and pull exertions. Torso flexion angle estimates obtained from IS and MC did not differ significantly. However, RMSE increased with target force intensity suggesting potential interactive effects between measurement error and physical task demand.
- Lim, S. I., Woo, J. C., Bahn, S., & Nam, C. S. (2012). The effects of individuals’ mood state and personality trait on the cognitive processing of emotional stimuli. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56.