Jangwoon Park, Ph.D.

Assistant Professor,
Department of Engineering,
College of Science and Engineering,
Texas A&M University-Corpus Christi


Google Scholar Profile,


ORCID: 0000-0002-3758-2281

Research Areas

  • Human Factors

  • Ergonomic Design

  • Vehicle-seat Design

2021 HFES in Baltimore

Effects of a Bolster Curvature on an Automobile Seat Fit

Vehicle-seat dimensions, such as width, length, height, and curvature, are important measures affecting a driver’s seat fit and seating comfort. The present study is intended to quantify the relationships between subjective seat fits and seat dimensions for designing an ergonomic vehicle seat. Eight seat engineers evaluated subjective seat fits for 54 different driver seats based on their expertise. Five seat dimensions were measured at each of the six cross-sectional planes (three for the cushion and three for the seatback) by using a custom-built, computerized program. The best-subset-logistic-regression analyses were employed to model the relationships between seat fit and seat dimensions at each of the cross-sectional planes. As a result, significant seat dimensions, such as insert width, bolster height, and/or bolster curvature, on the subjective seat fit (e.g., loose-fit, right-fit, and tight-fit) were quantified. The developed models showed 98% overall classification accuracy throughout the cross-sectional planes. In addition, to our best knowledge, this is the first model to quantify the effects of bolster curvature on the seat fit. The developed models are useful to support seat engineers, when they design driver-seat dimensions, by providing a seat fit score from 0 pts (improper fit) to 100 pts (best fit) for each seat dimension. In conclusion, the models promote a digital design process of an automobile seat, which would increase the efficiency of the design process and reduce the development costs.

The Driving Posture Models are applied by Jack

My driving-posture-prediction models published at Human Factors will be embedded in Jack, which is one of the most popular digital-human-model-simulation (DHMS) software. Now, Jack users can see the effect of a driver's gender and age on driving postures. Thank you for everyone, who makes this happens! Especially! Dr. Jason Hallman at TOYOTA and Dr. Matthew P. Reed at the University of Michigan.

Texas Mobility Summit, San Antonio, TX (https://ctr.utexas.edu/texas-mobility-summit/)

In partially automated vehicles, higher levels of control automation can lead to reduced situational awareness of human drivers; This is especially problematic when automation requires human intervention during the takeover request (TOR) mode. The format of this request has implications for safe driving behavior and driver stress. Research is needed to identify configurations for TOR systems that maximize safety during control transitions in partially automated vehicles. TOR time (i.e., the amount of advanced notice given to the human driver of a pending needed to take over manual control of the vehicle) has been investigated in several empirical studies. However, research is lacking with the effects of TOR methods (e.g., visual, auditory, and/or tactile channels) and TOR time on safety-related metrics (e.g., situational awareness, cognitive stress, trust). By compiling a large multimodal data set, which includes driver performance data, physiological data, and subjective feedback, we will have an opportunity to identify ideal configurations (i.e., request methods and TOR time) for safe transitions of control. At Texas A&M University-Corpus Christi (TAMU-CC), we are currently developing a fully adjustable driving simulator that can simulate partially automated vehicles in any vehicle package conditions ranging from a mid-size sedan to a large SUV/truck. We also have advanced wearable physiological-measurement and eye-tracking systems that can accurately estimate a driver’s stress levels by using pulse, ECG, heart rate variability, skin conductance, and eye movement. This project aims to understand the relationships between TOR methods, driving performance, situational awareness, and trust in automation based on behavioral and physiological response data.

CHEST Annual Meeting 2019 in New Orleans, LA

Respiratory disturbance index (RDI) is an important measure to diagnose the severity of obstructive sleep apnea (OSA). To get an accurate RDI value of a patient, an expensive sleep study must be undergone, and the patient needs to wear sleep and breathing monitoring sensors on the skin over a night. Although previous studies developed binary classification models for estimating the presence of OSA by using a patient’s anthropometric information [e.g., neck circumference, body mass index (BMI)], research is limited to directly predict a value of RDI. The present study developed statistical models to predict a patient’s RDI value by incorporating demographic information and anthropometric dimensions.

Driving simulator in TAMU-Corpus Christi

Nowadays, an autonomous vehicle is becoming more a hot research topic in Human Factors, such as Trust in Automation, Human-Machine Interface Design. The driving simulator that we are fabricating now is going to be a great asset for AV studies. My RA students (Artemio, Joshua, Brandon, and Byran) are working together to build a fully adjustable driving simulator at Engineering Lab in the university (see attached rendered images below). The simulator will be fully adjustable to locate and rotate steering wheel and pedals for simulating various vehicles ranged from typical passenger cars to large trucks.

Ergonomics Society of Korea (ESK) 2019 in Jeju

There is an increasing need for human interaction to improve with electronics in this digital age. Socially assistive robot (SAR) interactions with the elderly and youth can improve the quality of these individuals’ lives in terms of friendships. Since humans are emotional creatures and respond more agreeably to similar personality types, robots need to be designed and programmed in a more intuitive way to capture and match the users' personal characteristics to maximize human-machine friendships. The present study is intended to identify significant vocal features associated with a human’s personality type (introverted vs. extroverted), in a digital signal processing environment, and use vocal traits for characterization. The voices of 28 university students (14 introverted and 14 extroverted) were recorded when each verbally responded to the Walk-in-the-Woods questionnaire. Then, the response time for the first question for each participant was extracted. Statistical analyses were employed to test the significance of each measure for the two personality groups (introverted vs. extroverted).

(with Drs. Park and Kim, and Dr. Park's students)

Society of Automotive Engineers (SAE) Congress 2019

A seat dimension is one of the important factors affecting a driver’s seating comfort, fit, and satisfaction. In this regard, seat engineers put tremendous efforts to evaluate seat dimensions of a product seat until the dimensions are consistent with the design reference in a computer aided design (CAD). However, the existing evaluation process is heavily relying on seat engineers’ manual tasks which are highly repetitive, labor intensive, and time-demanding tasks. The objective of this study is to develop an automated system that can efficiently and accurately evaluate seat products by comparing estimated seat dimensions from a CAD model or a 3D scan model. By using the developed system, the evaluation time for comparing 18 seat dimensions on CAD and scan models has been substantially reduced by less than 1 minute, which is 99% time saving compared to 2 hours in the manual process. In addition, the seat dimensions can be more repeatedly measured than manual measurements by using developed computer-based algorithms. In conclusion, the developed system is particularly useful for quantitatively controlling the quality of manufactured seat products and for constructing a database of seat dimensions for better data management and benchmarking with other vehicle seats.

2nd Southwest Texas Asian Symposium 2018

Senior population is more vulnerable than young population in terms of having a quality sleep, since they tends to spend more time in a light sleep stage (Stage I) than young people. To improve sleep quality of senior population, it is important to identify abnormal sleep behavior changes by using a sleep monitoring system. This study is focus on to develop a non-obtrusive and accurate sleep position detecting system by using MS Kinect v2 sensor. We found that substantial differences in sleep position changes between older female (84 YO) and young male (33 YO) based on our sleep monitoring system.

Applied Human Factors and Ergonomics (AHFE) 2018

I made two presentations in the AHFE conference, Orlando, FL this month. One is the physiological responses in the automation failure and the other one is my swimming goggle research with 2D images. Active discussion, comments, and suggestions were followed up after each of the presentations (I appreciated folks who participated in the Q&A time). Meanwhile, I was impressed about the research at Hunan University and China National Institute of Standardization.

Orlando, It was my first time to visit in FL. The first impression is very humid compared to MI and TX, but I enjoyed the weather though. Since Orlando is known as a vacation city, it was fun to see a lot of tourists in the airport and can't forget the great food in the airport. The next conference will be held in DC in 2019. Hopefully, I will be there to see the HF folks again as well as good research outcomes.

Human Factors and Ergonomics Society (HFES) 2017

I attended HFES conference 2017 in Austin, TX this month. I served as a session chair and co-chair for Driver Safety and Hand Held Device sessions, respectively. In this conference, my colleagues at POSTECH, Younggeun Choi and Seunghoon Lee made good presentations entitled 'Analysis of grip posture for ergonomic smartphone interface design' and 'Development of statistical models for predicting a driver's hip and eye location', respectively. For networking, it was so great to meet my colleagues,

Dr. Thomas Ferris, Dr. Jaehyun Park, Heejin Jeong, Dr. Xiaopeng Yang, Jihyoung lee, Hayoung Jung, and Carolina Rodriguez. In addition, it was my pleasure to meet my mentor, Dr. Matthew Reed. Hope to see you guys again at HFES conference 2018 in Philadelphia, PA.

(Seunghoon) (UT Austin)

Engineering seminar 2017

On October 9th (Mon), my Ph.D. adviser, Dr. Heecheon You, who is a full professor in the Department of Industrial and Management Engineering at POSTECH in South Korea, gave a 45-min lecture at Texas A&M University - Corpus Christi. A wide range of audiences attended this engineering seminar including undergraduate students, faculty, staffs, and Associate Dean (Dr. Lea-Der Chen). The presentation topic was "Integration of Ergonomics & Information Communication Technologies for Smart Healthcare Product Development". Overall, it talked about Dr. You's research outcomes on ergonomics R&D activities as well as smart healthcare products that were recently developed in EDT lab, including Korean helicopter cockpit design, ergonomic ear set design, Dr. Liver, Smart Harmony, Finger Touch, Swallow Monitoring system, and i-Care, etc. Dr. You concluded his presentation with potential research collaborations between TAMU-CC and POSTECH, especially for aging in place technologies.

Applied Human Factors and Ergonomics (AHFE) 2017

I made a presentation about development of a designing protocol for swimming goggles using a user's 3D facial scan data in AHFE conference 2017 in Los Angeles. Product design technology by using users' 3D facial scan data is already developed and a number of papers regarding that topic are published (see Lee et al., 2013). However, research applying the technology to design swimming goggle is limited. This presentation is talking about how to overcome ergonomic issues of a typical swimming goggle by maximizing better fit and minimizing water leakage and drag force. If you have any questions on my presentation, please feel free to email me (jangwoon.park@tamucc.edu). This research is sponsored by Texas A&M University -- Corpus Christi Research Enhancement (RE) funding program. Special thanks to Dr. Mehrube Mehrubeoglu for her Senior Capstone Design Project class and her senior students (Jose Coleman, Christopher Hernandez, Joshua Hernandez, Shane Hubenak, and Aric McBride).