About

Dr. Jae Kun Shim is a Professor in the Department of Kinesiology at the University of Maryland and directs the Neuromechanics Research Core. His research focuses on the neural and mechanical mechanisms underlying human hand and digit actions, locomotion, and their applications to rehabilitation, medicine, and ergonomics. He has a background in kinesiology, biomechanics, and neuroscience, with degrees from Pennsylvania State University, Ball State University, and Kyung-Hee University in Seoul, South Korea. Dr. Shim serves as a Specialty Chief Editor of Frontiers in Human Neuroscience and as an Associate Editor of the Journal of Applied Biomechanics. His work involves neuromechanics, biomechanics, neuroscience, robotics, and bioengineering, contributing to the understanding of human movement and its clinical and ergonomic applications.

Research topics

• Computer Science
• Physics
• Engineering
• Psychology
• Artificial Intelligence
• Medicine
• Simulation
• Neuroscience
• Mathematics
• Classical mechanics
• Materials science
• Structural engineering
• Transport engineering
• Composite material
• Cognitive psychology
• Acoustics
• Automotive engineering
• Surgery

Selected publications

• A Study on the Utilization and Limitations of Generative AI-Based UI Design in the UCD Process

  Journal of Digital Contents Society · 2026-03-31

  articleOpen access1st authorCorresponding
  PublisherDOI

• Activity Type Effects Signal Quality in Electrocardiogram Devices

  Sensors · 2025-08-20 · 3 citations

  articleOpen access

  Electrocardiogram (ECG) devices are commonly used to monitor heart rate (HR) and heart rate variability (HRV), but their signal quality under non-upright or torso-dominant activities may suffer due to motion artifact and interference from surrounding musculature. We compared ECG signal quality during treadmill walking, circuit training, and an obstacle course using three chest-worn commercial devices (Polar H10, Equivital EQ-02, and Zephyr BioHarness 3.0) and a multi-lead ECG monitor (BIOPAC). Signal quality was quantified using the beat signal quality index (SQI), and HR data were rejected if SQI fell below 0.7 or if values were physiologically implausible. Signal rejection rate was calculated as the proportion of low-quality observations across device and activity type. Significant effects of both device (p < 0.001) and activity (p < 0.001) were observed, with greater signal rejection during the obstacle course and circuit training compared to treadmill walking (p < 0.01). The Zephyr exhibited significantly higher rejection rates than the Polar (p = 0.018) and BIOPAC (p = 0.017), while the Polar showed lower average rejection rates across all activities. These findings underscore the importance of including dynamic, non-upright tasks in ECG validation protocols and suggest that certain commercial devices may be more robust under realistic conditions.

  PublisherOA PDFDOI

• Midsole drop affects joint-level contribution to jump performance in females

  Footwear Science · 2025-06-20

  articleOpen accessSenior author
  PublisherDOI

• VidSole: A Multimodal Dataset for Joint Kinetics Quantification and Disease Detection with Deep Learning

  ArXiv.org · 2025-01-28

  preprintOpen access

  Understanding internal joint loading is critical for diagnosing gait-related diseases such as knee osteoarthritis; however, current methods of measuring joint risk factors are time-consuming, expensive, and restricted to lab settings. In this paper, we enable the large-scale, cost-effective biomechanical analysis of joint loading via three key contributions: the development and deployment of novel instrumented insoles, the creation of a large multimodal biomechanics dataset (VidSole), and a baseline deep learning pipeline to predict internal joint loading factors. Our novel instrumented insole measures the tri-axial forces and moments across five high-pressure points under the foot. VidSole consists of the forces and moments measured by these insoles along with corresponding RGB video from two viewpoints, 3D body motion capture, and force plate data for over 2,600 trials of 52 diverse participants performing four fundamental activities of daily living (sit-to-stand, stand-to-sit, walking, and running). We feed the insole data and kinematic parameters extractable from video (i.e., pose, knee angle) into a deep learning pipeline consisting of an ensemble Gated Recurrent Unit (GRU) activity classifier followed by activity-specific Long Short Term Memory (LSTM) regression networks to estimate knee adduction moment (KAM), a biomechanical risk factor for knee osteoarthritis. The successful classification of activities at an accuracy of 99.02 percent and KAM estimation with mean absolute error (MAE) less than 0.5 percent*body weight*height, the current threshold for accurately detecting knee osteoarthritis with KAM, illustrates the usefulness of our dataset for future research and clinical settings.

  PublisherOA PDFDOI

• Walking While Acting Sad and Happy Emotions Influences Risk Factors of Knee Osteoarthritis

  Journal of Applied Biomechanics · 2025-03-21

  articleSenior author

  Greater knee adduction moment is associated with increased risk and progression of knee osteoarthritis, and this biomechanical risk factor is modulated through kinematic gait modifications. Emotions are known to influence walking kinematics and speed, but the effect of different emotions on knee mechanics is unclear. To test this, 20 healthy participants walked while instrumented gait data was recorded. Participants initially walked naturally (baseline) and then acting 4 emotional walking conditions: Anger, Happy, Fear, and Sad, in randomized order. Statistical parametric mapping with an analysis of variance model determined the extent to which emotions influenced knee joint mechanics. Results indicated both the happy (P = .009) and sad (P < .001) condition resulted in lower knee adduction moment compared with baseline. Walking both happy and sad also resulted in walking speed changes from baseline (P < .001). A secondary analysis of covariance model with speed as the covariate indicated no significant effect of emotional condition on knee adduction moment (P > .05), which suggests that the changes from baseline can be attributed to the changes in walking speed. Decreased knee adduction is associated with reduced osteoarthritis progression and increased knee function, suggesting that walking while acting different emotions, specifically happy and sad, may moderate knee osteoarthritis risk.

  PublisherDOI

• Greater external negative mechanical work is accompanied by a greater metabolic cost of walking for socket-suspended versus bone-anchored prosthesis users with transfemoral limb loss

  Clinical Biomechanics · 2025-06-23

  article
  PublisherDOI

• Enhancing prehension strength and dexterity through cross-education effects in the elderly

  Scientific Reports · 2025-03-21 · 3 citations

  articleOpen accessSenior author

  Cross-education, the enhancement of an untrained limb following training of the opposite limb, encompasses both strength and dexterity-a vital factor in daily activities. In the elderly, where both strength and dexterity decline, investigating the simultaneous transfer of these attributes through motor training is crucial. This study explored the effects of a novel hand training program on prehension strength and hand dexterity in the elderly (> 65 years). Maximum Grasping Force (MGF), Jebsen-Taylor hand function test, and Purdue Pegboard test were measured. Training, focusing on 20% sub-maximal force control, occurred thrice weekly for five weeks. Post-training, improvements were observed in both MGF and hand function in both hands, indicating the efficacy of the program. Simultaneous inter-limb transfer effects in strength and dexterity support the potential of cross-education for hand rehabilitation in elderly or hemiparetic patients. This study contributes insights into optimizing interventions for enhancing strength and dexterity in the elderly.

  PublisherOA PDFDOI

• Gender differences in peak medial joint contact forces during activities of daily living.

  PubMed · 2025-01-01

  articleOpen access

  Women are more likely to suffer from knee osteoarthritis as compared to men. For men and women, greater peak knee medial joint contact force is associated with greater rates of knee osteoarthritis. However, it is unclear if the increased rates of knee osteoarthritis in women is associated with greater medial joint contact force. We hypothesize that because women experience greater rates of knee osteoarthritis, they would experience greater peak medial joint contact force. Fifty-two healthy, young participants (26 women, 26 men) performed sit-to-stand, stand-to-sit, self-selected speed walking, self-selected speed running, and set speed running trials over force plates while motion capture data was recorded. Medial joint contact force, scaled by bodyweight, was calculated with a reduction modeling approach from inverse dynamics data and ultrasound measured distances. Differences in peak medial joint contact force between men and women were tested with one-tailed unpaired Student's t-tests with a Bonferroni correction. No significant differences were seen between groups peak medial joint contact force in any of the tested movements. Medial joint contact force may not be able to explain the disparity in knee osteoarthritis rates between men and women.

  PublisherOA PDFDOI

• Analysing Innovations in Image Processing Digital Forensics

  Criminal Investigation Studies · 2025-04-30 · 1 citations

  article1st authorCorresponding

  영상처리 디지털포렌식 기술은 범죄 수사, 보안 감시, 법적 증거 분석 등 다양한 분야에서 중요한 역할을 하며 기술적 발전을 통해 분석의 정확성과 활용 가능성을 확대하고 있다. 본 연구는 특허 데이터를 활용하여 디지털포렌식 기술의 혁신 경로를 규명하였다. Google Patents에서 CPC 코드 G06T7/70에 해당하는 11,508건의 특허 데이터를 수집하고, 네트워크 분석과 SPLC 기법을 적용하여 주요 기술 클러스터(TR1 : AI 기반 영상 분석, TR2 : AI 기반 3D 영상 처리 및 공간 인식)를 도출하였다. TR1은 객체 탐지, 영상 품질 향상 및 법적 증거 검증을 중심으로 발전하였으며, TR2는 3D 영상 복원과 공간 인식을 활용한 증거 분석 및 사건 현장 재구성의 정밀도를 높이는 방향으로 진화하였다. 본 연구는 디지털포렌식 기술의 발전 경로를 분석하고, 기술 융합을 통해 법적 증거 검증 및 실시간 분석의 신뢰성 향상 가능성을 제시하였다. 이를 통해 디지털포렌식 기술 발전에 기여하고, 법적 증거의 신뢰성과 분석 정밀도 향상을 위한 기초 자료를 제공한다.

  PublisherDOI

• Prediction of Medial Tibiofemoral Joint Reaction Force Using Custom Instrumented Insoles and Neural Networks for Walking and Running Tasks

  Journal of Applied Biomechanics · 2025-06-23

  articleSenior author

  Medial tibiofemoral joint reaction force is a clinically relevant variable for knee osteoarthritis progression and can be estimated using complex musculoskeletal models. Musculoskeletal model estimation of this variable is time-consuming, expensive, requires trained researchers, and is restricted to lab settings. We aimed to simplify the measurement of the medial knee joint contact force during walking and running using custom instrumented insoles and deep learning methods. Motion capture, force plate, and insoles instrumented with triaxial piezoresistive force sensors recorded data while 9 young healthy female individuals walked and ran at varying speeds. Two task-specific convolutional neural networks were developed for walking and running using piezoresistive force sensors as inputs during the stance phase. Results showed that both models were able to estimate total medial joint contact force with strong correlation coefficients (r > .98) and moderate mean absolute error (<0.36 body weight). These methods show the possibility of collecting medial knee joint contact force during walking and running in a clinical setting. Future research with this framework can be used to provide biofeedback to reduce medial knee joint contact force in high-risk knee osteoarthritis groups in clinical settings and daily life.

  PublisherDOI

Recent grants

• A New Biomechanical Model to Examine Joint Control Adaptations during Running in

  NIH · $139k · 2012–2014

Frequent coauthors

• Ross H. Miller

  71 shared

• Kyung Koh

  University of Maryland, Baltimore

  67 shared

• Hyun Joon Kwon

  University of Maryland, College Park

  62 shared

• Brian S. Baum

  Massachusetts Institute of Technology

  51 shared

• Hiroaki Hobara

  45 shared

• Elizabeth M. Bell

  Park University

  27 shared

• Jaebum Park

  Seoul National University Hospital

  25 shared

• Yang Sun Park

  Yonsei University Health System

  23 shared

Labs

• Neuromechanics Research CorePI

Awards & honors

• School of Public Health, University of Maryland, 2015
• Kyung Hee International Scholar, Kyung Hee University, 2010
• Young Scientist Award (honorary), American Society of Biomec…
• Promising Young Scientist Award (honorary), International So…