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"Seong-Ho Jang"

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"Seong-Ho Jang"

Original Articles
Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model
Jung Ho Yang, Jae Hyeon Park, Seong-Ho Jang, Jaesung Cho
Ann Rehabil Med 2020;44(6):415-427.   Published online December 31, 2020
DOI: https://doi.org/10.5535/arm.20071
Objective
To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.
Methods
A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.
Results
In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.
Conclusion
The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

Citations

Citations to this article as recorded by  
  • Comparing prediction accuracy for 30-day readmission following primary total knee arthroplasty: the ACS-NSQIP risk calculator versus a novel artificial neural network model
    Anirudh Buddhiraju, Michelle Riyo Shimizu, Tony Lin-Wei Chen, Henry Hojoon Seo, Blake M. Bacevich, Pengwei Xiao, Young-Min Kwon
    Knee Surgery & Related Research.2025;[Epub]     CrossRef
  • Vision-based approach to knee osteoarthritis and Parkinson’s disease detection utilizing human gait patterns
    Zeeshan Ali, Jihoon Moon, Saira Gillani, Sitara Afzal, Muazzam Maqsood, Seungmin Rho
    PeerJ Computer Science.2025; 11: e2857.     CrossRef
  • CERAD-NAB and flexible battery based neuropsychological differentiation of Alzheimer’s dementia and depression using machine learning approaches
    Clara Dominke, Alina Maria Fischer, Timo Grimmer, Janine Diehl-Schmid, Thomas Jahn
    Aging, Neuropsychology, and Cognition.2024; 31(2): 221.     CrossRef
  • Detection of knee osteoarthritis based on recurrence quantification analysis, fuzzy entropy and shallow classifiers
    Wei Zeng, Limin Ma, Yu Zhang
    Multimedia Tools and Applications.2024; 83(4): 11977.     CrossRef
  • DETECTION OF KNEE OSTEOARTHRITIS BASED ON CENTER OF PRESSURE DATA AND THE BAT ALGORITHM
    MAHRAD POURYOSEF MIANDOAB, MOHAMMED N. ASHTIANI, ROOZBEH ABEDINI-NASSAB, SEYED MOHAMMAD REZA AKRAMI
    Journal of Mechanics in Medicine and Biology.2024;[Epub]     CrossRef
  • Inertial measurement unit sensor-based gait analysis in adults and older adults: A cross-sectional study
    Dong Hyun Yoon, Jeong-Hyun Kim, Kyuwon Lee, Jae-Sung Cho, Seong-Ho Jang, Shi-Uk Lee
    Gait & Posture.2024; 107: 212.     CrossRef
  • Classification of inertial sensor‐based gait patterns of orthopaedic conditions using machine learning: A pilot study
    Constanze Dammeyer, Corina Nüesch, Rosa M. S. Visscher, Yong K. Kim, Petros Ismailidis, Matthias Wittauer, Karl Stoffel, Yves Acklin, Christian Egloff, Cordula Netzer, Annegret Mündermann
    Journal of Orthopaedic Research.2024; 42(7): 1463.     CrossRef
  • Gait classification of knee osteoarthritis patients using shoe-embedded internal measurement units sensor
    Ahmed Raza, Yusuke Sekiguchi, Haruki Yaguchi, Keita Honda, Kenichiro Fukushi, Chenhui Huang, Kazuki Ihara, Yoshitaka Nozaki, Kentaro Nakahara, Shin-Ichi Izumi, Satoru Ebihara
    Clinical Biomechanics.2024; 117: 106285.     CrossRef
  • Explainable Deep-Learning-Based Gait Analysis of Hip–Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression
    Yong-Gyun Kim, Sungjoon Kim, Jae Hyeon Park, Seung Yang, Minkyu Jang, Yeo Joon Yun, Jae-sung Cho, Sungmin You, Seong-Ho Jang
    Sensors.2024; 24(14): 4504.     CrossRef
  • Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis
    Rabé Andersson, Javier Bermejo-García, Rafael Agujetas, Mikael Cronhjort, José Chilo
    Sensors.2024; 24(15): 4769.     CrossRef
  • Integrative approach to pedobarography and pelvis-trunk motion for knee osteoarthritis detection and exploration of non-radiographic rehabilitation monitoring
    Arnab Sarmah, Lipika Boruah, Satoshi Ito, Subramani Kanagaraj
    Frontiers in Bioengineering and Biotechnology.2024;[Epub]     CrossRef
  • Markerless vision-based knee osteoarthritis classification using machine learning and gait videos
    Slim Ben Hassine, Ala Balti, Sabeur Abid, Mohamed Moncef Ben Khelifa, Mounir Sayadi
    Frontiers in Signal Processing.2024;[Epub]     CrossRef
  • Machine learning-based detection of cervical spondylotic myelopathy using multiple gait parameters
    Xinyu Ji, Wei Zeng, Qihang Dai, Yuyan Zhang, Shaoyi Du, Bing Ji
    Biomimetic Intelligence and Robotics.2023; 3(2): 100103.     CrossRef
  • Assessment of blood flow around the knee joint in patients with knee osteoarthritis by color Doppler ultrasound
    Jianan Wu, Ying Li, Xiao Zhang, Jing Liu, Zhihui Qian, Peng Ren, Ruixia Xu, Lei Ren, Luquan Ren
    European Journal of Radiology.2023; 166: 111005.     CrossRef
  • Analysis and classification of gait patterns in osteoarthritic and asymptomatic knees using phase space reconstruction, intrinsic time-scale decomposition and neural networks
    Wei Zeng, Limin Ma, Yu Zhang
    Multimedia Tools and Applications.2023; 83(7): 21107.     CrossRef
  • Precision oncology: Artificial intelligence, circulating cell‐free DNA, and the minimally invasive detection of pancreatic cancer—A pilot study
    Ray O. Bahado‐Singh, Onur Turkoglu, Buket Aydas, Sangeetha Vishweswaraiah
    Cancer Medicine.2023; 12(19): 19644.     CrossRef
  • Design and development of foot worn piezoresistive sensor for knee pain analysis with supervised machine learning algorithms based on gait pattern
    M. Arumugaraja, B. Padmapriya, S. Poornachandra
    Measurement.2022; 200: 111603.     CrossRef
  • Analysis of Gait Characteristics Using Hip-Knee Cyclograms in Patients with Hemiplegic Stroke
    Ho Seok Lee, Hokyoung Ryu, Shi-Uk Lee, Jae-sung Cho, Sungmin You, Jae Hyeon Park, Seong-Ho Jang
    Sensors.2021; 21(22): 7685.     CrossRef
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Evaluation of Validity and Reliability of Inertial Measurement Unit-Based Gait Analysis Systems
Young-Shin Cho, Seong-Ho Jang, Jae-Sung Cho, Mi-Jung Kim, Hyeok Dong Lee, Sung Young Lee, Sang-Bok Moon
Ann Rehabil Med 2018;42(6):872-883.   Published online December 28, 2018
DOI: https://doi.org/10.5535/arm.2018.42.6.872
Objective
To replace camera-based three-dimensional motion analyzers which are widely used to analyze body movements and gait but are also costly and require a large dedicated space, this study evaluates the validity and reliability of inertial measurement unit (IMU)-based systems by analyzing their spatio-temporal and kinematic measurement parameters.
Methods
The investigation was conducted in three separate hospitals with three healthy participants. IMUs were attached to the abdomen as well as the thigh, shank, and foot of both legs of each participant. Each participant then completed a 10-m gait course 10 times. During each gait cycle, the hips, knees, and ankle joints were observed from the sagittal, frontal, and transverse planes. The experiments were conducted with both a camerabased system and an IMU-based system. The measured gait analysis data were evaluated for validity and reliability using root mean square error (RMSE) and intraclass correlation coefficient (ICC) analyses.
Results
The differences between the RMSE values of the two systems determined through kinematic parameters ranged from a minimum of 1.83 to a maximum of 3.98 with a tolerance close to 1%. The results of this study also confirmed the reliability of the IMU-based system, and all of the variables showed a statistically high ICC.
Conclusion
These results confirmed that IMU-based systems can reliably replace camera-based systems for clinical body motion and gait analyses.

Citations

Citations to this article as recorded by  
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  • Inertial measurement unit sensor-based gait analysis in adults and older adults: A cross-sectional study
    Dong Hyun Yoon, Jeong-Hyun Kim, Kyuwon Lee, Jae-Sung Cho, Seong-Ho Jang, Shi-Uk Lee
    Gait & Posture.2024; 107: 212.     CrossRef
  • Center of Mass Estimation for Impaired Gait Assessment Using Inertial Measurement Units
    Gabrielle C. Labrozzi, Holly Warner, Nathaniel S. Makowski, Musa L. Audu, Ronald J. Triolo
    IEEE Transactions on Neural Systems and Rehabilitation Engineering.2024; 32: 12.     CrossRef
  • Characterization of Walking in Mild Parkinson’s Disease: Reliability, Validity and Discriminant Ability of the Six-Minute Walk Test Instrumented with a Single Inertial Sensor
    Gaia Bailo, Francesca Lea Saibene, Virginia Bandini, Pietro Arcuri, Anna Salvatore, Mario Meloni, Anna Castagna, Jorge Navarro, Tiziana Lencioni, Maurizio Ferrarin, Ilaria Carpinella
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    Sensors.2024; 24(4): 1232.     CrossRef
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    Preeti Chauhan, Amit Kumar Singh, Naresh K Raghuwanshi
    Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.2024; 238(18): 8943.     CrossRef
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    Dana L. Lorenz, Antonie J. van den Bogert
    PeerJ.2024; 12: e17256.     CrossRef
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    Xiaotong Liu, Qiong Li, Saihui Hou, Min Ren, Xuecai Hu, Yongzhen Huang
    Neurocomputing.2024; 596: 128045.     CrossRef
  • Explainable Deep-Learning-Based Gait Analysis of Hip–Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression
    Yong-Gyun Kim, Sungjoon Kim, Jae Hyeon Park, Seung Yang, Minkyu Jang, Yeo Joon Yun, Jae-sung Cho, Sungmin You, Seong-Ho Jang
    Sensors.2024; 24(14): 4504.     CrossRef
  • Validity of Valor Inertial Measurement Unit for Upper and Lower Extremity Joint Angles
    Jacob Smith, Dhyey Parikh, Vincent Tate, Safeer Farrukh Siddicky, Hao-Yuan Hsiao
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  • Validity of wearable sensors for total knee arthroplasty (TKA) rehabilitation: A study in younger and older healthy participants
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    International Journal of Environmental Research and Public Health.2023; 20(4): 3107.     CrossRef
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    Byong Hun Kim, Sung Hyun Hong, In Wook Oh, Yang Woo Lee, In Ho Kee, Sae Yong Lee
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    Rehabilitation Research and Practice.2021; 2021: 1.     CrossRef
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    Vahid Abdollah, Tarek N. Dief, John Ralston, Chester Ho, Hossein Rouhani
    Gait & Posture.2021; 90: 137.     CrossRef
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    Clayton W. Swanson, Brett W. Fling
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  • Development of a Lower Limb Finite Element Musculoskeletal Gait Simulation Framework Driven Solely by Inertial Measurement Unit Sensors
    Sentong Wang, Kazunori Hase, Susumu Ota
    Biomechanics.2021; 1(3): 293.     CrossRef
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  • Analysis of Gait Characteristics Using Hip-Knee Cyclograms in Patients with Hemiplegic Stroke
    Ho Seok Lee, Hokyoung Ryu, Shi-Uk Lee, Jae-sung Cho, Sungmin You, Jae Hyeon Park, Seong-Ho Jang
    Sensors.2021; 21(22): 7685.     CrossRef
  • Feasibility Validation on Healthy Adults of a Novel Active Vibrational Sensing Based Ankle Band for Ankle Flexion Angle Estimation
    Peiqi Kang, Shuo Jiang, Peter B. Shull, Benny Lo
    IEEE Open Journal of Engineering in Medicine and Biology.2021; 2: 314.     CrossRef
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    Jae-Man Kwak, Tae-Hyun Ha, Yucheng Sun, Erica Kholinne, Kyoung-Hwan Koh, In-Ho Jeon
    Journal of Shoulder and Elbow Surgery.2020; 29(3): 593.     CrossRef
  • Age-Related Changes in Smoothness of Gait of Healthy Children and Early Adolescents
    Bruno Leban, Veronica Cimolin, Micaela Porta, Federico Arippa, Giuseppina Pilloni, Manuela Galli, Massimiliano Pau
    Journal of Motor Behavior.2020; 52(6): 694.     CrossRef
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    Marco Bravi, Emilio Gallotta, Michelangelo Morrone, Mirella Maselli, Fabio Santacaterina, Rossana Toglia, Calogero Foti, Silvia Sterzi, Federica Bressi, Sandra Miccinilli
    Gait & Posture.2020; 76: 175.     CrossRef
  • Determining the Most Appropriate Assistive Walking Device Using the Inertial Measurement Unit-Based Gait Analysis System in Disabled Patients
    Junhee Lee, Chang Hoon Bae, Aeri Jang, Seoyon Yang, Hasuk Bae
    Annals of Rehabilitation Medicine.2020; 44(1): 48.     CrossRef
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    Brandon T Nguyen, Nick A Baicoianu, Darrin B Howell, Keshia M Peters, Katherine M Steele
    Prosthetics & Orthotics International.2020; 44(3): 172.     CrossRef
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    José Antonio Barraza Madrigal, Jessica Cantillo Negrete, Roberto Muñoz Guerrero, Lauro Armando Contreras Rodríguez, Humberto Sossa
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The Prevalence and Characteristics of Depression in Work-related Musculoskeletal Disease
Jehwan Kwak, Hyung Kuk Kim, Taikon Kim, Seong-Ho Jang, Kyu Hoon Lee, Mi Jung Kim, Si-Bog Park, Seung Hoon Han
Ann Rehabil Med 2012;36(6):836-840.   Published online December 28, 2012
DOI: https://doi.org/10.5535/arm.2012.36.6.836
Objective

To reveal the relationship between depression and WMSD.

Method

Five physiatrists participated in the workplace musculoskeletal survey and diagnosed 724 office workers with WMSD by performing detailed history taking and physical examination. All subjects were asked to answer the Korean version of the Beck depressive inventory (K-BDI), and to express their pain according to the visual analogue scale (VAS) score. We categorized the subjects into 4 groups, myofascial pain syndrome (MPS), herniated intervertebral disk (HIVD), tenosynovitis, and others, and investigated the prevalence of depression in desk workers and relationship between WMSD and depression, and we compared pain intensity between the depression and non-depression groups. Correlation analysis was carried out between K-BDI and VAS scores in each group.

Results

The mean K-BDI score were 8.7±6.68. The prevalence of depression was higher in females than in male, and there was no relationship between age and depression. There was a significant connection between HIVD and depression (p<0.05). However, the other groups did not have significant connection to depression. The VAS score (5.02) of the depression group was significantly higher than that (4.10) of the non-depression group. In addition, there was a significant difference of VAS scores between the depression group and non-depression group in each disease group.

Conclusion

The mean VAS score of the depression group in WMSD was significantly higher than in the non-depression group. The correlation between BDI and VAS scores in the subjects was present, and the highest was in the HIVD group.

Citations

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    Shih-Ying Yang, Shih-Yen Hsu, Yi-Kai Su, Nan-Han Lu, Kuo-Ying Liu, Tai-Been Chen, Kon-Ning Chiu, Yung-Hui Huang, Li-Ren Yeh
    Diagnostics.2024; 14(21): 2456.     CrossRef
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    Yeh-Chan Kao, Ji-Ying Chen, Hsi-Han Chen, Kuang-Wen Liao, Shiau-Shian Huang
    The International Journal of Psychiatry in Medicine.2022; 57(2): 165.     CrossRef
  • Shared liability to pain, common mental disorders, and long-term work disability differs among women and men
    Jurgita Narusyte, Annina Ropponen, Ellenor Mittendorfer-Rutz, Pia Svedberg
    Pain.2020; 161(5): 1005.     CrossRef
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    Journal of Psychosomatic Research.2019; 118: 63.     CrossRef
  • Health, work and demographic factors associated with a lower risk of work disability and unemployment in employees with lower back, neck and shoulder pain
    Lisa Mather, Annina Ropponen, Ellenor Mittendorfer-Rutz, Jurgita Narusyte, Pia Svedberg
    BMC Musculoskeletal Disorders.2019;[Epub]     CrossRef
  • How are socio-demographic and psycho-social factors associated with the prevalence and chronicity of severe pain in 14 different body sites? A cross-sectional population-based survey
    Thomas Ernst Dorner, Katharina Viktoria Stein, Julia Hahne, Florian Wepner, Martin Friedrich, Ellenor Mittendorfer-Rutz
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  • Sickness absence due to back pain or depressive episode and the risk of all‐cause and diagnosis‐specific disability pension: A Swedish cohort study of 4,823,069 individuals
    T.E. Dorner, K. Alexanderson, P. Svedberg, A. Ropponen, K.V. Stein, E. Mittendorfer‐Rutz
    European Journal of Pain.2015; 19(9): 1308.     CrossRef
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  • Pain intensity is associated with self-reported disability for several domains of life in a sample of patients with musculoskeletal pain aged 50 or more
    Anabela G. Silva, Joaquim Alvarelhão, Alexandra Queirós, Nelson P. Rocha
    Disability and Health Journal.2013; 6(4): 369.     CrossRef
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The Effect of Leflunomide on Cold and Vibratory Sensation in Patients with Rheumatoid Arthritis
Hyung Kuk Kim, Si-Bog Park, Jong Woo Park, Seong-Ho Jang, Tae-Hwan Kim, Yoon-Kyoung Sung, Jae-Bum Jun
Ann Rehabil Med 2012;36(2):207-212.   Published online April 30, 2012
DOI: https://doi.org/10.5535/arm.2012.36.2.207
Objective

To evaluate the prevalence and risk factors of peripheral neuropathy in patients with rheumatoid arthritis (RA) treated with leflunomide (LEF) by quantitative sensory testing (QST).

Method

A total of 94 patients were enrolledin this study, out of which 47 patients received LEF. The other 47 patients received alternative disease-modifying antirheumatic drugs and served as the control group. The demographic characteristics, laboratory findings, concomitant diseases, and medication history were evaluated at the time of QST. The cooling (CDT) and vibratory detection threshold (VDT) as the representative components of QST were measured.

Results

Age, gender, RA duration, ESR, and CRP did not show any significant differences between the two groups. VDT did not demonstrate any significant difference in both groups. However, CDT in LEF group was significantly higher than that of the control group (8.6±2.7 in LEF vs. 5.6±3.8 in control). The proportion of RA patients in the LEF group showing abnormally high CDT was over 2 times greater than that of the control group, but these findings were not statistically significant. Age, RA duration (or LEF medication in LEF group), ESR, and CRP did not show significant correlation with CDT in both groups. VDT significantly correlated with age in both groups.

Conclusion

LEF treatment in patients with RA may lead to abnormal CDT in QST. CDT value was not affected by age, RA duration, disease activity, or LEF duration. It remains to be determined whether QST may be a valuable non-invasive instrument to evaluate the early sensory changes in patients with RA taking LEF.

Citations

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    Bénédicte Delcoigne, Ali Manouchehrinia, Christian Barro, Pascal Benkert, Zuzanna Michalak, Ludwig Kappos, David Leppert, Jon A. Tsai, Tatiana Plavina, Bernd C. Kieseier, Jan Lycke, Lars Alfredsson, Ingrid Kockum, Jens Kuhle, Tomas Olsson, Fredrik Piehl
    Neurology.2020;[Epub]     CrossRef
  • 4,812 View
  • 33 Download
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