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Böhm H, Stebbins J, Kothari A, Dussa CU. Dynamic Gait Analysis in Paediatric Flatfeet: Unveiling Biomechanical Insights for Diagnosis and Treatment. CHILDREN (BASEL, SWITZERLAND) 2024; 11:604. [PMID: 38790599 PMCID: PMC11119624 DOI: 10.3390/children11050604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Flatfeet in children are common, causing concern for parents due to potential symptoms. Technological advances, like 3D foot kinematic analysis, have revolutionized assessment. This review examined 3D assessments in paediatric idiopathic flexible flat feet (FFF). METHODS Searches focused on paediatric idiopathic FFF in PubMed, Web of Science, and SCOPUS. Inclusion criteria required 3D kinematic and/or kinetic analysis during posture or locomotion, excluding non-idiopathic cases, adult feet, and studies solely on pedobarography or radiographs. RESULTS Twenty-four studies met the criteria. Kinematic and kinetic differences between FFF and typical feet during gait were outlined, with frontal plane deviations like hindfoot eversion and forefoot supination, alongside decreased second peak vertical GRF. Dynamic foot classification surpassed static assessments, revealing varied movement patterns within FFF. Associations between gait characteristics and clinical measures like pain symptoms and quality of life were explored. Interventions varied, with orthoses reducing ankle eversion and knee and hip abductor moments during gait, while arthroereisis normalized calcaneal alignment and hindfoot eversion. CONCLUSIONS This review synthesises research on 3D kinematics and kinetics in paediatric idiopathic FFF, offering insights for intervention strategies and further research.
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Affiliation(s)
- Harald Böhm
- Orthopaedic Hospital for Children, Treatment Center Aschau im Chiemgau, 83229 Aschau im Chiemgau, Germany
- Faculty of Engineering and Health Göttingen, University of Applied Sciences and Arts, 37077 Göttingen, Germany
| | - Julie Stebbins
- Oxford University Hospitals NHSFT, Oxford OX3 9DU, UK; (J.S.); (A.K.)
| | - Alpesh Kothari
- Oxford University Hospitals NHSFT, Oxford OX3 9DU, UK; (J.S.); (A.K.)
| | - Chakravarthy Ughandar Dussa
- Department of Orthopaedic Trauma and Surgery, Friedrich-Alexander University Erlangen, 91054 Erlangen, Germany;
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Wang H, Su B, Lu L, Jung S, Qing L, Xie Z, Xu X. Markerless gait analysis through a single camera and computer vision. J Biomech 2024; 165:112027. [PMID: 38430608 DOI: 10.1016/j.jbiomech.2024.112027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
The assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement. Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.
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Affiliation(s)
- Hanwen Wang
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Bingyi Su
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Lu Lu
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Sehee Jung
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Liwei Qing
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Ziyang Xie
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA
| | - Xu Xu
- Edward P. Fitts Department of Industrial and Systems Engineering North, Carolina State University, Raleigh NC, 27695, USA.
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Mizuguchi Y, Nakao M, Nagai T, Takahashi Y, Abe T, Kakinoki S, Imagawa S, Matsutani K, Saito T, Takahashi M, Kato Y, Komoriyama H, Hagiwara H, Hirata K, Ogawa T, Shimizu T, Otsu M, Chiyo K, Anzai T. Machine learning-based gait analysis to predict clinical frailty scale in elderly patients with heart failure. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:152-162. [PMID: 38505484 PMCID: PMC10944685 DOI: 10.1093/ehjdh/ztad082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 03/21/2024]
Abstract
Aims Although frailty assessment is recommended for guiding treatment strategies and outcome prediction in elderly patients with heart failure (HF), most frailty scales are subjective, and the scores vary among raters. We sought to develop a machine learning-based automatic rating method/system/model of the clinical frailty scale (CFS) for patients with HF. Methods and results We prospectively examined 417 elderly (≥75 years) with symptomatic chronic HF patients from 7 centres between January 2019 and October 2023. The patients were divided into derivation (n = 194) and validation (n = 223) cohorts. We obtained body-tracking motion data using a deep learning-based pose estimation library, on a smartphone camera. Predicted CFS was calculated from 128 key features, including gait parameters, using the light gradient boosting machine (LightGBM) model. To evaluate the performance of this model, we calculated Cohen's weighted kappa (CWK) and intraclass correlation coefficient (ICC) between the predicted and actual CFSs. In the derivation and validation datasets, the LightGBM models showed excellent agreements between the actual and predicted CFSs [CWK 0.866, 95% confidence interval (CI) 0.807-0.911; ICC 0.866, 95% CI 0.827-0.898; CWK 0.812, 95% CI 0.752-0.868; ICC 0.813, 95% CI 0.761-0.854, respectively]. During a median follow-up period of 391 (inter-quartile range 273-617) days, the higher predicted CFS was independently associated with a higher risk of all-cause death (hazard ratio 1.60, 95% CI 1.02-2.50) after adjusting for significant prognostic covariates. Conclusion Machine learning-based algorithms of automatically CFS rating are feasible, and the predicted CFS is associated with the risk of all-cause death in elderly patients with HF.
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Affiliation(s)
- Yoshifumi Mizuguchi
- Department of Cardiovascular Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita-15 Nishi-7, Kita-ku, Sapporo 0608638, Japan
| | - Motoki Nakao
- Department of Cardiovascular Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita-15 Nishi-7, Kita-ku, Sapporo 0608638, Japan
| | - Toshiyuki Nagai
- Department of Cardiovascular Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita-15 Nishi-7, Kita-ku, Sapporo 0608638, Japan
| | - Yuki Takahashi
- Department of Cardiovascular Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita-15 Nishi-7, Kita-ku, Sapporo 0608638, Japan
| | - Takahiro Abe
- Department of Cardiovascular Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita-15 Nishi-7, Kita-ku, Sapporo 0608638, Japan
| | - Shigeo Kakinoki
- Department of Cardiology, Otaru Kyokai Hospital, Hokkaido, Japan
| | - Shogo Imagawa
- Department of Cardiology, National Hospital Organization Hakodate National Hospital, Hokkaido, Japan
| | - Kenichi Matsutani
- Department of Cardiology, Sunagawa City Medical Center, Hokkaido, Japan
| | - Takahiko Saito
- Department of Cardiology, Japan Red Cross Kitami Hospital, Hokkaido, Japan
| | - Masashige Takahashi
- Department of Cardiology, Japan Community Healthcare Organization Hokkaido Hospital, Sapporo, Japan
| | - Yoshiya Kato
- Department of Cardiology, Kushiro City General Hospital, Hokkaido, Japan
| | | | - Hikaru Hagiwara
- Department of Cardiology, Kushiro City General Hospital, Hokkaido, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Takahiro Ogawa
- Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Takuto Shimizu
- Technical Planning Office, INFOCOM CORPORATION, Tokyo, Japan
| | - Manabu Otsu
- Technical Planning Office, INFOCOM CORPORATION, Tokyo, Japan
| | - Kunihiro Chiyo
- Technical Planning Office, INFOCOM CORPORATION, Tokyo, Japan
| | - Toshihisa Anzai
- Department of Cardiovascular Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita-15 Nishi-7, Kita-ku, Sapporo 0608638, Japan
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Wade L, Needham L, Evans M, McGuigan P, Colyer S, Cosker D, Bilzon J. Examination of 2D frontal and sagittal markerless motion capture: Implications for markerless applications. PLoS One 2023; 18:e0293917. [PMID: 37943887 PMCID: PMC10635560 DOI: 10.1371/journal.pone.0293917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 10/21/2023] [Indexed: 11/12/2023] Open
Abstract
This study examined if occluded joint locations, obtained from 2D markerless motion capture (single camera view), produced 2D joint angles with reduced agreement compared to visible joints, and if 2D frontal plane joint angles were usable for practical applications. Fifteen healthy participants performed over-ground walking whilst recorded by fifteen marker-based cameras and two machine vision cameras (frontal and sagittal plane). Repeated measures Bland-Altman analysis illustrated that markerless standard deviation of bias and limits of agreement for the occluded-side hip and knee joint angles in the sagittal plane were double that of the camera-side (visible) hip and knee. Camera-side sagittal plane knee and hip angles were near or within marker-based error values previously observed. While frontal plane limits of agreement accounted for 35-46% of total range of motion at the hip and knee, Bland-Altman bias and limits of agreement (-4.6-1.6 ± 3.7-4.2˚) were actually similar to previously reported marker-based error values. This was not true for the ankle, where the limits of agreement (± 12˚) were still too high for practical applications. Our results add to previous literature, highlighting shortcomings of current pose estimation algorithms and labelled datasets. As such, this paper finishes by reviewing methods for creating anatomically accurate markerless training data using marker-based motion capture data.
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Affiliation(s)
- Logan Wade
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
| | - Laurie Needham
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
| | - Murray Evans
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
| | - Polly McGuigan
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
| | - Steffi Colyer
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
| | - Darren Cosker
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
| | - James Bilzon
- Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom
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Inai T, Takabayashi T. Lower-limb sagittal joint angles during gait can be predicted based on foot acceleration and angular velocity. PeerJ 2023; 11:e16131. [PMID: 37744216 PMCID: PMC10512936 DOI: 10.7717/peerj.16131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Background and purpose Continuous monitoring of lower-limb movement may help in the early detection and control/reduction of diseases (such as the progression of orthopedic diseases) by applying suitable interventions. Therefore, it is invaluable to calculate the lower-limb movement (sagittal joint angles) while walking daily for continuous evaluation of such risks. Although cameras in a motion capture system are necessary for calculating lower-limb sagittal joint angles during gait, the method is unrealistic considering the setting is difficult to achieve in daily life. Therefore, the estimation of lower-limb sagittal joint angles during walking based on variables, which can be measured using wearable sensors (e.g., foot acceleration and angular velocity), is important. This study estimates the lower-limb sagittal joint angles during gait from the norms of foot acceleration and angular velocity using machine learning and validates the accuracy of the estimated joint angles with those obtained using a motion capture system. Methods Healthy adults (n = 200) were asked to walk at a comfortable speed (10 trials), and their lower-limb sagittal joint angles, foot accelerations, and angular velocities were obtained. Using these variables, we established a feedforward neural network and estimated the lower-limb sagittal joint angles. Results The average root mean squared errors of the lower-limb sagittal joint angles during gait ranged between 2.5°-7.0° (hip: 7.0°; knee: 4.0°; and ankle: 2.5°). Conclusion These results show that we can estimate the lower-limb sagittal joint angles during gait using only the norms of foot acceleration and angular velocity, which can help calculate the lower-limb sagittal joint angles during daily walking.
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Affiliation(s)
- Takuma Inai
- National Institute of Advanced Industrial Science and Technology, Takamatsu City, Japan
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Jiang J, Skalli W, Siadat A, Gajny L. Effect of Face Blurring on Human Pose Estimation: Ensuring Subject Privacy for Medical and Occupational Health Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:9376. [PMID: 36502076 PMCID: PMC9739378 DOI: 10.3390/s22239376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/23/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
The face blurring of images plays a key role in protecting privacy. However, in computer vision, especially for the human pose estimation task, machine-learning models are currently trained, validated, and tested on original datasets without face blurring. Additionally, the accuracy of human pose estimation is of great importance for kinematic analysis. This analysis is relevant in areas such as occupational safety and clinical gait analysis where privacy is crucial. Therefore, in this study, we explore the impact of face blurring on human pose estimation and the subsequent kinematic analysis. Firstly, we blurred the subjects’ heads in the image dataset. Then we trained our neural networks using the face-blurred and the original unblurred dataset. Subsequently, the performances of the different models, in terms of landmark localization and joint angles, were estimated on blurred and unblurred testing data. Finally, we examined the statistical significance of the effect of face blurring on the kinematic analysis along with the strength of the effect. Our results reveal that the strength of the effect of face blurring was low and within acceptable limits (<1°). We have thus shown that for human pose estimation, face blurring guarantees subject privacy while not degrading the prediction performance of a deep learning model.
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Affiliation(s)
- Jindong Jiang
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, 75013 Paris, France
- Laboratoire de Conception Fabrication Commande, Arts et Metiers Institute of Technology, 57070 Metz, France
| | - Wafa Skalli
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, 75013 Paris, France
| | - Ali Siadat
- Laboratoire de Conception Fabrication Commande, Arts et Metiers Institute of Technology, 57070 Metz, France
| | - Laurent Gajny
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, 75013 Paris, France
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