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Background: Osteoarthritis (OA) stands as the most prevalent disability among the elderly population. Assessing functional outcomes after Total Knee Replacement (TKR) typically involves Gait analysis along with other evaluation methods. The objective of this study was to compare Gait results, including temporospatial parameters, joint angles, gait profile score (GPS), and movement analysis profiles (MAP), between conventional and iASSIST TKR techniques. Method: The study involved 21 participants (mean age 68.4 ± 4.2 years), with 16 females and 5 males. Among them, 11 patients had traditional surgery (15 TKR), and 10 patients had iASSIST surgery (13 TKR), totaling 28 knees (7 bilateral). The pre-operative Gait analysis was conducted one day before the surgical procedure, whereas the postoperative Gait analysis was performed, on average, 210 ± 20 days after surgery. Gait analysis was conducted using the Qualisys Motion capture system, operating at a rate of 120 Hz. The data were thoroughly analyzed using Visual 3D C-Motion Software. Results: An analysis of gait biomechanics metrics, encompassing temporospatial parameters, joint angles, GPS, and MAP, was undertaken. Significant differences were observed in sagittal plane joint angles of the pelvis and hip, transverse plane joint angles of the knee, cadence, and MAP of foot internal/external rotation. However, there were no statistically significant differences between the two TKR techniques in the remaining temporospatial variables, joint angles, GPS, or MAP. Conclusion: This study revealed a significant difference between iASSIST-guided TKR and conventional TKR, demonstrating that the iASSIST procedure led to improvements in walking biomechanics. Findings hold potential utility for orthopedic surgeons in their decision-making processes, ultimately contributing to the improvement of functional outcomes following TKR.
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Background: Understanding joint loading and the crucial role of joint moments is essential for developing treatment strategies in gait analysis, which often requires the precise estimation of joint moments through an inverse dynamic approach. This process necessitates the use of a force plate synchronized with a motion capture system. However, effectively capturing ground reaction force in typically developing (TD) children and those with congenital talipes equino varus (CTEV) presents challenges, while the availability and high cost of additional force plates pose additional challenges. Therefore the study aimed to develop, train, and identify the most effective machine learning (ML) model to predict joint moments from kinematics for TD children and those with CTEV. Method: In a study at the Gait Lab, 13 children with bilateral CTEV and 17 TD children underwent gait analysis to measure kinematics and kinetics, using a 12-camera Qualisys Motion Capture System and an AMTI force plate. ML models were then trained to predict joint moments from kinematic data as input. Results: The random forest regressor and deep neural networks (DNN) proved most effective in predicting joint moments from kinematics for TD children, yielding better results. The Random Forest regressor achieved an average r of 0.75 and nRMSE of 23.03 % for TD children, and r of 0.74 and 23.82 % for CTEV. DNN achieved an average r of 0.75 and nRMSE of 22.83 % for TD children, and r of 0.76 and nRMSE of 23.9 % for CTEV. Conclusions: The findings suggest that using machine learning to predict joint moments from kinematics shows moderate potential as an alternative to traditional gait analysis methods for both TD children and those with CTEV. Despite its potential, the current prediction accuracy limitations hinder the immediate clinical application of these techniques for decision-making in a pediatric population.
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Purpose: 3D bioprinting is capable of rapidly producing small-scale human-based tissue models, or organoids, for pathology modeling, diagnostics, and drug development. With the use of 3D bioprinting technology, 3D functional complex tissue can be created by combining biocompatible materials, cells, and growth factor. In today's world, 3D bioprinting may be the best solution for meeting the demand for organ transplantation. It is essential to examine the existing literature with the objective to identify the future trend in terms of application of 3D bioprinting, different bioprinting techniques, and selected tissues by the researchers, it is very important to examine the existing literature. To find trends in 3D bioprinting research, this work conducted an systematic literature review of 3D bioprinting. Methodology: This literature provides a thorough study and analysis of research articles on bioprinting from 2000 to 2022 that were extracted from the Scopus database. The articles selected for analysis were classified according to the year of publication, articles and publishers, nation, authors who are working in bioprinting area, universities, biomaterial used, and targeted applications. Findings: The top nations, universities, journals, publishers, and writers in this field were picked out after analyzing research publications on bioprinting. During this study, the research themes and research trends were also identified. Furthermore, it has been observed that there is a need for additional research in this domain for the development of bioink and their properties that can guide practitioners and researchers while selecting appropriate combinations of biomaterials to obtain bioink suitable for mimicking human tissue. Significance of the Research: This research includes research findings, recommendations, and observations for bioprinting researchers and practitioners. This article lists significant research gaps, future research directions, and potential application areas for bioprinting. Novelty: The review conducted here is mainly focused on the process of collecting, organizing, capturing, evaluating, and analyzing data to give a deeper understanding of bioprinting and to identify potential future research trends.
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Background: In non-western countries, deep squatting is a daily activity, and prolonged deep squatting is common among occupational squatters. Household tasks, taking a bath, socializing, using toilets, and performing religious acts are among the activities frequently carried out while squatting by the Asian population. High knee loading is responsible for a knee injury and osteoarthritis. Finite element analysis is an effective tool to determine stresses on the knee joint. Methods: Magnetic Resonance Imaging (MRI) and Computed Tomographic (CT) images were acquired of one adult without knee injuries. The CT images were acquired at the fully extended knee and one more set of images was acquired with the knee at a deeply flexed knee position. The MRI was acquired with the fully extended knee. 3-Dimensional models of bones were created using CT and soft tissue using MRI with the help of 3D Slicer software. Kinematics and finite element analysis of the knee was performed for standing and deep squatting posture using Ansys Workbench 2022. Results: High peak stresses were observed at deep squatting compared to standing along with the reduction in the contact area. Peak von Mises stresses on femoral cartilage, tibial cartilage, patellar cartilage, and meniscus were increased from 3.3 MPa to 19.9 MPa, 2.9 MPa to 12.4 MPa, 1.5 MPa to 16.7 MPa and 15.8 MPa to 32.8 MPa respectively during deep squatting. Posterior translation of 7.01 mm, and 12.58 mm was observed for medial and lateral femoral condyle respectively from full extension to 153° knee flexion. Conclusions: Increased stresses in the knee joint at deep squat posture may cause cartilage damage. A sustained deep squat posture should be avoided for healthy knee joints. More posterior translations of the medial femoral condyle at higher knee flexion angle warrant further investigation.
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Background: Increased knee flexion is required for deep squatting in the daily life of the non-western population as well as in many sports activities. The purpose of this study was to estimate as well as to compare knee joint contact forces during heel contact (HC) and heel rise (HR) deep squatting in 10 healthy young Indian participants. Materials and Methods: Kinematic data were captured using a 12-camera Motion Analysis system. Kinetic data were collected using two Kistler force plates. EMG of 6 lower limb muscles was monitored by Noraxon wireless EMG. OpenSim musculoskeletal model was customized to increase the maximum knee flexion capability of the existing model and knee joint contact forces were estimated. Results: A significant difference in tibiofemoral (p < 0.001) as well as patellofemoral (p = 0.006) knee joint contact force was observed between HC and HR squatting. The resultant maximum tibiofemoral KJCF was 5.9 (± 0.54) times body weight (BW) and 5.3 (± 0.6) BW for the HC and HR, respectively. The resultant maximum patellofemoral KJCF was 7.8 (± 0.57) BW and 7.1 (± 0.73) BW for the HC and HR, respectively. Conclusion: The findings can provide implications for physiotherapists to design rehabilitation exercise protocols, exercise professionals, and the development of high flexion knee implants. Supplementary Information: The online version contains supplementary material available at 10.1007/s43465-022-00798-y.
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This review article provides an overview of techniques used to determine human knee joint contact forces during squatting. The main two approaches are experimental and theoretical. Thigh calf contact has a significant effect on knee forces and should not be neglected. In this study, data were searched electronically and organized by techniques to find knee joint contact force during squatting theoretically and experimentally. There was a large variation in peak tibiofemoral (CV = 0.45) and patellofemoral (CV = 0.38) contact forces predicted theoretically. However, very little variation was observed between peak tibiofemoral contact forces (CV = 0.12) measured in vivo experimentally but measured knee joint force is available up to a limited knee flexion angle. There was a reduction in knee joint contact forces due to thigh calf contact. Literature of knee joint contact force prediction theoretically during squatting incorporating thigh calf contact force is very limited.