Background: Different posterior tibial slopes (PTS) after posterior-stabilized total knee arthroplasty (PS-TKA) may lead to different biomechanical characteristics of knee joint. This cadaveric study was designed to investigate the tibiofemoral kinematics and contact pressures after PS-TKA with different PTS.Methods: Nine human cadaveric knee specimens were used for PS-TKA with the PTS of 3°, 6°, and 9°. The tibiofemoral kinematics and contact pressures were measured during knee flexion angle changing from 0 to 120° (with an increment of 10°) with an axial load of 1000 N at each angle.Results: The root mean square (RMS) of the tibiofemoral contact area and the mean and peak contact pressures during knee flexion were 586.2 mm², 1.85 MPa, and 5.39 MPa before TKA and changed to 130.2 mm², 7.56 MPa, and 17.98 MPa after TKA, respectively. Larger contact area and smaller mean and peak contact pressures were found in the joints with the larger PTS after TKA. The RMS differences of femoral rotation before and after TKA were more than 9.9°. The posterior translation of the lateral condyle with larger PTS was more than that with smaller PTS, while overall, the RMS differences before and after TKA were more than 11.4 mm.Conclusion: After TKA, the tibiofemoral contact area is reduced, and the contact pressure is increased greatly. Approximately 80% of the femoral rotation is lost, and only about 60% of the femoral translation of lateral condyle is recovered. TKA with larger PTS results in more posterior femoral translation, larger contact area, and smaller contact pressure, indicating that with caution, it may be beneficial to properly increase PTS for PS-TKA.

Background: Parkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a critical gap in the early and non-invasive detection of PD. Current diagnostic methods are often invasive, expensive, or late in identifying the disease, leading to missed opportunities for early intervention.Objective: The goal of this study is to explore the efficiency and accuracy of combining fNIRS technology with machine learning algorithms in diagnosing early-stage PD patients and to evaluate the feasibility of this approach in clinical practice.Methods: Using an ETG-4000 type near-infrared brain function imaging instrument, data was collected from 120 PD patients and 60 healthy controls. This cross-sectional study employed a multi-channel mode to monitor cerebral blood oxygen changes. The collected data were processed using a general linear model and β values were extracted. Subsequently, four types of machine learning models were developed for analysis: Support vector machine (SVM), K-nearest neighbors (K-NN), random forest (RF), and logistic regression (LR). Additionally, SHapley Additive exPlanations (SHAP) technology was applied to enhance model interpretability.Results: The SVM model demonstrated higher accuracy in differentiating between PD patients and control group (accuracy of 85%, f1 score of 0.85, and an area under the ROC curve of 0.95). SHAP analysis identified the four most contributory channels (CH) as CH01, CH04, CH05, and CH08.Conclusion: The model based on the SVM algorithm exhibited good diagnostic performance in the early detection of PD patients. Future early diagnosis of PD should focus on the Frontopolar Cortex (FPC) region.

Odonate is a new, intelligent three-dimensional gait analysis system based on binocular depth cameras and neural networks, but its accuracy has not been validated. Twenty-six healthy subjects and sixteen patients with post-stroke were recruited to investigate the validity and reliability of Odonate for gait analysis and examine its ability to discriminate abnormal gait patterns. The repeatability tests of different raters and different days showed great consistency. Compared with the results measured by Vicon, gait velocity, cadence, step length, cycle time, and sagittal hip and knee joint angles measured by Odonate showed high consistency, while the consistency of the gait phase division and the sagittal ankle joint angle was slightly lower. In addition, the stages with statistical differences between healthy subjects and patients during a gait cycle measured by the two systems were consistent. In conclusion, Odonate has excellent inter/intra-rater reliability, and has strong validity in measuring some spatiotemporal parameters and the sagittal joint angles, except the gait phase division and the ankle joint angle. Odonate is comparable to Vicon in its ability to identify abnormal gait patterns in patients with post-stroke. Therefore, Odonate has the potential to provide accessible and objective measurements for clinical gait assessment.

Background: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial component in the rehabilitation assessment of post-stroke hemiplegic patients. However, the sensor data generated from such analyses are often complex and challenging to interpret in clinical practice, requiring significant time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry, utilizing sophisticated sensors, is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of sensor-acquired isokinetic muscle strength data, combined with machine learning techniques, to predict the GDI in hemiplegic patients. This study utilizes data captured from sensors embedded in the Biodex dynamometry system and the Vicon 3D motion capture system, highlighting the integration of sensor technology in clinical gait analysis. Methods: This study was a cross-sectional, observational study that included a cohort of 150 post-stroke hemiplegic patients. The sensor data included measurements such as peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. The GDI was calculated using data from a Vicon 3D motion capture system. This study employed four machine learning models—Lasso Regression, Random Forest (RF), Support Vector regression (SVR), and BP Neural Network—to model and validate the sensor data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R² of 0.89, and an MAE of 2.99. In contrast, the Lasso Regression model yielded an MSE of 22.29, an R² of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R² of 0.82, and an MAE of 7.68, while the BP Neural Network model exhibited the poorest performance with an MSE of 50.38, an R² of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical sensor-derived features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusions: This study highlights the potential of integrating advanced sensor technology with machine learning techniques in the analysis of complex clinical data. The developed GDI prediction model, based on sensor-acquired isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application.

Background: Neuromuscular electrical stimulation (NMES) is widely used to improve neuromuscular function, but most studies in healthy older adults have focused on longer-term outcomes such as muscle strength, mass, or architecture. Whether a single NMES session can induce measurable immediate changes in ankle-related neuromuscular performance and task-evoked cortical activity in healthy older adults remains unclear. Methods: Twenty-seven healthy older adults participated in this within-participant pre-post design study. A single 20-min NMES session was applied to the right tibialis anterior (40 Hz, maximum tolerable intensity, 2 s on/10 s off), with intensity individualized to maximal tolerable contraction (mean ± SD: 28.5 ± 6.2 mA; range: 18–42 mA). Before and after NMES, maximal ankle dorsiflexion performance was assessed on a dynamometer, and passive muscle biomechanical properties were quantified with MyotonPro. Whole-brain fNIRS was recorded during the dorsiflexion task. HbO signals were analyzed using a GLM with channel- and ROI-level inference, effect sizes, and false discovery rate (FDR) correction. Results: NMES increased ankle dorsiflexion strength immediately after stimulation, whereas passive muscle mechanical properties showed no measurable short-term change. Task-related cortical activation was observed in both the pre-intervention and post-intervention sessions. Although exploratory analyses suggested some localized post-pre differences, no cortical effects survived correction for multiple comparisons. Exploratory brain-behavior correlations were observed, but none remained significant after correction. Conclusion: A single session of NMES improved ankle dorsiflexion strength immediately in healthy older adults, while passive muscle mechanical properties did not change over the same period. Cortical activation was observed in both sessions, but no pre-post differences survived correction for multiple comparisons. The strength findings support an acute neuromuscular effect of NMES, whereas the cortical findings remain inconclusive.

[This corrects the article DOI: 10.3389/fragi.2026.1726632.].