The effect of foot orientation modifications on knee joint biomechanics during different activities

Introduction Foot position during daily activities can influence the magnitude and rate of knee joint loading [1]. Over time, increased loading can cause cumulative damage to the articulating surfaces of the knee joint, especially in people with existing knee osteoarthritis [2]. Knee joint loading is difficult to measure in vivo as the majority of knee loading is distributed on the medial compartment of the knee joint, therefore, knee adduction moment (KAM) is commonly used as a surrogate measure for knee joint loading [3].   Foot orientation is believed to have an impact on knee loading during daily activities such as walking and standing from a chair, altering the direction of the ground reaction force vector to reduce the adduction moment arm, relative to the knee joint [4]. However, limited studies have systematically explored the effect of foot orientation on KAM in activities other than walking, which is crucial for improving functional mobility and quality of life in this population beyond the lab. Therefore, this study aims to evaluate the effect of different foot orientations (toe-in, parallel and toe-out) on knee loading across several daily activities (walking, sit-to-stand, and stair climbing).   Methods Twenty-nine participants (56 ± 5 years, 170 ± 8 cm, 74 ± 14 kg) performed over-ground walking, stair climbing and sit-to-stand movements at their preferred constant speed under three foot conditions, 10° toe-in, 10° toe-out, neutral (0°). Participants performed walking and sit-to-stand on overground force plates, and stair climbing on a portable force plate embedded within the stairs. Each condition within each activity was repeated until five successful trials were obtained.   Three-dimensional kinematic (200 Hz) and kinetic data (1000 Hz) were recorded to obtain knee joint moments and foot progression angles. Foot progression angle was identified using the frontal angle of foot (defined as a 6DOF rigid body) to the global coordinate system (QTM). KAM was computed using inverse dynamics (Visual 3D) and normalised to body mass. Mean within-participant values were calculated for statistical analysis, with repeated measures ANOVA and Bonferroni post-hoc analysis used to compare the KAMs of three foot orientations across all activities.   Results KAMs during toe-in foot position were significantly lower than those under neutral foot position during walking (P = 0.011), stair climbing and sit-to-stand (P &lt; 0.001), while the KAMs during neutral foot position were significantly lower than those in toe-out foot position across all activities (P &lt; 0.001) (Fig 1). Figure 1: Median and interquartile, peak KAM for toe-out, toe-in and neutral foot position conditions during walking, stair climbing and sit-to-stand.   Discussion All results showed a significant decrease in peak KAM during the toe-in foot position condition compared to toe-out and neutral foot positions, which is consistent with previous gait studies. The results of this study indicate that toe-in gait can reduce knee joint loading not only during walking, but also in stair climbing and sit-to-stand activities.   The results of this study will be of help in gait retraining programme in clinics and rehabilitation aimed at minimising knee loading and joint pain to slow the progression of the disease. They may provide a range of clinical guidance for injury prevention in a healthy older population under the common contexts  of stair climbing and sit-to-stand, taking the technique outside the lab. Future studies should explore the effectiveness of altered foot orientation modifications on knee loading and pain reduction, in a patient population such as knee osteoarthritis.   References 1.   Valenzuela et al, J Sports Sci. Med, 15:50-56, 2016. 2.   Lynn et al, Clin Biomech, 23: 779-786, 2008. 3.   Manal et al, Osteoarthr. Cartil, 23:1107-1111, 2015. 4.   Rutherford et al, Osteoarthr. Cartil, 16:883-889, 2008.   Acknowledgements This project was funded by China Scholarship Council.</p

Backward Double Integration is a Valid Method to Calculate Maximal and Sub-Maximal Jump Height

The backward double integration method uses one force plate and could calculate jump height for countermovement jumping, squat jumping and drop jumping by analysing the landing phase instead of the push-off phase. This study compared the accuracy and variability of the forward double integration (FDI), backwards double integration (BDI) and Flight Time + Constant (FT+C) methods, against the marker-based rigid-body modelling method. It was hypothesised that the jump height calculated using the BDI method would be equivalent to the FDI method, while the FT+C method would have reduced accuracy and increased variability during sub-maximal jumping compared to maximal jumping. Twenty-four volunteers performed five maximal and five sub-maximal countermovement jumps, while force plate and motion capture data were collected. The BDI method calculated equivalent mean jump heights compared to the FDI method, with only slightly higher variability (2–3 mm), and therefore can be used in situations where FDI cannot be employed. The FT+C method was able to account for reduced heel-lift distance, despite employing an anthropometrically scaled heel-lift constant. However, across both sub-maximal and maximal jumping, it had increased variability (1.1 cm) compared to FDI and BDI and should not be used when alternate methods are available.</p

Comparisons of laboratory-based methods to calculate jump height and improvements to the field-based flight-time method

Laboratory methods that are required to calculate highly precise jump heights during experimental research have never been sufficiently compared and examined. Our first aim was to compare jumping outcome measures of the same jump, using four different methods (double integration from force plate data, rigid-body modeling from motion capture data, marker-based video tracking, and a hybrid method), separately for countermovement and squat jumps. Additionally, laboratory methods are often unsuitable for field use due to equipment or time restrictions. Therefore, our second aim was to improve an additional field-based method (flight-time method), by combining this method with an anthropometrically scaled constant. Motion capture and ground reaction forces were used to calculate jump height of twenty-four participants who performed five maximal countermovement jumps and five maximal squat jumps. Within-participant mean and standard deviation of jump height, flight distance, heel-lift, and take-off velocity were compared for each of the four methods. All four methods calculated countermovement jump height with low variability and are suitable for research applications. The double integration method had significant errors in squat jump height due to integration drift, and all other methods had low variability and are therefore suitable for research applications. Rigid-body modeling was unable to determine the position of the center of mass at take-off in both jumping movements and should not be used to calculate heel-lift or flight distance. The flight-time method was greatly improved with the addition of an anthropometrically scaled heel-lift constant, enabling this method to estimate jump height and subsequently estimate power output in the field.</p

The Performance of Open-Source Pose Estimation Algorithms During Walking, Running and Jumping

Several deep learning-based pose estimation methods (OpenPose, AlphaPose and DeepLabCut) were bench-marked against full-body marker-based motion capture. Joint centre locations between systems were evaluated during walking, running and jumping

DEVELOPMENT AND EVALUATION OF A DEEP LEARNING BASED MARKERLESS MOTION CAPTURE SYSTEM

This study presented a deep learning based markerless motion capture workflow andevaluated performance against marker-based motion capture during overground running.Multi-view high speed (200 Hz) image data were collected concurrently with marker-basedmotion capture (ground-truth data) permitting a direct comparison between methods. Lowerlimb kinematic data for six participants demonstrated high levels of agreement for lowerlimb joint angles with average RMSE ranging between 2.5° - 4.4° for hip sagittal and frontalplane motion, and 4.2° - 5.2° for knee and ankle motion. These differences generally fallwithin the known uncertainties of marker-based motion capture, suggesting that ourmarkerless approach could be used for appropriate biomechanics applications. While thereis a need for high quality open-access datasets to further facilitate performanceimprovements, markerless motion capture technology continues to improve; presentingexciting opportunities for biomechanics researchers and practitioners to capture largeamounts of high quality, ecologically valid data both in and out of the laboratory setting