Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks.

Measuring or estimating the forces acting on the human body during movement is critical for determining the biomechanical aspects relating to injury, disease and healthy ageing. In this study we examined whether quantifying whole-body motion (segmental accelerations) using a commercial markerless motion capture system could accurately predict three-dimensional ground reaction force during a diverse range of human movements: walking, running, jumping and cutting. We synchronously recorded 3D ground reaction forces (force instrumented treadmill or in-ground plates) with high-resolution video from eight cameras that were spatially calibrated relative to a common coordinate system. We used a commercially available software to reconstruct whole body motion, along with a geometric skeletal model to calculate the acceleration of each segment and hence the whole-body centre of mass and ground reaction force across each movement task. The average root mean square difference (RMSD) across all three dimensions and all tasks was 0.75 N/kg, with the maximum average RMSD being 1.85 N/kg for running vertical force (7.89 % of maximum). There was very strong agreement between peak forces across tasks, with R2 values indicating that the markerless prediction algorithm was able to predict approximately 95-99 % of the variance in peak force across all axes and movements. The results were comparable to previous reports using whole-body marker-based approaches and hence this provides strong proof-of-principle evidence that markerless motion capture can be used to predict ground reaction forces and therefore potentially assess movement kinetics with limited requirements for participant preparation.

Human foot form and function: variable and versatile, yet sufficiently related to predict function from form.

The human foot is a complex structure that plays an important role in our capacity for upright locomotion. Comparisons of our feet with those of our closest extinct and extant relatives have linked shape features (e.g. the longitudinal and transverse arches, heel size and toe length) to specific mechanical functions. However, foot shape varies widely across the human population, so it remains unclear if and how specific shape variants are related to locomotor mechanics. Here we constructed a statistical shape-function model (SFM) from 100 healthy participants to directly explore the relationship between the shape and function of our feet. We also examined if we could predict the joint motion and moments occurring within a person's foot during locomotion based purely on shape features. The SFM revealed that the longitudinal and transverse arches, relative foot proportions and toe shape along with their associated joint mechanics were most variable. However, each of these only accounted for small proportions of the overall variation in shape, deformation and joint mechanics, most likely owing to the high structural complexity of the foot. Nevertheless, a leave-one-out analysis showed that the SFM can accurately predict joint mechanics of a novel foot, based on its shape and deformation.

Foot shape is related to load-induced shape deformations, but neither are good predictors of plantar soft tissue stiffness.

Modern human feet are considered unique among primates in their capacity to transmit propulsive forces and re-use elastic energy. Considered central to both these capabilities are their arched configuration and the plantar aponeurosis (PA). However, recent evidence has shown that their interactions are not as simple as proposed by the theoretical and mechanical models that established their significance. Using three-dimensional foot scans and statistical shape and deformation modelling, we show that the shape of the longitudinal and transverse arches varies widely among the healthy adult population, and that the former is subject to load-induced arch flattening, whereas the latter is not. However, longitudinal arch shape and flattening are only one of the various foot shape-deformation relationships. PA stiffness was also found to vary widely. Yet only a small amount of this variability (approx. 10-18%) was explained by variations in foot shape, deformation and their combination. These findings add to the mounting evidence showing that foot mechanics are complex and cannot be accurately represented by simple models. Especially the interactions between longitudinal arch and PA appear to be far less constrained than originally proposed, most likely due to the many degrees of freedom provided by the structural complexity of our feet.

Time Course of Neuromuscular, Hormonal, and Perceptual Responses Following Moderate- and High-Load Resistance Priming Exercise.

PURPOSE: The aim of this study was to map responses over 32 hours following high-load (HL) and moderate-load (ML) half-squat priming. METHODS: Fifteen participants completed control, HL (87% 1RM), and ML (65% 1RM) activities in randomized, counterbalanced order. Countermovement jump (CMJ), squat jump (SJ), saliva testosterone, saliva cortisol, and perceptual measures were assessed before and 5 minutes, 8 hours, 24 hours, and 32 hours after each activity. Results are presented as percentage change from baseline and 95% confidence interval (CI). Cliff delta was used to determine threshold for group changes. RESULTS: SJ height increased by 4.5% (CI = 2.2-6.8, Cliff delta = 0.20) 8 hours following HL. CMJ and SJ improved by 6.1% (CI = 2.1-7.8, Cliff delta = 0.27) and 6.5% (CI = 1.2-11.8, Cliff delta = 0.30), respectively, 32 hours after ML. No clear diurnal changes in CMJ or SJ occurred 8 hours following control; however, increases of 3.9% (CI = 2.9-9.2, Cliff delta = 0.26) and 4.5% (CI = 0.9-8.1, Cliff delta = 0.24), respectively, were observed after 32 hours. Although diurnal changes in saliva hormone concentration occurred (Cliff delta = 0.37-0.92), the influence of priming was unclear. Perceived "physical feeling" was greater 8 hours following HL (Cliff delta = 0.36) and 32 hours after ML and control (Cliff delta = 0.17-0.34). CONCLUSIONS: HL priming in the morning may result in small improvements in jump output and psychophysiological state in the afternoon. Similar improvements were observed in the afternoon the day after ML priming.