It is calculated that a human being undergoes approximately 6,000 steps a day. (Althoff et al., 2017; Paluch et al., 2022; Tison et al., 2022). Therefore, any discrepancy between the joint’s load-bearing capacity and the actual load it experiences often precipitates pathological changes within the ankle joint (Peiffer et al., 2023). Given the diversity in individual gait patterns, it is plausible that certain patterns may be associated with specific pathologies, such as ankle osteoarthrosis (Jarchi et al., 2018; Horst et al., 2021).

Gait analysis enables clinicians and researchers to investigate kinematic and kinetic parameters. This crucial biomechanical information can subsequently be used to establish diagnoses, evaluate therapeutic interventions, guide rehabilitation and more (van Dijsseldonk et al., 2018; Peri et al., 2019). However, current literature lacks comprehensive discussion on the influence of walking speed on the kinematics and kinetics of the ankle joint. It is a common approach to compare the gait biomechanics of pathological individuals to those of healthy individuals during gait analysis studies. However, it is essential to consider the influence of walking speed on an individual’s gait pattern, as pathological individuals often exhibit slower walking speeds compared to healthy adults. Booij et al. have previously shown that comparing total knee replacement patients with controls depends on the walking speed, and have provided a solution for speed correction using principal component analysis and full waveform analysis by use of statistical parametric mapping (Booij et al., 2021). Failing to account for this crucial factor can impede the validity and interpretability of the comparison (Fukuchi et al., 2019). Moreover, investigating pace in gait-analysis is not trivial. Several studies have previously investigated the influence of pace on ankle biomechanics, observing a higher range of motion, joint and muscle force in the ankle with increasing speed. Clinical protocols typically encompass walking distances ranging from 4 m to 10 m (Fineberg et al., 2013; van Hoeve et al., 2017; Schreiber and Moissenet, 2019; Klöpfer-Krämer et al., 2020; Alexander et al., 2021). However, the measurement of steady-state gait using these short tests presents several challenges in terms of standardization, as walking involves natural fluctuations in gait speed due to acceleration and deceleration. These factors can significantly impact the mean gait speed observed during such measurements. Treadmill-assisted gait analysis facilitates precise control and adjustment of the subject’s pace, presenting a potential solution for these inherent limitations. It is imperative to acknowledge that the locomotor patterns observed on the treadmill may exhibit constraints, as ambulation on a treadmill differs from overground walking. (Liu et al., 2016; Krumpoch et al., 2021).

The Gait Real-time analysis Interactive Lab (GRAIL, MotekForce Link Amsterdam BV, Netherlands) is a novel self-paced treadmill-assisted gait platform that incorporates a synchronized virtual reality environment on a semicircular screen (Figure 1). The instrument has found application in prior research endeavors; however, such applications have been circumscribed. The GRAIL remains distinctive as a platform uncommonly employed in the majority of medical centers. It takes the form of a treadmill-assisted gait platform encircled by screens, facilitating the creation of a virtual reality environment for the patient. It has been mostly used in previous studies focusing on balance training and motor control in patients with a history of stroke (de Rooij et al., 2021; Van Bladel et al., 2023), neuromuscular (Gagliardi et al., 2018; van Dijsseldonk et al., 2018) and chronic respiratory diseases (Liu et al., 2016). In case of age-related ankle problems, such as ankle arthritis, the GRAIL could stand out as an instrumental tool for in-depth exploration of the ankle’s biomechanical changes. Its integration into clinical practice has the potential to revolutionize treatment approaches by facilitating precise examinations and informed decision-making, ultimately improving the overall management of this age-related condition.

Advances in computational dynamics, such as those facilitated by the AnyBody Modeling System (Anybody Technology A/S, Aalborg, Denmark) (Damsgaard, 2006) or OpenSim (Delp et al., 2007; Seth et al., 2018), offer valuable tools for investigating internal forces and moments in the ankle joint, as well as muscle forces. By integrating anatomical data with motion capture information, it utilizes inverse dynamic optimization techniques to simulate the biomechanical behavior of the musculoskeletal system (Damsgaard, 2006; Van Houcke et al., 2020; Peiffer et al., 2022).

Therefore, this study aims to explore the potential impact of walking speed on ankle kinetics and kinematics using treadmill-assisted gait analysis. We will measure these variables at different pace, with the collected data subsequently analyzed via musculoskeletal modelling and simulations, where after statistical parametric mapping will be used to identify potential time-continuous differences (Liu et al., 2016; Motek, 2023). We hypothesize that higher walking speeds will lead to alterations in the ankle kinematics, joint reaction, and muscle forces on a real-time interactive gait platform.

The objective of this study was to analyze the kinematics and kinetics of the ankle and subtalar joint in a group of healthy participants. Specifically, treadmill-assisted gait analysis was conducted using the GRAIL system at two different walking speeds of 0.8 m/s and 1.1 m/s. Joint angles, muscle forces, and joint reaction forces were calculated and compared across the different speeds. As hypothesized, the variations in walking speed had a significant impact on these parameters.

When investigating kinematics, an increase in plantarflexion and decrease in dorsiflexion was found when walking at 1.1 m/s compared to 0.8 m/s. Furthermore, a decrease in inversion was seen, while eversion remained constant. Plantarflexion occurred sooner in the gait cycle, corresponding to a faster attainment of terminal stance and pre-swing phase.

The analysis of kinetics revealed several differences, particularly in joint forces. Notably, a higher peak ankle JRF was found during midstance (i.e., the phase between heel strike and toe-off) at faster walking pace. More specifically, a mean peak force of 5.6 times BW was calculated at 0.8 m/s, compared to 6.1 times BW at 1.1 m/s. While this difference has not been shown in previous literature, the magnitude of these values are in agreement with previous studies (Brockett and Chapman, 2016; Prinold et al., 2016; Kim et al., 2018; Benemerito et al., 2020). These findings are consistent with previous studies by Dubbeldam et al. for the kinematic results and Riley et al. regarding the kinetic results (Riley et al., 2001; Dubbeldam et al., 2010). Alexander et al. have also found higher joint reaction forces for the ankle at 1.3 m/s than at 0.9 m/s, with corresponding values reported (approximately 6 times BW at 0.9 m/s and 6.3 times BW at 1.3 m/s) (Alexander et al., 2021). Additionally, our study showed that the peak joint reaction force occurred sooner in the gait cycle when walking at higher pace, presumably as a result of the peak plantarflexion occurring sooner in the gait cycle.

Regarding muscle forces, an increase in peak force of all muscle groups was found when walking at 1.1 m/s. This increase was most pronounced for the M. soleus (0.21 times BW) and M. peroneus longus (0.21 times BW). A similar trend was found in the literature, exhibiting greater muscle forces in faster walking speeds (Liu et al., 2006). Furthermore, Liu et al. found similar results for the muscles of the upper leg; namely, higher speed resulting in greater muscle forces (Liu et al., 2006). The findings of this study highlight the distinct muscle activation patterns associated with different gait phases. During the support phase, characterized by the initial heel strike, the M. tibialis anterior demonstrated significant activity, signifying its role in foot dorsiflexion. In the midstance phase, the M. tibialis posterior showed predominant activation, indicating its involvement in foot inversion. As the gait transitioned from midstance to propulsion, the M. gastrocnemius exhibited early activation, followed by pronounced engagement during the propulsion phase, jointly with the M. soleus, which played a crucial role in plantar flexion. In the transition from propulsion to the swing phase, the M. peroneus longus displayed notable activity, contributing to toe-off. Toward the end of the swing phase, both the dorsiflexors and eversion muscles demonstrated coordinated activation in preparation for the subsequent heel strike. These findings confirm the results of previous studies in this domain (Riley et al., 2001; Liu et al., 2006; Dubbeldam et al., 2010; Fukuchi et al., 2019).

The principal findings of our study, which revealed greater joint reaction forces and muscle forces acting on the ankle during higher walking pace, hold significant clinical implications that can enhance our understanding of human gait mechanics and have practical applications in clinical practice. For example, in patients with osteochondral lesions of the ankle, limiting the amount of joint reaction force causes less stress on the articular cartilage, and potentially less risk for additional mechanically-induced cartilage breakdown (Peiffer et al., 2023). Furthermore, knowledge of which muscles are most active during the specific gait phases allow clinicians and physiotherapists to target rehabilitation interventions to strengthen and stabilize the specific muscles at the appropriate time points within the gait cycle.

The foot and ankle are susceptible to age-related pathologies, such as ankle osteoarthritis, ankle instability and deformities (Barg et al., 2013; Peiffer et al., 2018; Burssens et al., 2022). These conditions induce alterations in ankle biomechanics, prompting a growing emphasis on exploring foot kinematics and gait analysis. The investigation of an individual’s biomechanics, specifically through a comprehensive gait analysis, holds substantial promise for these patients. Recognizing the nuanced variations in gait patterns among affected individuals can offer valuable insights into the progression and manifestation of these conditions (Valderrabano et al., 2007). The GRAIL system emerges as a possible optimal apparatus for the in-depth examination of such physiological dynamics. The utilization of GRAIL in clinical settings presents a promising avenue for advancing our understanding of these age-related diseases. Moreover, the implementation of the GRAIL system in clinical contexts could pave the way for the development of targeted therapeutic interventions. Since it can formulate precise and personalized treatment strategies, it holds the potential to enhance the overall quality of care for individuals grappling with these age-related afflictions.

The strengths of this study lie in the utilization of advanced technology such as the GRAIL system, assisted by treadmill, allowing for a continuous gait examination. Additionally, the AnyBody system was employed to estimate kinetics. The extensive use of markers on the foot and ankle allowed for a detailed examination of foot kinematics. Moreover, PLLN and SPM during statistical analysis made it possible to investigate both timing as magnitude significant differences during the whole gait cycle.

Several limitations of this study should be noted. First, as in all marked-based gait-analysis, errors in marker positioning can introduce errors in the described joint kinematics and subsequent calculation kinetics. By use of a multiple markers on the foot and ankle, this error was expected to be minimal. Furthermore, models were scaled using the length-mass-fat law, which is not as accurate as subject specific modelling (derived from medical imaging). Second, only young healthy participants in the age range of 18–50 years were included, without orthopedic or neurological conditions affecting gait. These do not represent the aging population. While this ensures reference values to be compared with further research in a pathological study group, it may not fully represent the aging population. Third, we have used a two-segment foot model, allowing for motion at the ankle and subtalar joint. Several previous studies have experimented with six-segment or even twenty-six-segment foot models, allowing for analysis of the different joints in the foot (Leardini et al., 1999; Forlani et al., 2015; Montefiori et al., 2022). An additional constraint necessitating consideration pertains to the sample size, which currently comprises only 20 subjects.

The findings of this study show that a higher walking pace significantly increases the peak joint reaction force and muscle force of the ankle. Furthermore, kinematic, and kinetic parameters exhibit timing differences between 0.8 m/s and 1.1 m/s walking pace. These results within young, healthy subjects may hold clinical implications for patients with foot and ankle conditions, such as rehabilitation choices to limit the forces exerted on the ankle joint. In research, it is vital to utilize standardized protocols that include predetermined walking speeds, enabling a reliable comparison of patients with average normative values.