Going Short: The Effects of Short-Travel Key Switches on Typing Performance, Typing Force, Forearm Muscle Activity, and User Experience.

This study examined the effects of 4 micro-travel keyboards on forearm muscle activity, typing force, typing performance, and self-reported discomfort and difficulty. A total of 20 participants completed typing tasks on 4 commercially available devices with different key switch characteristics (dome, scissors, and butterfly) and key travels (0.55, 1.3, and 1.6 mm). The device with short-travel (0.55 mm) and a dome-type key switch mechanism was associated with higher muscle activities (6%-8%, P < .01), higher typing force (12%, P < .01), slower typing speeds (8%, P < .01), and twice as much discomfort (P < .05), compared with the other 3 devices: short-travel (0.55 mm) and butterfly switch design and long travel (1.3 and 1.6 mm) with scissor key switches. Participants rated the devices with larger travels (1.3 and 1.6 mm) with least discomfort (P = .02) and difficulty (P < .01). When stratified by sex/gender, these observed associations were larger and more significant in the female participants compared with male participants. The devices with similar travel but different key switch designs had difference in outcomes and devices with different travel were sometimes not different. The results suggest that key travel alone does not predict typing force or muscle activity.

The effect of time normalization and biomechanical signal processing techniques of ground reaction force curves on deep-learning model performance.

Time-series data are common in biomechanical studies. These data often undergo pre-processing steps such as time normalization or filtering prior to use in further analyses, including deep-learning classification. In this context, it remains unclear how these preprocessing steps affect deep-learning model performance. Thus, the aim of this study is to assess the effect of time-normalization and filtering on the performance of deep-learning classification models. We also investigated the effect of amplitude scaling. Using a public dataset (Gutenburg Gait Database, a ground reaction force database of level overground walking at self-selected walking speed involving 350 healthy individuals), we trained convolutional neural network (CNN) and long short-term memory (LSTM) models to predict binary sex (male, female) using three-dimensional ground-reaction forces to which we applied different processing approaches: zero padding, interpolation to 100% of signal, filtering, and scaling (min-max, body mass). The results show that transformations resulted in differences in model performances. Highest performance was obtained using unfiltered data, zero-padding, and min-max amplitude scaling (F1-score of 91 and 87% for CNN and LSTM, respectively). Not filtering data and using min-max scaling generally improve performance for both model architectures. For interpolation, results are not consistent across model architectures. This study suggests that processing steps must be considered in applications where deep-learning classification performance is relevant.

Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data.

Clinical datasets often comprise multiple data points or trials sampled from a single participant. When these datasets are used to train machine learning models, the method used to extract train and test sets must be carefully chosen. Using the standard machine learning approach (random-wise split), different trials from the same participant may appear in both training and test sets. This has led to schemes capable of segregating data points from a same participant into a single set (subject-wise split). Past investigations have demonstrated that models trained in this manner underperform compared to those trained using random-split schemes. Additional training of models via a small subset of trials, known as calibration, bridges the gap in performance across split schemes; however, the amount of calibration trials required to achieve strong model performance is unclear. Thus, this study aims to investigate the relationship between calibration training set size and prediction accuracy on the calibration test set. A database of 30 young, healthy adults performing multiple walking trials across nine different surfaces while fit with inertial measurement unit sensors on the lower limbs was used to develop a deep-learning classifier. For subject-wise trained models, calibration on a single gait cycle per surface yielded a 70% increase in F1-score, the harmonic mean of precision and recall, while 10 gait cycles per surface were sufficient to match the performance of a random-wise trained model. Code to generate calibration curves may be found at (https://github.com/GuillaumeLam/PaCalC).

Generalizability of deep learning models for predicting outdoor irregular walking surfaces.

Observations from laboratory-based gait analysis are difficult to extrapolate to real-world environments where gait behavior is modulated in response to complex environmental conditions and surface profiles. Inertial measurement units (IMUs) permit real-world gait analysis; however, automatic detection of surfaces encountered remains largely unexplored. The aims of this study are to quantify for machine learning models the effect of (1) random and subject-wise data splitting and (2) sensor location and count on surface classification performance. Thirty participants walked on nine surface conditions (flat-even, slope-up, slope-down, stairs-up, stairs-down, cobblestone, grass, banked-left, banked-right) wearing IMUs (wrist, trunk, bilateral thighs, bilateral shanks). Data were separated into gait cycles, normalized to 101 samples, and spilt into train and test sets (85 and 15%, respectively). For random splitting, trials were randomly assigned to the train or test set. In subject-wise splitting, all trials from 4 random participants were selected for testing. Linear discriminant analysis extracted features from the IMUs. Features were delivered to a neural network. F1-score evaluated model performance. Models achieved F1 scores of 0.96 and 0.78 using random and subject-wise splitting, respectively. Random splitting performance was mainly invariant to sensor location/count; however, subject-wise splitting showed best performance using lower-limb sensors. In general, stairs and sloped surfaces were easily predicted (F1 > 0.85) while banked surfaces were challenging, especially for subject-wise models (F1 ≈ 0.6). Neural networks can detect surfaces based on subtle changes in walking behavior captured by IMUs. Data splitting approaches and sensor location/count (subject-wise) have a non-negligible effect on model performance.

Muscle co-contractions are greater in older adults during walking at self-selected speeds over uneven compared to even surfaces.

Falls in the aging population are a major public health concern. Outdoor falls in community-dwelling older adults are often triggered by uneven pedestrian walkways. Our understanding of the motor control adaptations to walk over an uneven surface, and the effects of aging on these adaptations is sparse. Here, we study changes in muscle co-contraction, a clinically accepted measure of motor control, due to changes in walking surfaces typically encountered in the outdoor built environment. We address the following research questions: 1) are there walking surface and sex-based differences in muscle co-contractions between young and older adults? and 2) is muscle co-contraction associated with age? We calculated muscle co-contractions from 13 young and 17 older adults during walking at self-selected speeds over even and uneven brick walkways. Muscle co-contraction at the ankle joint was determined from the tibialis anterior and lateral gastrocnemius muscle pair, and at the knee joint from the rectus femoris and semitendinosus muscle pair. Older adults displayed 8-13% greater ankle muscle co-contractions during walking over uneven compared to even surfaces. We found 55-61% (entire gait) and 73-75% (stance phase) greater ankle muscle co-contractions in older females compared to older males during walking over even and uneven surfaces. We found 31-43% greater knee muscle co-contractions in older females compared to older males during the swing phase of walking over even and uneven surfaces. This study underscores the need for determining muscle co-contractions from even and uneven surfaces for quantifying motor control deficits due to aging or neuromuscular disorders.

Knee joint kinematics and neuromuscular responses in female athletes during and after multi-directional perturbations.

The purpose of this study was to investigate weight-bearing knee joint kinematic and neuromuscular responses during lateral, posterior, rotational, and combination (simultaneous lateral, posterior, and rotational motions) perturbations and post-perturbations phases in 30° flexed-knee and straight-knee conditions. Thirteen healthy female athletes participated. Knee joint angles and muscle activity of vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), semitendinosus (ST), lateral gastrocnemius (LG), and medial gastrocnemius (MD) muscles were computed. Knee abducted during lateral perturbations, whereas it adducted during the other perturbations. It was internally rotated during flexed-knee and externally rotated during straight-knee perturbations and post-perturbations. VL and VM's mean and maximum activities during flexed-knee perturbations were greater than those of straight-knee condition. BF's mean activities were greater during flexed-knee perturbations compared with straight-knee condition, while its maximum activities observed during combination perturbations. ST's maximum activities during combination perturbations were greatest compared with the other perturbations. LG and MG's activities were greater during straight-knee conditions. Compared with the perturbation phase, the mean and maximum muscles' activities were significantly greater during post-perturbations. The time of onset of maximum muscle activity showed a distinctive pattern among the perturbations and phases. The perturbation direction is an important variable which induces individualized knee kinematic and neuromuscular response.

A database of human gait performance on irregular and uneven surfaces collected by wearable sensors.

Gait analysis has traditionally relied on laborious and lab-based methods. Data from wearable sensors, such as Inertial Measurement Units (IMU), can be analyzed with machine learning to perform gait analysis in real-world environments. This database provides data from thirty participants (fifteen males and fifteen females, 23.5 ± 4.2 years, 169.3 ± 21.5 cm, 70.9 ± 13.9 kg) who wore six IMUs while walking on nine outdoor surfaces with self-selected speed (16.4 ± 4.2 seconds per trial). This is the first publicly available database focused on capturing gait patterns of typical real-world environments, such as grade (up-, down-, and cross-slopes), regularity (paved, uneven stone, grass), and stair negotiation (up and down). As such, the database contains data with only subtle differences between conditions, allowing for the development of robust analysis techniques capable of detecting small, but significant changes in gait mechanics. With analysis code provided, we anticipate that this database will provide a foundation for research that explores machine learning applications for mobile sensing and real-time recognition of subtle gait adaptations.

Knee muscle co-contractions are greater in old compared to young adults during walking and stair use.

BACKGROUND: Muscle co-contraction is an accepted clinical measure to quantify the effects of aging on neuromuscular control and movement efficiency. However, evidence of increased muscle co-contraction in old compared to young adults remains inconclusive. RESEARCH QUESTION: Are there differences in lower-limb agonist/antagonist muscle co-contractions in young and old adults, and males and females, during walking and stair use? METHODS: In a retrospective study, we analyzed data from 20 healthy young and 19 healthy old adults during walking, stair ascent, and stair descent at self-selected speeds, including marker trajectories, ground reaction force, and electromyography activity. We calculated muscle co-contraction at the knee (vastus lateralis vs. biceps femoris) and ankle (tibialis anterior vs. medial gastrocnemius) using the ratio of the common area under a muscle pairs' filtered and normalized electromyography curves to the sum of the areas under each muscle in that pair. RESULTS: Old compared to young adults displayed 18%-22% greater knee muscle co-contractions during the entire cycle of stair use activities. We found greater (17%-29%) knee muscle co-contractions in old compared to young adults during the swing phase of walking and stair use. We found no difference in ankle muscle co-contractions between the two age groups during all three activities. We found no difference in muscle co-contraction between males and females at the knee and ankle joints for all three activities. SIGNIFICANCE: Based on our findings, we recommend clinical evaluation to quantify the effects of aging through muscle co-contraction to include the knee joint during dynamic activities like walking and stair use, and independent evaluation of the stance and swing phases.

The effect of subject measurement error on joint kinematics in the conventional gait model: Insights from the open-source pyCGM tool using high performance computing methods.

The conventional gait model (CGM) is a widely used biomechanical model which has been validated over many years. The CGM relies on retro-reflective markers placed along anatomical landmarks, a static calibration pose, and subject measurements as inputs for joint angle calculations. While past literature has shown the possible errors caused by improper marker placement, studies on the effects of inaccurate subject measurements are lacking. Moreover, as many laboratories rely on the commercial version of the CGM, released as the Plug-in Gait (Vicon Motion Systems Ltd, Oxford, UK), integrating improvements into the CGM code is not easily accomplished. This paper introduces a Python implementation for the CGM, referred to as pyCGM, which is an open-source, easily modifiable, cross platform, and high performance computational implementation. The aims of pyCGM are to (1) reproduce joint kinematic outputs from the Vicon CGM and (2) be implemented in a parallel approach to allow integration on a high performance computer. The aims of this paper are to (1) demonstrate that pyCGM can systematically and efficiently examine the effect of subject measurements on joint angles and (2) be updated to include new calculation methods suggested in the literature. The results show that the calculated joint angles from pyCGM agree with Vicon CGM outputs, with a maximum lower body joint angle difference of less than 10-5 degrees. Through the hierarchical system, the ankle joint is the most vulnerable to subject measurement error. Leg length has the greatest effect on all joints as a percentage of measurement error. When compared to the errors previously found through inter-laboratory measurements, the impact of subject measurements is minimal, and researchers should rather focus on marker placement. Finally, we showed that code modifications can be performed to include improved hip, knee, and ankle joint centre estimations suggested in the existing literature. The pyCGM code is provided in open source format and available at https://github.com/cadop/pyCGM.

Late-cueing of gait tasks on an uneven brick surface impacts coordination and center of mass control in older adults.

BACKGROUND: Changing directions while walking (turning gait), often with little planning time, is essential to navigating irregular surfaces in the built-environment. It is unclear how older adults reorient their bodies under these constraints and whether adaptations are related to declines in physiological characteristics. RESEARCH QUESTION: The aims of this study were to (1) investigate whether surface irregularity, late-cueing, and age negatively affect coordination, kinematics, and center of mass (COM) movement during 90° turning gait and (2) determine if adaptations correlate with declines in strength, balance, and reaction-time. METHODS: Eighteen young (18-35 years) and sixteen older (65+ years) healthy adults participated in the study. Retro-reflective marker and trunk-accelerometry data were used to compute upper-body segmental reorientation timing, upper-body kinematics, and COM movement characteristics. Balance scores, lower-limb strength, and choice-reaction-times were also recorded. RESULTS: Young and older adults maintained a cranial-caudal (head, shoulders, pelvis) reorientation sequence (p ≤ 0.018), lowered head pitch (uneven surface; young p = 0.035 and old p < 0.001), increased maximum COM acceleration (uneven surface and late-cueing; p ≤ 0.002), and decreased COM smoothness (uneven surface; p < 0.001). Young adults increased shoulder roll (uneven surface and late-cueing; p ≤ 0.008). Reduced stride regularity (late-cueing) was observed in older (p < 0.001), compared to young (p = 0.017), adults. Declines in strength (p ≤ 0.040) and balance (p = 0.018) were correlated with gait adaptations of older adults. SIGNIFICANCE: Late-cueing on an uneven surface is challenging for older adults. These challenges are exacerbated by strength and balance deficits.

Muscular co-contraction is related to varus thrust in patients with knee osteoarthritis.

BACKGROUND: Patients with knee osteoarthritis often present with varus thrust and muscular co-contraction during gait. It is unclear if these adaptations are related. The objective was to examine the relationship between muscle co-contraction and varus thrust during gait in patients with knee osteoarthritis and to determine if these relationships are modulated by disease severity or history of knee ligament rupture. METHODS: Participants (n = 42, 23 women, mean age 58 years) with knee osteoarthritis completed gait trials at self-selected speeds. Varus thrust was measured with an eight camera motion capture system sampled at 100 Hz. Co-contraction ratios were measured with surface electromyography sampled at 2000 Hz over the quadriceps, hamstrings, and gastrocnemius. Disease severity was measured on radiographs and history of anterior cruciate ligament rupture was confirmed on magnetic resonance imaging. Linear regression analyses examined the relationship between varus thrust and co-contraction ratios after controlling for radiographic disease severity and history of anterior cruciate ligament rupture. FINDINGS: Higher vastus lateralis-lateral hamstring (b = 0.081, P < 0.001; R2 = 0.353) and vastus medialis-medial hamstring (b = 0.063, P = 0.028; R2 = 0.168) co-contraction ratios were associated with greater varus thrust. Quadriceps-gastrocnemius co-contractions ratios were not related to varus thrust (P > 0.05). Radiographic disease severity or history of anterior cruciate ligament injury did not significantly contribute to regression models. INTERPRETATION: Greater quadriceps-hamstring co-contraction is associated with greater varus thrust in patients with knee osteoarthritis. Potential explanations include increased co-contraction may provide stability or there is a proprioceptive reflex that is independent of any stabilizing role. Research is needed to test these hypotheses.

biomechZoo: An open-source toolbox for the processing, analysis, and visualization of biomechanical movement data.

It is common for biomechanics data sets to contain numerous dependent variables recorded over time, for many subjects, groups, and/or conditions. These data often require standard sorting, processing, and analysis operations to be performed in order to answer research questions. Visualization of these data is also crucial. This manuscript presents biomechZoo, an open-source toolbox that provides tools and graphical user interfaces to help users achieve these goals. The aims of this manuscript are to (1) introduce the main features of the toolbox, including a virtual three-dimensional environment to animate motion data (Director), a data plotting suite (Ensembler), and functions for the computation of three-dimensional lower-limb joint angles, moments, and power and (2) compare these computations to those of an existing validated system. To these ends, the steps required to process and analyze a sample data set via the toolbox are outlined. The data set comprises three-dimensional marker, ground reaction force (GRF), joint kinematic, and joint kinetic data of subjects performing straight walking and 90° turning manoeuvres. Joint kinematics and kinetics processed within the toolbox were found to be similar to outputs from a commercial system. The biomechZoo toolbox represents the work of several years and multiple contributors to provide a flexible platform to examine time-series data sets typical in the movement sciences. The toolbox has previously been used to process and analyse walking, running, and ice hockey data sets, and can integrate existing routines, such as the KineMat toolbox, for additional analyses. The toolbox can help researchers and clinicians new to programming or biomechanics to process and analyze their data through a customizable workflow, while advanced users are encouraged to contribute additional functionality to the project. Students may benefit from using biomechZoo as a learning and research tool. It is hoped that the toolbox can play a role in advancing research in the movement sciences. The biomechZoo m-files, sample data, and help repositories are available online (http://www.biomechzoo.com) under the Apache 2.0 License. The toolbox is supported for Matlab (r2014b or newer, The Mathworks Inc., Natick, USA) for Windows (Microsoft Corp., Redmond, USA) and Mac OS (Apple Inc., Cupertino, USA).

Skating start propulsion: three-dimensional kinematic analysis of elite male and female ice hockey players.

The forward skating start is a fundamental skill for male and female ice hockey players. However, performance differences by athlete's sex cannot be fully explained by physiological variables; hence, other factors such as skating technique warrant examination. Therefore, the purpose of this study was to evaluate the body movement kinematics of ice hockey skating starts between elite male and female ice hockey participants. Male (n = 9) and female (n = 10) elite ice hockey players performed five forward skating start accelerations. An 18-camera motion capture system placed on the arena ice surface captured full-body kinematics during the first seven skating start steps within 15 meters. Males' maximum skating speeds were greater than females. Skating technique sex differences were noted: in particular, females presented ~10° lower hip abduction throughout skating stance as well as ~10° greater knee extension at initial ice stance contact, conspicuously followed by a brief cessation in knee extension at the moment of ice contact, not evident in male skaters. Further study is warranted to explain why these skating technique differences exist in relation to factors such as differences in training, equipment, performance level, and anthropometrics.

The use of turning tasks in clinical gait analysis for children with cerebral palsy.

BACKGROUND: Turning while walking is a crucial component of locomotion that is performed using an outside (step) or inside (spin) limb strategy. The aims of this paper were to determine how children with cerebral palsy perform turning maneuvers and if specific kinematic and kinetic adaptations occur compared to their typically developing peers. METHODS: Motion capture data from twenty-two children with cerebral palsy and fifty-four typically developing children were collected during straight and 90° turning gait trials. Experimental data were used to compute spatio-temporal parameters, margin of stability, ground reaction force impulse, as well as joint kinematics and kinetics. FINDINGS: Both child groups preferred turning using the spin strategy. The group of children with cerebral palsy exhibited the following adaptations during turning gait compared to the typically developing group: stride length was decreased across all phases of the turn with largest effect size for the depart phase (2.02), stride width was reduced during the turn phase, but with a smaller effect size (0.71), and the average margin of stability during the approach phase of turning was reduced (effect size of 0.98). Few overall group differences were found for joint kinematic and kinetic measures; however, in many cases, the intra-subject differences between straight walking and turning gait were larger for the majority of children with cerebral palsy than for the typically developing children. INTERPRETATION: In children with cerebral palsy, turning gait may be a better discriminant of pathology than straight walking and could be used to improve the management of gait abnormalities.

Muscle contributions to centre of mass acceleration during turning gait in typically developing children: A simulation study.

Turning while walking requires substantial joint kinematic and kinetic adaptations compared to straight walking in order to redirect the body centre of mass (COM) towards the new walking direction. The role of muscles and external forces in controlling and redirecting the COM during turning remains unclear. The aim of this study was to compare the contributors to COM medio-lateral acceleration during 90° pre-planned turns about the inside limb (spin) and straight walking in typically developing children. Simulations of straight walking and turning gait based on experimental motion data were implemented in OpenSim. The contributors to COM global medio-lateral acceleration during the approach (outside limb) and turn (inside limb) stance phase were quantified via an induced acceleration analysis. Changes in medio-lateral COM acceleration occurred during both turning phases, compared to straight walking (p<0.001). During the approach, outside limb plantarflexors (soleus and medial gastrocnemius) contribution to lateral (away from the turn side) COM acceleration was reduced (p<0.001), whereas during the turn, inside limb plantarflexors (soleus and gastrocnemii) contribution to lateral acceleration (towards the turn side) increased (p≤0.013) and abductor (gluteus medius and minimus) contribution medially decreased (p<0.001), compared to straight walking, together helping accelerate the COM towards the new walking direction. Knowledge of the changes in muscle contributions required to modulate the COM position during turning improves our understanding of the control mechanisms of gait and may be used clinically to guide the management of gait disorders in populations with restricted gait ability.

Ground reaction forces and lower-limb joint kinetics of turning gait in typically developing children.

Turning is a common locomotor task essential to daily activity; however, very little is known about the forces and moments responsible for the kinematic adaptations occurring relative to straight-line gait in typically developing children. Thus, the aims of this study were to analyse ground reaction forces (GRFs), ground reaction free vertical torque (TZ), and the lower-limb joint kinetics of 90° outside (step) and inside (spin) limb turns. Step, spin, and straight walking trials from fifty-four typically developing children were analysed. All children were fit with the Plug-in Gait and Oxford Foot Model marker sets while walking over force plates embedded in the walkway. Net internal joint moments and power were computed via a standard inverse dynamics approach. All dependent variables were statistically analysed over the entire curves using the mean difference 95% bootstrap confidence band approach. GRFs were directed medially for step turns and laterally for spin turns during the turning phase. Directions were reversed and magnitudes decreased during the approach phase. Step turns showed reduced ankle power generation, while spin turns showed large TZ. Both strategies required large knee and hip coronal and transverse plane moments during swing. These kinetic differences highlight adaptations required to maintain stability and reorient the body towards the new walking direction during turning. From a clinical perspective, turning gait may better reveal weaknesses and motor control deficits than straight walking in pathological populations, such as children with cerebral palsy, and could potentially be implemented in standard gait analysis sessions.

Spatio-temporal parameters and lower-limb kinematics of turning gait in typically developing children.

Turning is a requirement for most locomotor tasks; however, knowledge of the biomechanical requirements of successful turning is limited. Therefore, the aims of this study were to investigate the spatio-temporal and lower-limb kinematics of 90° turning. Seventeen typically developing children, fitted with full body and multi-segment foot marker sets, having performed both step (outside leg) and spin (inside leg) turning strategies at self-selected velocity, were included in the study. Three turning phases were identified: approach, turn, and depart. Stride velocity and stride length were reduced for both turning strategies for all turning phases (p<0.03 and p<0.01, respectively), while stance time and stride width were increased during only select phases (p<0.05 and p<0.01, respectively) for both turn conditions compared to straight gait. Many spatio-temporal differences between turn conditions and phases were also found (p<0.03). Lower-limb kinematics revealed numerous significant differences mainly in the coronal and transverse planes for the hip, knee, ankle, midfoot, and hallux between conditions (p<0.05). The findings summarized in this study help explain how typically developing children successfully execute turns and provide greater insight into the biomechanics of turning. This knowledge may be applied to a clinical setting to help improve the management of gait disorders in pathological populations, such as children with cerebral palsy.

Ankle and midfoot kinetics during normal gait: a multi-segment approach.

Multi-segment foot models are increasingly being used to evaluate intra and inter-segment foot kinematics such as the motion between the hindfoot/tibia (ankle) and the forefoot/hindfoot (midfoot) during walking. However, kinetic analysis have been mainly restricted to one-segment foot models and could be improved by considering a multi-segment approach. Therefore, the aims of this study were to (1) implement a kinetic analysis of the ankle and theoretical midfoot joints using the existing Oxford Foot Model (OFM) through a standard inverse dynamics approach using only marker, force plate and anthropometric data and (2) to compare OFM ankle joint kinetics to those output by the one-segment foot plugin-gait model (PIG). 10 healthy adolescents fitted with both the OFM and PIG markers performed barefoot comfortable speed walking trials over an instrumented walkway. The maximum ankle power generation was significantly reduced by approximately 40% through OFM calculations compared to PIG estimates (p<0.001). This result was not caused by a decrease in OFM computed joint moments, but by a reduction in the angular velocity between the tibia/hindfoot (OFM) compared to the tibia/foot (PIG) (p<0.001). Additionally, analysis revealed considerable midfoot loading. One-segment foot models overestimate ankle power, and may also overestimate the contribution of the triceps surae. A multi-segment approach may help quantify the important contribution of the midfoot ligaments and musculature to power generation. We therefore recommend the use of multi-segment foot models to estimate ankle and midfoot kinetics, especially when surgical decision-making is based on the results of three-dimensional gait analysis.

Ground reaction force adaptations during cross-slope walking and running.

Though transversely inclined (cross-sloped) surfaces are prevalent, our understanding of the biomechanical adaptations required for cross-slope locomotion is limited. The purpose of this study was to examine ground reaction forces (GRF) in cross-sloped and level walking and running. Nine young adult males walked and ran barefoot along an inclinable walkway in both level (0°) and cross-slope (10°) configurations. The magnitude and time of occurrence of selected features of the GRF were extracted from the force plate data. GRF data were collected in level walking and running (LW and LR), inclined walking and running up-slope (IWU and IRU), and down-slope (IWD and IRD), respectively. The GRF data were then analyzed using repeated measures MANOVA. In the anteroposterior direction, the timing of the peak force values differed across conditions during walking (p=.041), while the magnitude of forces were modified across conditions for running (p=.047). Most significant differences were observed in the mediolateral direction, where generally force values were up to 390% and 530% (p<.001) larger during the cross-slope conditions compared to level for walking and running, respectively. The maximum force peak during running occurred earlier at IRU compared to the other conditions (p≤.031). For the normal axis a significant difference was observed in the first maximum force peak during walking (p=.049). The findings of this study showed that compared to level surfaces, functional adaptations are required to maintain forward progression and dynamic stability in stance during cross-slope walking and running.

Inter-segment foot kinematics during cross-slope running.

Cross-slopes are a common terrain characteristic, however there is no biomechanical knowledge of the intra-foot adaptations required for running on these surface inclinations. The purpose of this study was to evaluate the kinematic changes induced within the foot while running on a transversely inclined surface. A three-segment foot model distinguishing between the hindfoot, forefoot, and hallux was used for this purpose. Nine healthy experienced male runners volunteered to perform level (0°) and cross-slope (10°) running trials barefoot at a moderate speed. Multivariate analysis of variance (MANOVA) for repeated measures was used to analyze the kinematics of the hindfoot with respect to tibia (HF/TB), forefoot with respect to hindfoot (FF/HF), and hallux with respect to forefoot (HX/FF) during level running (LR), incline running up-slope (IRU), and incline running down-slope (IRD) conditions. In the sagittal plane, the FF/HF angle showed greater dorsiflexion at peak vertical force production (MaxFz) in IRD compared to LR (p=0.042). The HX/FF was significantly more extended during IRU than LR at foot strike (p=0.027). More importantly, frontal plane asymmetries were also found. HF/TB angles revealed greater inversion at foot strike followed by greater eversion at MaxFz for IRU compared to IRD (p=0.042 and p=0.018, respectively). For the FF/HF angle, maximum eversion was greater during IRD than LR (p=0.035). Data suggests that running on cross-slopes can induce substantial intra-foot kinematic adaptations, whether this represents a risk of injury to both recreational and professional runners remains to be determined.