A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics

Functional Data Analysis: An Introduction and Recent Developments

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

Interlaboratory Study Toward Combining Gait Kinematics Data Sets of Long-Distance Runners

The limited sample size in gait studies has hampered progress in the field. This challenge could be addressed through multicenter studies, thereby leveraging data sets from different laboratories. This study compared 3-dimensional lower-extremity running kinematics between the Biomechanics and Motor Control Laboratory, Federal University of ABC (Brazil), and the Running Injury Clinic, University of Calgary (Canada). Three-dimensional lower-extremity kinematics from 23 male runners were collected from each laboratory using comparable instrumentation and experimental procedures. The 3-dimensional hip, knee, and ankle angles were compared within and between centers using root-mean-square deviation. Two-sample t tests Statistical Parametric Mapping tested the hypothesis that the data from both laboratories were not different. The sagittal plane hip, knee, and ankle angles were similar between laboratories, while notable differences were observed for frontal (hip and ankle) and transverse (hip and knee) plane angles. The average interlaboratory root-mean-square deviation (2.6°) was lower than the intralaboratory root-mean-square deviation (Biomechanics and Motor Control = 4.8°, Running Injury Clinic = 5.6°), with the ankle transverse angle displaying the smallest, and the knee transverse angle displaying the largest variability. This study demonstrates the potential of combining gait kinematics data from different laboratories to increase sample size, but frontal and transverse plane data should be considered with caution.

An approximate solution of the SLIP model under the regime of linear angular dynamics during stance and the stability of symmetric periodic running gaits

Terrestrial locomotion is a complex phenomenon that is often linked to the survival of an individual and of an animal species. Mathematical models seek to express in quantitative terms how animals move, but this is challenging because the ways in which the nervous and musculoskeletal systems interact to produce body movement is not completely understood. Models with many variables tend to lack biological interpretability and describe the motion of an animal with too many independent degrees of freedom. Instead, reductionist models aim to describe the essential features of a gait with the smallest number of variables, often concentrating on the center of mass dynamics. In particular, spring-mass models have been successful in extracting and describing important characteristics of running. In this paper, we consider the spring loaded inverted pendulum model under the regime of constant angular velocity, small compression, and small angle swept during stance. We provide conditions for the asymptotic stability of periodic trajectories for the full range of parameters. The hypothesis of linear angular dynamics during stance is successfully tested on publicly available human data of individuals running on a treadmill at different velocities. Our analysis highlights a novel bifurcation phenomenon for varying Froude number: there are periodic trajectories of the spring loaded inverted pendulum model that are stable only in a restricted range of Froude numbers, while they become unstable for smaller or larger Froude numbers.

Validity of an Inertial Measurement Unit System to Measure Lower Limb Kinematics at Point of Contact during Incremental High-Speed Running

There is limited validation for portable methods in evaluating high-speed running biomechanics, with inertial measurement unit (IMU) systems commonly used as wearables for this purpose. This study aimed to evaluate the validity of an IMU system in high-speed running compared to a 3D motion analysis system (MAS). One runner performed incremental treadmill running, from 12 to 18 km/h, on two separate days. Sagittal angles for the shank, knee, hip and pelvis were measured simultaneously with three IMUs and the MAS at the point of contact (POC), the timing when the foot initially hits the ground, as identified by IMU system acceleration, and compared to the POC identified via force plate. Agreement between the systems was evaluated using intra-class correlation coefficients, Pearson's r, Bland-Altman limits of agreements, root mean square error and paired t-tests. The IMU system reliably determined POC (which subsequently was used to calculate stride time) and measured hip flexion angle and anterior pelvic tilt accurately and consistently at POC. However, it displayed inaccuracy and inconsistency in measuring knee flexion and shank angles at POC. This information provides confidence that a portable IMU system can aid in establishing baseline running biomechanics for performance optimisation, and/or inform injury prevention programs.

Comparison of ground reaction forces as running speed increases between male and female runners

Introduction: The biomechanics associated with human running are affected by gender and speed. Knowledge regarding ground reaction force (GRF) at various running speeds is pivotal for the prevention of injuries related to running. This study aimed to investigate the gait pattern differences between males and females while running at different speeds, and to verify the relationship between GRFs and running speed among both males and females. Methods: GRF data were collected from forty-eight participants (thirty male runners and eighteen female runners) while running on an overground runway at seven discrete speeds: 10, 11, 12, 13, 14, 15 and 16 km/h. Results: The ANOVA results showed that running speed had a significant effect (p < 0.05) on GRFs, propulsive and vertical forces increased with increasing speed. An independent t-test also showed significant differences (p < 0.05) in vertical and anterior-posterior GRFs at all running speeds, specifically, female runners demonstrated higher propulsive and vertical forces than males during the late stance phase of running. Pearson correlation and stepwise multiple linear regression showed significant correlations between running speed and the GRF variables. Discussion: These findings suggest that female runners require more effort to keep the same speed as male runners. This study may provide valuable insights into the underlying biomechanical factors of the movement patterns at GRFs during running.

Clustering analysis across different speeds reveals two distinct running techniques with no differences in running economy

Establishing the links between running technique and economy remains elusive due to high inter-individual variability. Clustering runners by technique may enable tailored training recommendations, yet it is unclear if different techniques are equally economical and whether clusters are speed-dependent. This study aimed to identify clusters of runners based on technique and to compare cluster kinematics and running economy. Additionally, we examined the agreement of clustering partitions of the same runners at different speeds. Trunk and lower-body kinematics were captured from 84 trained runners at different speeds on a treadmill. We used Principal Component Analysis for dimensionality reduction and agglomerative hierarchical clustering to identify groups of runners with a similar technique, and we evaluated cluster agreement across speeds. Clustering runners at different speeds independently produced different partitions, suggesting single speed clustering can fail to capture the full speed profile of a runner. The two clusters identified using data from the whole range of speeds showed differences in pelvis tilt and duty factor. In agreement with self-optimisation theories, there were no differences in running economy, and no differences in participants' characteristics between clusters. Considering inter-individual technique variability may enhance the efficacy of training designs as opposed to 'one size fits all' approaches.

Effect of Running Speed on Knee Biomechanics in Collegiate Athletes Following Anterior Cruciate Ligament Reconstruction

INTRODUCTION

Athletes after anterior cruciate ligament reconstruction (ACLR) demonstrate altered surgical knee running kinematics and kinetics compared with the nonsurgical limb and healthy controls. The effect of running speed on biomechanics has not been formally assessed in athletes post-ACLR. The purpose of this study was to characterize how knee biomechanics change with running speed between 3.5-7 (EARLY) and 8-13 (LATE) months post-ACLR.

METHODS

Fifty-five Division I collegiate athletes post-ACLR completed running analyses (EARLY: n = 40, LATE: n = 41, both: n = 26) at 2.68, 2.95, 3.35, 3.80, and 4.47 m·s -1 . Linear mixed-effects models assessed the influence of limb, speed, time post-ACLR, and their interactions on knee kinematics and kinetics.

RESULTS

A significant limb-speed interaction was detected for peak knee flexion, knee flexion excursion, and rate of knee extensor moment ( P < 0.02), controlling for time. From 3.35 to 4.47 m·s -1 , knee flexion excursion decreased by -2.3° (95% confidence interval, -3.6 to -1.0) in the nonsurgical limb and -1.0° (95% confidence interval, -2.3 to -0.3) in the surgical limb. Peak vertical ground reaction force, peak knee extensor moment, and knee negative work increased similarly with speed for both limbs ( P < 0.002). A significant limb-time interaction was detected for all variables ( P < 0.001). Accounting for running speed, improvements in all surgical limb biomechanics were observed from EARLY to LATE ( P < 0.001), except for knee flexion at initial contact ( P = 0.12), but between-limb differences remained ( P < 0.001).

CONCLUSIONS

Surgical and nonsurgical knee biomechanics increase similarly with speed in collegiate athletes at EARLY and LATE, with the exception of peak knee flexion, knee flexion excursion, and rate of knee extensor moment. Surgical knee biomechanics improved from EARLY and LATE, but significant between-limb differences persisted.

Differences between lower extremity joint running kinetics captured by marker-based and markerless systems were speed dependent

BACKGROUND

The development of computer vision technology has enabled the use of markerless movement tracking for biomechanical analysis. Recent research has reported the feasibility of markerless systems in motion analysis but has yet to fully explore their utility for capturing faster movements, such as running. Applied studies using markerless systems in clinical and sports settings are still lacking. Thus, the present study compared running biomechanics estimated by marker-based and markerless systems. Given running speed not only affects sports performance but is also associated with clinical injury prevention, diagnosis, and rehabilitation, we aimed to investigate the effects of speed on the comparison of estimated lower extremity joint moments and powers between markerless and marker-based technologies during treadmill running as a concurrent validating study.

METHODS

Kinematic data from marker-based/markerless technologies were collected, along with ground reaction force data, from 16 young adults running on an instrumented treadmill at 3 speeds: 2.24 m/s, 2.91 m/s, and 3.58 m/s (5.0 miles/h, 6.5 miles/h, and 8.0 miles/h). Sagittal plane moments and powers of the hip, knee, and ankle were calculated by inverse dynamic methods. Time series analysis and statistical parametric mapping were used to determine system differences.

RESULTS

Compared to the marker-based system, the markerless system estimated increased lower extremity joint kinetics with faster speed during the swing phase in most cases.

CONCLUSION

Despite the promising application of markerless technology in clinical settings, systematic markerless overestimation requires focused attention. Based on segment pose estimations, the centers of mass estimated by markerless technologies were farther away from the relevant distal joint centers, which led to greater joint moments and powers estimates by markerless vs. marker-based systems. The differences were amplified by running speed.

Synergic control of the minimum toe clearance in young and older adults during foot swing on treadmill walking in different speeds

BACKGROUND

The vertical toe position at minimum toe clearance (MTC) in the swing phase is critical for walking safety. Consequently, the joints involved should be strictly controlled and coordinated to stabilize the foot at MTC. The uncontrolled manifold (UCM) hypothesis framework has been used to determine the existence of synergies that stabilize relevant performance variables during walking. However, no study investigated the presence of a multi-joint synergy stabilizing the foot position at MTC and the effects of age and walking speed on this synergy.

RESEARCH QUESTIONS

Is there a multi-joint synergy stabilizing MTC during treadmill walking? Does it depend on the persons' age and walking speed?

METHODS

Kinematic data from 23 young and 15 older adults were analyzed using the UCM approach. The participants walked on a treadmill at three speeds: slow, self-selected, and fast. The sagittal and frontal joint angles from the swing and stance legs and pelvis obliquity were used as motor elements and the vertical toe position at MTC was the performance variable. The variances in the joint space that affected (VORT, 'bad' variance) and did not affect (VUCM, 'good' variance) the toe position at MTC and the synergy index (ΔV) were computed.

RESULTS

The ΔV>0 was revealed for all subjects. Walking speed did not affect ΔV in older adults, whereas ΔV reduced with speed in young adults. ΔV was higher for older than for young adults at self-selected and fast speeds, owing to a lower VORT in the older group.

SIGNIFICANCE

The vertical toe position at MTC was stabilized by a strong multi-joint synergy. In older adults, this synergy was stronger, as they were better at limiting VORT than young adults. Reduced VORT in older adults could be caused by more constrained walking, which may be associated with anxiety due to walking on a treadmill.

Alternative Models for Pelvic Marker Occlusion in Cycling

Bike fitting aims to optimize riders' positions to improve their performance and reduce the risk of injury. To calculate joint angles, the location of the joint centers of the lower limbs needs to be identified. However, one of the greatest difficulties is the location of the hip joint center due to the frequent occlusion of the anterior superior iliac spine markers. Therefore, the objective of this study was to validate a biomechanical model adapted to cycling (modified pelvic model, MPM), based on the traditional pelvic model (TPM) with an additional lateral technical marker placed on the iliac crests. MPM was also compared with a widely used model in cycling, trochanter model (TM). Thirty-one recreational cyclists pedaled on a roller bike while the movement was captured with a 7-camera VICON system. The position of the hip joint center and knee angle were calculated and compared with the TPM continuously (along 10 pedaling cycles) and discreetly at 90° and 180° crank positions. No significant differences were found in the position of the hip joint center or in the knee flexion/extension angle between the TPM and the MPM. However, there are differences between TPM and TM (variations between 4.1° and 6.9° in favor of the TM at 90° and 180°; P < .001). Bland-Altman graphs comparing the models show an average difference or bias close to 0° (limits of agreement [0.2 to -8.5]) between TPM and MPM in both lower limbs and a mean difference of between -4° and -7° (limits of agreement [-0.6 to -13.2]) when comparing TPM and TM. Given the results, the new cycling pelvic model has proven to be valid compared with the TPM when performing bike fitting studies, with the advantage that the occluded markers are avoided. Despite its simplicity, the TM presents measurement errors that may be relevant when making diagnoses, which makes its usefulness questionable.

Modeling Running via Optimal Control for Shoe Design

Shoe manufacturing technology is advancing faster than new shoe designs can viably be evaluated in human subject trials. To aid in the design process, this paper presents a model for estimating how new shoe properties will affect runner performance. This model assumes runners choose their gaits to optimize an intrinsic, unknown objective function. To learn this objective function, a simple two-dimensional mechanical model of runners was used to predict their gaits under different objectives, and the resulting gaits were compared to data from real running trials. The most realistic model gaits, i.e., the ones that best matched the data, were obtained when the model runners minimized the impulse they experience from the ground as well as the mechanical work done by their leg muscles. Using this objective function, the gait and thus performance of running under different shoe conditions can be predicted. The simple model is sufficiently sensitive to predict the difference in performance of shoes with disruptive designs but cannot distinguish between existing shoes whose properties are fairly similar. This model therefore is a viable tool for coarse-grain exploration of the design space and identifying promising behaviors of truly novel shoe materials and designs.

Continuous Locomotion Mode and Task Identification for an Assistive Exoskeleton Based on Neuromuscular-Mechanical Fusion

Human walking parameters exhibit significant variability depending on the terrain, speed, and load. Assistive exoskeletons currently focus on the recognition of locomotion terrain, ignoring the identification of locomotion tasks, which are also essential for control strategies. The aim of this study was to develop an interface for locomotion mode and task identification based on a neuromuscular-mechanical fusion algorithm. The modes of level and incline and tasks of speed and load were explored, and seven able-bodied participants were recruited. A continuous stream of assistive decisions supporting timely exoskeleton control was achieved according to the classification of locomotion. We investigated the optimal algorithm, feature set, window increment, window length, and robustness for precise identification and synchronization between exoskeleton assistive force and human limb movements (human-machine collaboration). The best recognition results were obtained when using a support vector machine, a root mean square/waveform length/acceleration feature set, a window length of 170, and a window increment of 20. The average identification accuracy reached 98.7% ± 1.3%. These results suggest that the surface electromyography-acceleration can be effectively used for locomotion mode and task identification. This study contributes to the development of locomotion mode and task recognition as well as exoskeleton control for seamless transitions.

Neuromechanical stabilisation of the centre of mass during running

BACKGROUND

Stabilisation of the centre of mass (COM) trajectory is thought to be important during running. There is emerging evidence of the importance of leg length and angle regulation during running, which could contribute to stability in the COM trajectory The present study aimed to understand if leg length and angle stabilises the vertical and anterior-posterior (AP) COM displacements, and if the stability alters with running speeds.

METHODS

Data for this study came from an open-source treadmill running dataset (n = 28). Leg length (m) was calculated by taking the resultant distance of the two-dimensional sagittal plane leg vector (from pelvis segment to centre of pressure). Leg angle was defined by the angle subtended between the leg vector and the horizontal surface. Leg length and angle were scaled to a standard deviation of one. Uncontrolled manifold analysis (UCM) was used to provide an index of motor abundance (IMA) in the stabilisation of the vertical and AP COM displacement.

RESULTS

IMAAP and IMAvertical were largely destabilising and always stabilising, respectively. As speed increased, the peak destabilising effect on IMAAP increased from -0.66(0.18) at 2.5 m/s to -1.12(0.18) at 4.5 m/s, and the peak stabilising effect on IMAvertical increased from 0.69 (0.19) at 2.5 m/s to 1.18 (0.18) at 4.5 m/s.

CONCLUSION

Two simple parameters from a simple spring-mass model, leg length and angle, can explain the control behind running. The variability in leg length and angle helped stabilise the vertical COM, whilst maintaining constant running speed may rely more on inter-limb variation to adjust the horizontal COM accelerations.

A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities

Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task. To capture a more holistic picture of lower limb movements in everyday life, we collected data from 12 participants performing 20 non-cyclic activities (e.g. sit-to-stand, jumping, squatting, lunging, cutting) as well as 11 cyclic activities (e.g. walking, running) while kinematics (motion capture and IMUs), kinetics (force plates), and electromyography (EMG) were collected. This dataset provides normative biomechanics for a highly diverse range of activities and common tasks from a consistent set of participants and sensors.

Adolescent and young adult hip and knee strength profiles relate to running gait biomechanics

OBJECTIVES

Compare and assess relationships between strength and running biomechanics among healthy adolescents and young adult males and females.

DESIGN

Retrospective cohort.

SETTING

Clinic.

PARTICIPANTS

802 healthy participants (570 F, 232 M; 16.6 ± 2.3 years).

MAIN OUTCOME MEASURES

Mass-normalized knee flexor and extensor strength, hip adductor and abductor strength, hamstrings-to-quadriceps (H:Q), and abductor-to-adductor (Abd:Add) ratios were obtained using hand-held dynamometry. Mass-normalized peak vertical ground reaction force (vGRF), %stance, cadence, and stride length were obtained using an instrumented treadmill. Multivariate analyses of variance were used to compare strength and biomechanics across ages and sexes. Linear regressions were used to assess the relationships between strength and biomechanics, accounting for speed, age, and sex. Independent t-tests were used to compare strength between strength ratio profiles.

RESULTS

Strength and running biomechanics significantly differed between sexes (p-range: <0.001-0.05) and age groups (p-range: <0.001-0.02). Strength and strength ratios were significantly associated with increased cadence (p-range:0.001-0.04) and stride lengths (p-range:0.004-0.03), and decreased vGRF (p < 0.001). Lower H:Q ratios had significantly lower strength measures (p < 0.001). Higher Abd:Add ratios had significantly increased abductor strength (p < 0.001).

CONCLUSIONS

Strength and running biomechanics differed by sexes and ages. Hip and knee strength and strength ratios were related to select spatiotemporal and kinetic biomechanical features.

Lower-limb joint kinetics in jump rope skills performed by competitive athletes

The purpose of this study was to characterise lower-limb joint kinetics during consecutive double unders and speed step sprints performed by competitive jump rope athletes, and to compare these measurements to running. Sixteen adolescent competitive jump rope athletes performed consecutive double under, speed step, and running trials while motion capture and ground reaction force data were collected. Lower-limb joint moments, power, and work were calculated using an inverse dynamics approach and discrete measurements were compared between skills. Peak ground reaction forces were similar between movements; however, knee and hip joint kinetics were distributed differently between double unders and speed step. In general, double unders were characterised by an increased reliance on knee joint kinetics, while speed step was characterised by an increased reliance on hip joint kinetics. Peak ankle moments were 9-20% greater in speed step when compared to double unders and running (p ≤ 0.050), and peak negative ankle power was 39-114% greater in double unders and speed step when compared to running (p ≤ 0.002). These findings may have important implications for injury risk and load management in jump rope athletes or other individuals that incorporate jump rope into their training programs.

On the variability and dependence of human leg stiffness across strides during running and some consequences for the analysis of locomotion data

Typically, animal locomotion studies involve consecutive strides, which are frequently assumed to be independent with parameters that do not vary across strides. This assumption is often not tested. However, failing in particular to account for dependence across strides may cause an incorrect estimate of the uncertainty of the measurements and thereby lead to either missing (overestimating variance) or over-evaluating (underestimating variance) biological signals. In turn, this impacts replicability of the results because variability is accounted for differently across experiments. In this paper, we analyse the changes of a couple of measures of human leg stiffness across strides during running experiments, using a publicly available dataset. A major finding of this analysis is that the time series of these measurements of stiffness show autocorrelation even at large lags and so there is dependence between individual strides, even when separated by many intervening strides. Our results question the practice in biomechanics research of using each stride as an independent observation or of sub-selecting strides at small lags. Following the outcome of our analysis, we strongly recommend caution in doing so without first confirming the independence of the measurements across strides and without confirming that sub-selection does not produce spurious results.

Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition

Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects' thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements.

Musculotendon Parameters in Lower Limb Models: Simplifications, Uncertainties, and Muscle Force Estimation Sensitivity

Musculotendon parameters are key factors in the Hill-type muscle contraction dynamics, determining the muscle force estimation accuracy of a musculoskeletal model. Their values are mostly derived from muscle architecture datasets, whose emergence has been a major impetus for model development. However, it is often not clear if such parameter update indeed improves simulation accuracy. Our goal is to explain to model users how these parameters are derived and how accurate they are, as well as to what extent errors in parameter values might influence force estimation. We examine in detail the derivation of musculotendon parameters in six muscle architecture datasets and four prominent OpenSim models of the lower limb, and then identify simplifications which could add uncertainties to the derived parameter values. Finally, we analyze the sensitivity of muscle force estimation to these parameters both numerically and analytically. Nine typical simplifications in parameter derivation are identified. Partial derivatives of the Hill-type contraction dynamics are derived. Tendon slack length is determined as the musculotendon parameter that muscle force estimation is most sensitive to, whereas pennation angle is the least impactful. Anatomical measurements alone are not enough to calibrate musculotendon parameters, and the improvement on muscle force estimation accuracy will be limited if the source muscle architecture datasets are the only main update. Model users may check if a dataset or model is free of concerning factors for their research or application requirements. The derived partial derivatives may be used as the gradient for musculotendon parameter calibration. For model development, we demonstrate that it is more promising to focus on other model parameters or components and seek alternative strategies to further increase simulation accuracy.

Validity of IMU measurements on running kinematics in non-rearfoot strike runners across different speeds

This study aims to determine the validity of the lower extremity joint kinematics measured by inertial measurement units (IMUs) in non-rearfoot strike pattern (NRFS) runners across different speeds. Fifteen NRFS runners completed three 2-min running tests on a treadmill in random order at 8, 10 and 12 km/h, whilst data were synchronously collected using the IMU system and an optical motion capture system. Before the offset was corrected, the validity of the knee angle waveform was higher than that of the hip and ankle; after the offset was corrected, the validity increased in all three joints. The correlation between the touchdown angles in the sagittal plane measured by the two systems was relatively high after the offset was corrected. The running speed influenced the offset-corrected measurements, with higher error values at higher speeds. The IMU system was able to provide measurements of running kinematics in the sagittal plane of NRFS runners at different running speeds but was unable to reliably measure motion in the frontal and horizontal planes. Future research should analyse the 3D gait of NRFS runners under a larger range of speed conditions to provide evidentiary support for the use of IMUs in running analysis outside the laboratory.

Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment

Wearable sensors and machine learning algorithms are becoming a viable alternative for biomechanical analysis outside of the laboratory. The purpose of this work was to estimate gait events from inertial measurement units (IMUs) and utilize machine learning for the estimation of ground reaction force (GRF) waveforms. Sixteen healthy runners were recruited for this study, with varied running experience. Force sensing insoles were used to measure normal foot-shoe forces, providing a proxy for vertical GRF and a standard for the identification of gait events. Three IMUs were mounted on each participant, two bilaterally on the dorsal aspect of each foot and one clipped to the back of each participant's waistband, approximating their sacrum. Participants also wore a GPS watch to record elevation and velocity. A Bidirectional Long Short Term Memory Network (BD-LSTM) was used to estimate GRF waveforms from inertial waveforms. Gait event estimation from both IMU data and machine learning algorithms led to accurate estimations of contact time. The GRF magnitudes were generally underestimated by the machine learning algorithm when presented with data from a novel participant, especially at faster running speeds. This work demonstrated that estimation of GRF waveforms is feasible across a range of running velocities and at different grades in an uncontrolled environment.

A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision

In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.2 total hours of movement data from a college student population with an approximately equal split of males to females. In addition, for one of the activities, we captured the egocentric field of view and gaze of the subjects using an eye tracker. Finally, we provide some examples of applications using the dataset and discuss how it might open possibilities for new studies in human gait analysis.

Tools and Modalities for Postural Ergonomics Research in Surgery and Neurosurgery

Work-related musculoskeletal disorders (WMSDs) are common amongst neurosurgeons and can affect a surgeon's ability to operate. Performing surgical ergonomics research is important to minimize the prevalence and effect of WMSDs on the surgeons. The aim of this review is to highlight some of the most important objective and subjective tools available for surgical ergonomics research. Subjective tools can be divided into three categories: (1) questionnaires (either validated or non-validated) filled out by the participants, (2) survey assessments/standardized scoring systems filled out by the researchers, and (3) video analysis. Subjective tools have the drawbacks of recall bias and intra-rater and inter-rater variability. Some of the most important objective tools available are surface electromyography, force plate/pressure sensors analysis, inertial measurement units (IMUs) and kinematics data capturing using reflective markers. Although these modalities do not have the drawbacks that hinder the use of subjective tools, using most of them in the real-life operating theatre, with the exception of IMUs, is challenging. Conducting surgical ergonomics research is important to optimize the performance of neurosurgeons. The advancements towards wearable, wireless technologies will make it easier for surgeons to perform ergonomics research in the operating room.

Measurement error associated with gait cycle selection in treadmill running at various speeds

A common approach in the biomechanical analysis of running technique is to average data from several gait cycles to compute a 'representative mean.' However, the impact of the quantity and selection of gait cycles on biomechanical measures is not well understood. We examined the effects of gait cycle selection on kinematic data by: (i) comparing representative means calculated from varying numbers of gait cycles to 'global' means from the entire capture period; and (ii) comparing representative means from varying numbers of gait cycles sampled from different parts of the capture period. We used a public dataset (n = 28) of lower limb kinematics captured during a 30-second period of treadmill running at three speeds (2.5 m s^-1, 3.5 m s^-1 and 4.5 m s^-1). 'Ground truth' values were determined by averaging data across all collected strides and compared to representative means calculated from random samples (1,000 samples) of n (range = 5-30) consecutive gait cycles. We also compared representative means calculated from n (range = 5-15) consecutive gait cycles randomly sampled (1,000 samples) from within the same data capture period. The mean, variance and range of the absolute error of the representative mean compared to the 'ground truth' mean progressively reduced across all speeds as the number of gait cycles used increased. Similar magnitudes of 'error' were observed between the 2.5 m s^-1 and 3.5 m s^-1 speeds at comparable gait cycle numbers -where the maximum errors were < 1.5 degrees even with a small number of gait cycles (i.e., 5-10). At the 4.5 m s^-1 speed, maximum errors typically exceeded 2-4 degrees when a lower number of gait cycles were used. Subsequently, a higher number of gait cycles (i.e., 25-30) was required to achieve low errors (i.e., 1-2 degrees) at the 4.5 m s^-1 speed. The mean, variance and range of absolute error of representative means calculated from different parts of the capture period was consistent irrespective of the number of gait cycles used. The error between representative means was low (i.e., < 1.5 degrees) and consistent across the different number of gait cycles at the 2.5 m s^-1 and 3.5 m s^-1 speeds, and consistent but larger (i.e., up to 2-4 degrees) at the 4.5 m s^-1 speed. Our findings suggest that selecting as many gait cycles as possible from a treadmill running bout will minimise potential 'error.' Analysing a small sample (i.e., 5-10 cycles) will typically result in minimal 'error' (i.e., < 2 degrees), particularly at lower speeds (i.e., 2.5 m s^-1 and 3.5 m s^-1). Researchers and clinicians should consider the balance between practicalities of collecting and analysing a smaller number of gait cycles against the potential 'error' when determining their methodological approach. Irrespective of the number of gait cycles used, we recommend that the potential 'error' introduced by the choice of gait cycle number be considered when interpreting the magnitude of effects in treadmill-based running studies.

Actuation Strategies for a Wearable Cable-Driven Exosuit Based on Synergies in Younger and Older Adults

Older adults (aged 55 years and above) have greater difficulty carrying out activities of daily living than younger adults (aged 25−55 years). Although age-related changes in human gait kinetics are well documented in qualitative terms in the scientific literature, these differences may be quantified and analyzed using the analysis of motor control strategies through kinetic synergies. The gaits of two groups of people (older and younger adults), each with ten members, were analyzed on a treadmill at a constant controlled speed and their gait kinetics were recorded. The decomposition of the kinetics into synergies was applied to the joint torques at the hip, knee, and ankle joints. Principal components determined the similarity of the kinetic torques in the three joints analyzed and the effect of the walking speed on the coordination pattern. A total of three principal components were required to describe enough information with minimal loss. The results suggest that the older group showed a change in coordination strategy compared to that of the younger group. The main changes were related to the ankle and hip torques, both showing significant differences (p-value <0.05) between the two groups. The findings suggest that the differences between the gait patterns of the two groups were closely related to a reduction in ankle torque and an increase in hip torque. This change in gait pattern may affect the rehabilitation strategy used when designing general-purpose rehabilitation devices or rehabilitation/training programs for the elderly.

Comparison of Lower Extremity Joint Moment and Power Estimated by Markerless and Marker-Based Systems during Treadmill Running

Background: Markerless (ML) motion capture systems have recently become available for biomechanics applications. Evidence has indicated the potential feasibility of using an ML system to analyze lower extremity kinematics. However, no research has examined ML systems’ estimation of the lower extremity joint moments and powers. This study aimed to compare lower extremity joint moments and powers estimated by marker-based (MB) and ML motion capture systems. Methods: Sixteen volunteers ran on a treadmill for 120 s at 3.58 m/s. The kinematic data were simultaneously recorded by 8 infrared cameras and 8 high-resolution video cameras. The force data were recorded via an instrumented treadmill. Results: Greater peak magnitudes for hip extension and flexion moments, knee flexion moment, and ankle plantarflexion moment, along with their joint powers, were observed in the ML system compared to an MB system (p < 0.0001). For example, greater hip extension (MB: 1.42 ± 0.29 vs. ML: 2.27 ± 0.45) and knee flexion (MB: −0.74 vs. ML: −1.17 nm/kg) moments were observed in the late swing phase. Additionally, the ML system’s estimations resulted in significantly smaller peak magnitudes for knee extension moment, along with the knee production power (p < 0.0001). Conclusions: These observations indicate that inconsistent estimates of joint center position and segment center of mass between the two systems may cause differences in the lower extremity joint moments and powers. However, with the progression of pose estimation in the markerless system, future applications can be promising.

Design and Backdrivability Modeling of a Portable High Torque Robotic Knee Prosthesis With Intrinsic Compliance For Agile Activities

High-performance prostheses are crucial to enable versatile activities like walking, squatting, and running for lower extremity amputees. State-of-the-art prostheses are either not powerful enough to support demanding activities or have low compliance (low backdrivability) due to the use of high speed ratio transmission. Besides speed ratio, gearbox design is also crucial to the compliance of wearable robots, but its role is typically ignored in the design process. This paper proposed an analytical backdrive torque model that accurately estimate the backdrive torque from both motor and transmission to inform the robot design. Following this model, this paper also proposed methods for gear transmission design to improve compliance by reducing inertia of the knee prosthesis. We developed a knee prosthesis using a high torque actuator (built-in 9:1 planetary gear) with a customized 4:1 low-inertia planetary gearbox. Benchtop experiments show the backdrive torque model is accurate and proposed prosthesis can produce 200 Nm high peak torque (shield temperature <60°C), high compliance (2.6 Nm backdrive torque), and high control accuracy (2.7/8.1/1.7 Nm RMS tracking errors for 1.25 m/s walking, 2 m/s running, and 0.25 Hz squatting, that are 5.4%/4.1%/1.4% of desired peak torques). Three able-bodied subject experiments showed our prosthesis could support agile and high-demanding activities.

Integration of Inertial Sensors in a Lower Limb Robotic Exoskeleton

Motion assistance exoskeletons are designed to support the joint movement of people who perform repetitive tasks that cause damage to their health. To guarantee motion accompaniment, the integration between sensors and actuators should ensure a near-zero delay between the signal acquisition and the actuator response. This study presents the integration of a platform based on Imocap-GIS inertial sensors, with a motion assistance exoskeleton that generates joint movement by means of Maxon motors and Harmonic drive reducers, where a near zero-lag is required for the gait accompaniment to be correct. The Imocap-GIS sensors acquire positional data from the user's lower limbs and send the information through the UDP protocol to the CompactRio system, which constitutes a high-performance controller. These data are processed by the card and subsequently a control signal is sent to the motors that move the exoskeleton joints. Simulations of the proposed controller performance were conducted. The experimental results show that the motion accompaniment exhibits a delay of between 20 and 30 ms, and consequently, it may be stated that the integration between the exoskeleton and the sensors achieves a high efficiency. In this work, the integration between inertial sensors and an exoskeleton prototype has been proposed, where it is evident that the integration met the initial objective. In addition, the integration between the exoskeleton and IMOCAP is among the highest efficiency ranges of similar systems that are currently being developed, and the response lag that was obtained could be improved by means of the incorporation of complementary systems.

Force and Torque Characterization in the Actuation of a Walking-Assistance, Cable-Driven Exosuit

Soft exosuits stand out when it comes to the development of walking-assistance devices thanks to both their higher degree of wearability, lower weight, and price compared to the bulkier equivalent rigid exoskeletons. In cable-driven exosuits, the acting force is driven by cables from the actuation system to the anchor points; thus, the user’s movement is not restricted by a rigid structure. In this paper, a 3D inverse dynamics model is proposed and integrated with a model for a cable-driven actuation to predict the required motor torque and traction force in cables for a walking-assistance exosuit during gait. Joint torques are to be shared between the user and the exosuit for different design configurations, focusing on both hip and ankle assistance. The model is expected to guide the design of the exosuit regarding aspects such as the location of the anchor points, the cable system design, and the actuation units. An inverse dynamics analysis is performed using gait kinematic data from a public dataset to predict the cable forces and position of the exosuit during gait. The obtained joint reactions and cable forces are compared with those in the literature, and prove the model to be accurate and ready to be implemented in an exosuit control scheme. The results obtained in this study are similar to those found in the literature regarding the walking study itself as well as the forces under which cables operate during gait and the cable position cycle.

Multi-Output Sequential Deep Learning Model for Athlete Force Prediction on a Treadmill Using 3D Markers

Reliable and innovative methods for estimating forces are critical aspects of biomechanical sports research. Using them, athletes can improve their performance and technique and reduce the possibility of fractures and other injuries. For this purpose, throughout this project, we proceeded to research the use of video in biomechanics. To refine this method, we propose an RNN trained on a biomechanical dataset of regular runners that measures both kinematics and kinetics. The model will allow analyzing, extracting, and drawing conclusions about continuous variable predictions through the body. It marks different anatomical and reflective points (96 in total, 32 per dimension) that will allow the prediction of forces (N) in three dimensions (Fx, Fy, Fz), measured on a treadmill with a force plate at different velocities (2.5 m/s, 3.5 m/s, 4.5 m/s). In order to obtain the best model, a grid search of different parameters that combined various types of layers (Simple, GRU, LSTM), loss functions (MAE, MSE, MSLE), and sampling techniques (down-sampling, up-sampling) helped obtain the best performing model (LSTM, MSE, down-sampling) achieved an average coefficient of determination of 0.68, although when excluding Fz it reached 0.92.

Validation of Running Gait Event Detection Algorithms in a Semi-Uncontrolled Environment

The development of lightweight portable sensors and algorithms for the identification of gait events at steady-state running speeds can be translated into the real-world environment. However, the output of these algorithms needs to be validated. The purpose of this study was to validate the identification of running gait events using data from Inertial Measurement Units (IMUs) in a semi-uncontrolled environment. Fifteen healthy runners were recruited for this study, with varied running experience and age. Force-sensing insoles measured normal foot-shoe forces and provided a standard for identification of gait events. Three IMUs were mounted to the participant, two bilaterally on the dorsal aspect of the foot and one clipped to the back of each participant’s waistband, approximating their sacrum. The identification of gait events from the foot-mounted IMU was more accurate than from the sacral-mounted IMU. At running speeds <3.57 m s−1, the sacral-mounted IMU identified contact duration as well as the foot-mounted IMU. However, at speeds >3.57 m s−1, the sacral-mounted IMU overestimated foot contact duration. This study demonstrates that at controlled paces over level ground, we can identify gait events and measure contact time across a range of running skill levels.

Basis expansion approaches for functional analysis of variance with repeated measures

The methodological contribution in this paper is motivated by biomechanical studies where data characterizing human movement are waveform curves representing joint measures such as flexion angles, velocity, acceleration, and so on. In many cases the aim consists of detecting differences in gait patterns when several independent samples of subjects walk or run under different conditions (repeated measures). Classic kinematic studies often analyse discrete summaries of the sample curves discarding important information and providing biased results. As the sample data are obviously curves, a Functional Data Analysis approach is proposed to solve the problem of testing the equality of the mean curves of a functional variable observed on several independent groups under different treatments or time periods. A novel approach for Functional Analysis of Variance (FANOVA) for repeated measures that takes into account the complete curves is introduced. By assuming a basis expansion for each sample curve, two-way FANOVA problem is reduced to Multivariate ANOVA for the multivariate response of basis coefficients. Then, two different approaches for MANOVA with repeated measures are considered. Besides, an extensive simulation study is developed to check their performance. Finally, two applications with gait data are developed.

Characterizing the performance of human leg external force control

Our legs act as our primary contact with the surrounding environment, generating external forces that enable agile motion. To be agile, the nervous system has to control both the magnitude of the force that the feet apply to the ground and the point of application of this force. The purpose of this study was to characterize the performance of the healthy human neuromechanical system in controlling the force-magnitude and position of an externally applied force. To accomplish this, we built an apparatus that immobilized participants but allowed them to exert variable but controlled external forces with a single leg onto a ground embedded force plate. We provided real-time visual feedback of either the leg force-magnitude or force-position that participants were exerting against the force platform and instructed participants to best match their real-time signal to prescribed target step functions. We tested target step functions of a range of sizes and quantified the responsiveness and accuracy of the control. For the control of force-magnitude and for intermediate step sizes of 0.45 bodyweights, we found a bandwidth of 1.8 ± 0.5 Hz, a steady-state error of 2.6 ± 0.9%, and a steady-state variability of 2.7 ± 0.9%. We found similar control performance in terms of responsiveness and accuracy across step sizes and between force-magnitude and position control. Increases in responsiveness correlated with reductions in other measures of control performance, such as a greater magnitude of overshooting. We modelled the observed control performance and found that a second-order model was a good predictor of external leg force control. We discuss how benchmarking force control performance in young healthy humans aids in understanding differences in agility between humans, between humans and other animals, and between humans and engineered systems.

Descending 13 real world steps: A dataset and analysis of stair descent

BACKGROUND

Stair descent analysis has been typically limited to laboratory staircases of 4 or 5 steps. To date there has been no report of gait parameters during unconstrained stair descent outside of the laboratory, and few motion capture datasets are publicly available.

RESEARCH QUESTION

We aim to collect a dataset and perform gait analysis for stair descent outside of the laboratory. We aim to measure basic kinematic and kinetic gait parameters and foot placement behavior.

METHODS

We present a public stair descent dataset from 101 unimpaired participants aged 18-35 on an unconstrained 13-step staircase collected using wearable sensors. The dataset consists of kinematics (full-body joint angle and position), kinetics (plantar normal forces, acceleration), and foot placement for 30,609 steps.

RESULTS

We report the lower limb joint angle ranges (30° and 8° for hip flexion and extension, 85° and -11° for knee flexion and extension, and 31° and 28° for ankle dorsi- and plantar-flexion). The self-selected speed was 0.79 ± 0.16 m/s, with cycle duration of 0.97 ± 0.18 s. Mean foot overhang as a percentage of foot length was 17.07 ± 6.66 %, and we calculate that foot size explains only 6% of heel placement variation, but 79% of toe placement variation. We also find a minor but significant asymmetry between left and right maximum hip flexion angle, though all other measured parameters were symmetrical.

SIGNIFICANCE

This is the first quantitative observation of gait data from a large number (n = 101) of participants descending an unconstrained staircase outside of a laboratory. This study enables analysis of gait characteristics including self-selected walking speed and foot placement to better understand typical stair gait behavior. The dataset is a public resource for understanding typical stair descent.

The Iliotibial Band: A Complex Structure with Versatile Functions

The development of a pronounced iliotibial band (ITB) is an anatomically distinct evolution of humans. The mechanical behaviour of this “new” structure is still poorly understood and hotly debated in current literature. Iliotibial band syndrome (ITBS) is one of the leading causes of lateral knee pain injuries in runners. We currently lack a comprehensive understanding of the healthy behaviour of the ITB, and this is necessary prior to further investigating the aetiology of pathologies like ITBS. Therefore, the purpose of this narrative review was to collate the anatomical, biomechanical and clinical literature to understand how the mechanical function of the ITB is influenced by anatomical variation, posture and muscle activation. The complexity of understanding the mechanical function of the ITB is due, in part, to the presence of its two in-series muscles: gluteus maximus (GMAX) and tensor fascia latae (TFL). At present, we lack a fundamental understanding of how GMAX and TFL transmit force through the ITB and what mechanical role the ITB plays for movements like walking or running. While there is a range of proposed ITBS treatment strategies, robust evidence for effective treatments is still lacking. Interventions that directly target the running biomechanics suspected to increase either ITB strain or compression of lateral knee structures may have promise, but clinical randomised controlled trials are still required.

Deep Neural Networks for Human's Fall-risk Prediction using Force-Plate Time Series Signal

Early and accurate identification of the balance deficits could reduce falls, in particular for older adults, a prone population. Our work investigates deep neural networks' capacity to identify human balance patterns towards predicting fall-risk. Human balance ability can be characterized based on commonly-used balance metrics, such as those derived from the force-plate time series. We hypothesized that low, moderate, and high risk of falling can be characterized based on balance metrics, derived from the force-plate time series, in conjunction with deep learning algorithms. Further, we predicted that our proposed One-One-One Deep Neural Networks algorithm provides a considerable increase in performance compared to other algorithms. Here, an open source force-plate dataset, which quantified human balance from a wide demographic of human participants (163 females and males aged 18-86) for varied standing conditions (eyes-open firm surface, eyes-closed firm surface, eyes-open foam surface, eyes-closed foam surface) was used. Classification was based on one of the several indicators of fall-risk tied to the fear of falling: the clinically-used Falls Efficacy Scale (FES) assessment. For human fall-risk prediction, the deep learning architecture implemented comprised of: Recurrent Neural Network (RNN), Long-Short Time Memory (LSTM), One Dimensional Convolutional Neural Network (1D-CNN), and a proposed One-One-One Deep Neural Network. Results showed that our One-One-One Deep Neural Networks algorithm outperformed the other aforementioned algorithms and state-of-the-art models on the same dataset. With an accuracy, precision, and sensitivity of 99.9%, 100%, 100%, respectively at the 12th epoch, we found that our proposed One-One-One Deep Neural Network model is the most efficient neural network in predicting human's fall-risk (based on the FES measure) using the force-plate time series signal. This is a novel methodology for an accurate prediction of human risk of fall.

Running Biomechanics of Adolescents With Autism Spectrum Disorder

Research examining gait biomechanics of persons with autism spectrum disorder (ASD) has grown significantly in recent years and has demonstrated that persons with ASD walk at slower self-selected speeds and with shorter strides, wider step widths, and reduced lower extremity range of motion and moments compared to neurotypical controls. In contrast to walking, running has yet to be examined in persons with ASD. The purpose of this study was to examine lower extremity running biomechanics in adolescents (13-18-year-olds) with ASD and matched (age, sex, and body mass index (BMI)) neurotypical controls. Three-dimensional kinematics and ground reaction forces (GRFs) were recorded while participants ran at two matched speeds: self-selected speed of adolescents with ASD and at 3.0 m/s. Sagittal and frontal plane lower extremity biomechanics and vertical GRF waveforms were compared using two-way analyses of variances (ANOVAs) via statistical parametric mapping (SPM). Adolescents with ASD ran with reduced stride length at self-selected speed (0.29 m) and reduced vertical displacement (2.1 cm), loading-propulsion GRFs (by 14.5%), propulsion plantarflexion moments (18.5%), loading-propulsion hip abduction moments (44.4%), and loading knee abduction moments (69.4%) at both speeds. Running at 3.0 m/s increased sagittal plane hip and knee moments surrounding initial contact (both 10.4%) and frontal plane knee angles during midstance (2.9 deg) and propulsion (2.8 deg) compared to self-selected speeds. Reduced contributions from primarily the ankle plantarflexion but also knee abduction and hip abduction moments likely reduced the vertical GRF and displacement. As differences favored reduced loading, youth with ASD can safely be encouraged to engage in running as a physical activity.

Comparing shallow, deep, and transfer learning in predicting joint moments in running

Joint moments are commonly calculated in biomechanics research and provide an indirect measure of muscular behaviors and joint loads. However, joint moments cannot be easily quantified clinically or in the field, primarily due to challenges measuring ground reaction forces outside the laboratory. The present study aimed to compare the accuracy of three different machine learning (ML) techniques - functional regression [ ML_fregress ], a deep neural network (DNN) built from scratch [ ML_DNN ], and transfer learning [ ML_TL ], in predicting joint moments during running. Data for this study came from an open-source dataset and two studies on running with and without external loads. Three-dimensional (3D) joint moments of the hip, knee, and ankle, were derived using inverse dynamics. 3D joint angle, velocity, and acceleration of the three joints served as predictors for each of the three ML techniques. Prediction performance was generally the best using ML_DNN, and the worse using ML_fregress. Absolute predictive performance was the best for sagittal plane moments, which ranged from a RMSE of 0.16 Nm/kg at the ankle using ML_DNN, to a RMSE of 0.49Nm/kg at the knee using ML_fregress. ML_DNN resulted in the greatest improvement in relative prediction performance (relRMSE) by 20% compared to ML_fregress for the ankle adduction-abduction moment. DNN with or without transfer learning was superior in predicting joint moments using kinematic inputs compared to functional regression. Synergizing ML with kinematic inputs has the potential to solve the constraints of obtaining high fidelity biomechanics data normally only possible during laboratory studies.

A Pilot Study of Muscle Force between Normal Shoes and Bionic Shoes during Men Walking and Running Stance Phase Using Opensim

The original idea for bionic shoes (BSs) involves combining the function of unstable foot conditions and the structure of the human plantar. The purpose of this study was to investigate the differences between the normal shoes (NS) and the BS during the stance phases of walking and running. A total of 15 Chinese males from Ningbo University were recruited for this study (age: 24.3 ± 2.01 years; height: 176.25 ± 7.11 cm, body weight (BW): 75.75 ± 8.35 kg). The participants were asked to perform a walking and running task. Statistical parametric mapping (SPM) analysis was used to investigate any differences between NSs and BSs during the walking and running stance phases. The results demonstrated that there were significant differences found (21.23–28.24%, p = 0.040; 84.47–100%, p = 0.017) in hip extension and flexion between the NS and the BS during the walking stance phase. There were no significant differences found in ankle and moment during the running stance phase. Significant differences were found in the rectus femoris (5.29–6.21%; p = 0.047), tibialis anterior (14.37–16.40%; p = 0.038), and medial gastrocnemius (25.55–46.86%; p < 0.001) between the NS and the BS during the walking stance phase. Significant differences were found in rectus femoris (12.83–13.10%, p = 0.049; 15.89–80.19%, p < 0.001), tibialis anterior (15.85–18.31%, p = 0.039; 21.14–24.71%, p = 0.030), medial gastrocnemius (80.70–90.44%; p = 0.007), and lateral gastrocnemius (11.16–27.93%, p < 0.001; 62.20–65.63%, p = 0.032; 77.56–93.45%, p < 0.001) between the NS and the BS during the running stance phase. These findings indicate that BSs are more efficient for muscle control than unstable shoes and maybe suitable for rehabilitation training.

A simple computational method to estimate stance velocity in running

Running dynamical analyses typically approximate a runner's stance velocity as the average stride cycle velocity (the average running speed). That approximation necessarily overestimates stance velocity and biases subsequent results. Stance velocity is crucial in kinetic spring-mass analyses of running, where approximation of a runner's impact angle and calculation of leg stiffness require that input. Here, a new method is presented to approximate a runner's stance velocity via measurement of contact and flight times with the runner's average speed, leg length or height, and mass. This method accurately estimated the stance velocity of simulated spring-mass systems across typical running speeds of 3.5-5.5 m s-1 (r>0.99) and more accurately estimated the impact angle and leg stiffness. The method also accurately estimated the peak horizontal ground reaction force across speeds (r=0.82), but the bias magnitude increased with speed. Robustness of the new method was further tested for runners at 2.5, 3.5 and 4.5 m s-1, and the new method provided steeper impact angles than those from traditional estimates and correspondingly higher leg stiffnesses, analogous to the observations in the simulation models. Horizontal ground reaction force estimates were weakly correlated in braking and propulsion. They were improved by a corrective algorithm, but the intra- and inter-individual variation persisted. The directionality and magnitude of angle and stiffness estimates in the human runners were similar to simulations, suggesting the new method more accurately modeled runners' spring-mass characteristics by using an accurate approximation of stance velocity. The new method can improve traditional kinetic analyses of running where stance velocity recordings are not captured with kinematic recordings and extend opportunities for accurate field-based analyses with limited measurement sources.

The menisci are not shock absorbers: A biomechanical and comparative perspective

The lateral and medial menisci are fibrocartilaginous structures in the knee that play a crucial role in normal knee biomechanics. However, one commonly cited role of the menisci is that they function as "shock absorbers." Here we provide a critique of this notion, drawing upon a review of comparative anatomical and biomechanical data from humans and other tetrapods. We first review those commonly, and often exclusively, cited studies in support of a shock absorption function and show that evidence for a shock absorptive function is dubious. We then review the evolutionary and comparative evidence to show that (1) the human menisci are unremarkable in morphology compared with most other tetrapods, and (2) "shock" during locomotion is uncommon, with humans standing out as one of the only tetrapods that regularly experiences high levels of shock during locomotion. A shock-absorption function does not explain the origin of menisci, nor are human menisci specialized in any way that would explain a unique shock-absorbing function during human gait. Finally, we show that (3) the material properties of menisci are distinctly poorly suited for energy dissipation and that (4) estimations of meniscal energy absorption based on published data are negligible, both in their absolute amount and in comparison to other well-accepted structures which mitigate shock during locomotion. The menisci are evolutionarily ancient structures crucial for joint congruity, load distribution, and stress reduction, among a number of other functions. However, the menisci are not meaningful shock absorbers, neither in tetrapods broadly, nor in humans.

Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running

The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern. The purpose of this study was to develop FootNet, a novel kinematics and deep learning-based algorithm for the detection of step events in treadmill running. Five treadmill running datasets were gathered and processed to obtain segment and joint kinematics, and to identify the contact phase within each gait cycle using force data. The proposed algorithm is based on a long short-term memory recurrent neural network and takes the distal tibia anteroposterior velocity, ankle dorsiflexion/plantar flexion angle and the anteroposterior and vertical velocities of the foot centre of mass as input features to predict the contact phase within a given gait cycle. The chosen model architecture underwent 5-fold cross-validation and the final model was tested in a subset of participants from each dataset (30%). Non-parametric Bland-Altman analyses (bias and [95% limits of agreement]) and root mean squared error (RMSE) were used to compare FootNet against the force data step event detection method. The association between detection errors and running speed, foot-strike angle and incline were also investigated. FootNet outperformed previously published algorithms (foot-strike bias = 0 [–10, 7] ms, RMSE = 5 ms; toe-off bias = 0 [–10, 10] ms, RMSE = 6 ms; and contact time bias = 0 [–15, 15] ms, RMSE = 8 ms) and proved robust to different running speeds, foot-strike angles and inclines. We have made FootNet’s source code publicly available for step event detection in treadmill running when force data are not available.

Reducing the metabolic energy of walking and running using an unpowered hip exoskeleton

BACKGROUND

Walking and running are the most common means of locomotion in human daily life. People have made advances in developing separate exoskeletons to reduce the metabolic rate of walking or running. However, the combined requirements of overcoming the fundamental biomechanical differences between the two gaits and minimizing the metabolic penalty of the exoskeleton mass make it challenging to develop an exoskeleton that can reduce the metabolic energy during both gaits. Here we show that the metabolic energy of both walking and running can be reduced by regulating the metabolic energy of hip flexion during the common energy consumption period of the two gaits using an unpowered hip exoskeleton.

METHODS

We analyzed the metabolic rates, muscle activities and spatiotemporal parameters of 9 healthy subjects (mean ± s.t.d; 24.9 ± 3.7 years, 66.9 ± 8.7 kg, 1.76 ± 0.05 m) walking on a treadmill at a speed of 1.5 m s-1 and running at a speed of 2.5 m s-1 with different spring stiffnesses. After obtaining the optimal spring stiffness, we recruited the participants to walk and run with the assistance from a spring with optimal stiffness at different speeds to demonstrate the generality of the proposed approach.

RESULTS

We found that the common optimal exoskeleton spring stiffness for walking and running was 83 Nm Rad-1, corresponding to 7.2% ± 1.2% (mean ± s.e.m, paired t-test p < 0.01) and 6.8% ± 1.0% (p < 0.01) metabolic reductions compared to walking and running without exoskeleton. The metabolic energy within the tested speed range can be reduced with the assistance except for low-speed walking (1.0 m s-1). Participants showed different changes in muscle activities with the assistance of the proposed exoskeleton.

CONCLUSIONS

This paper first demonstrates that the metabolic cost of walking and running can be reduced using an unpowered hip exoskeleton to regulate the metabolic energy of hip flexion. The design method based on analyzing the common energy consumption characteristics between gaits may inspire future exoskeletons that assist multiple gaits. The results of different changes in muscle activities provide new insight into human response to the same assistive principle for different gaits (walking and running).

Changes in the Trunk and Lower Extremity Kinematics Due to Fatigue Can Predispose to Chronic Injuries in Cycling

Kinematic analysis of the cycling position is a determining factor in injury prevention and optimal performance. Fatigue caused by high volume training can alter the kinematics of the lower body and spinal structures, thus increasing the risk of chronic injury. However, very few studies have established relationships between fatigue and postural change, being these in 2D analysis or incremental intensity protocols. Therefore, this study aimed to perform a 3D kinematic analysis of pedaling technique in a stable power fatigue protocol 23 amateur cyclists (28.3 ± 8.4 years) participated in this study. For this purpose, 3D kinematics in hip, knee, ankle, and lumbar joints, and thorax and pelvis were collected at three separate times during the protocol. Kinematic differences at the beginning, middle, and end of the protocol were analyzed for all joints using one-dimensional statistical parametric mapping. Significant differences (p < 0.05) were found in all the joints studied, but not all of them occur in the same planes or the same phase of the cycle. Some of the changes produced, such as greater lumbar and thoracic flexion, greater thoracic and pelvic tilt, or greater hip adduction, could lead to chronic knee and lumbar injuries. Therefore, bike fitting protocols should be carried out in fatigue situations to detect risk factor situations.

Improving spring-mass parameter estimation in running using nonlinear regression methods

Runners are commonly modeled as spring-mass systems, but the traditional calculations of these models rely on discrete observations during the gait cycle (e.g. maximal vertical force) and simplifying assumptions (e.g. leg length), challenging the predicative capacity and generalizability of observations. We present a method to model runners as spring-mass systems using nonlinear regression (NLR) and the full vertical ground reaction force (vGRF) time series without additional inputs and fewer traditional parameter assumptions. We derived and validated a time-dependent vGRF function characterized by four spring-mass parameters - stiffness, touchdown angle, leg length and contact time - using a sinusoidal approximation. Next, we compared the NLR-estimated spring-mass parameters with traditional calculations in runners. The mixed-effect NLR method (ME NLR) modeled the observed vGRF best (RMSE:155 N) compared with a conventional sinusoid approximation (RMSE: 230 N). Against the conventional methods, its estimations provided similar stiffness approximations (-0.2±0.6 kN m^-1) with moderately steeper angles (1.2±0.7 deg), longer legs (+4.2±2.3 cm) and shorter effective contact times (-12±4 ms). Together, these vGRF-driven system parameters more closely approximated the observed vertical impulses (observed: 214.8 N s; ME NLR: 209.0 N s; traditional: 223.6 N s). Finally, we generated spring-mass simulations from traditional and ME NLR parameter estimates to assess the predicative capacity of each method to model stable running systems. In 6/7 subjects, ME NLR parameters generated models that ran with equal or greater stability than traditional estimates. ME NLR modeling of the vGRF in running is therefore a useful tool to assess runners holistically as spring-mass systems with fewer measurement sources or anthropometric assumptions. Furthermore, its utility as statistical framework lends itself to more complex mixed-effects modeling to explore research questions in running.

A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions

We introduce a novel dataset containing 3-dimensional biomechanical and wearable sensor data from 22 able-bodied adults for multiple locomotion modes (level-ground/treadmill walking, stair ascent/descent, and ramp ascent/descent) and multiple terrain conditions of each mode (walking speed, stair height, and ramp inclination). In this paper, we present the data collection methods, explain the structure of the open dataset, and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics as a function of the gait phase. This dataset offers a comprehensive source of locomotion information for the same set of subjects to motivate applications in locomotion recognition, developments in robotic assistive devices, and improvement of biomimetic controllers that better adapt to terrain conditions. With such a dataset, models for these applications can be either subject-dependent or subject-independent, allowing greater flexibility for researchers to advance the field.

A Pilot Study of Musculoskeletal Abnormalities in Patients in Recovery from a Unilateral Rupture-Repaired Achilles Tendon

The purpose of this study was to compare the inter-limb joint kinematics, joint moments, muscle forces, and joint reaction forces in patients after an Achilles tendon rupture (ATR) via subject-specific musculoskeletal modeling. Six patients recovering from a surgically repaired unilateral ATR were included in this study. The bilateral Achilles tendon (AT) lengths were evaluated using ultrasound imaging. The three-dimensional marker trajectories, ground reaction forces, and surface electromyography (sEMG) were collected on both sides during self-selected speed during walking, jogging and running. Subject-specific musculoskeletal models were developed to compute joint kinematics, joint moments, muscle forces and joint reaction forces. AT lengths were significantly longer in the involved side. The side-to-side triceps surae muscle strength deficits were combined with decreased plantarflexion angles and moments in the injured leg during walking, jogging and running. However, the increased knee extensor femur muscle forces were associated with greater knee extension degrees and moments in the involved limb during all tasks. Greater knee joint moments and joint reaction forces versus decreased ankle joint moments and joint reaction forces in the involved side indicate elevated knee joint loads compared with reduced ankle joint loads that are present during normal activities after an ATR. In the frontal plane, increased subtalar eversion angles and eversion moments in the involved side were demonstrated only during jogging and running, which were regarded as an indicator for greater medial knee joint loading. It seems after an ATR, the elongated AT accompanied by decreased plantarflexion degrees and calf muscle strength deficits indicates ankle joint function impairment in the injured leg. In addition, increased knee extensor muscle strength and knee joint loads may be a possible compensatory mechanism for decreased ankle function. These data suggest patients after an ATR may suffer from increased knee overuse injury risk.

Effects of segment masses and cut-off frequencies on the estimation of vertical ground reaction forces in running

The purpose of this study is to examine the effect of the body's mass distribution to segments and the filtering of kinematic data on the estimation of vertical ground reaction forces from positional data. A public dataset of raw running biomechanics was used for the purposes of the analysis, containing recordings of twenty-eight competitive or elite athletes running on an instrumented treadmill at three different speeds. A grid-search on half of the trials was employed to seek the values of the parameters that optimise the approximation of biomechanical loads. Two-way ANOVAs were then conducted to examine the significance of the parameterised factors in the modelled waveforms. The reserved recordings were used to validate the predictive accuracy of the model. The cut-off filtering frequencies of the pelvis and thigh markers were correlated to running speed and heel-strike patterns, respectively. Optimal segment masses were in agreement with standardised literature reported values. Root mean square errors for slow running (2.5 m/s) were on average equal to 0.1 (body weight normalized). Errors increased with running speeds to 0.13 and 0.18 for 3.5 m/s and 4.5 m/s, respectively. This study accurately estimated vertical ground reaction forces for slow-paced running by only considering the kinematics of the pelvis and thighs. Future studies should consider configuring the filtering of kinematic inputs based on the location of markers and type of running.