A multimodal dataset of human gait at different walking speeds established on injury-free adult participants

Implications of variability in triceps surae muscle volumes on peak lower limb muscle forces during human walking

Musculoskeletal modeling can be used to estimate forces during locomotion. These models, however, are dependent on underlying assumptions about the model inputs, such as muscle volumes and fiber lengths, to calculate muscle forces. Triceps surae (gastrocnemius medialis, gastrocnemius lateralis, soleus) muscle volume distributions vary among humans. Here we quantify how this muscle volume variation impacts maximum estimated lower limb muscle forces during the braking and propulsive phases of the stance phase of walking. Three triceps surae muscle volume distributions (AnyBody Modeling System standard cadaver [MS], average of 21 cadavers [C], average of 21 young, healthy adults [YHA]) were evaluated in a standard musculoskeletal model using the kinetic and kinematic data of 10 healthy individuals at three walking velocities. Maximum muscle forces were calculated using inverse dynamics and an algorithm to solve the muscle redundancy problem in the AnyBody Modeling System. Repeated measure ANOVAs were used to test for significant differences among the three muscle distribution configurations for each muscle/muscle group at each velocity. Triceps surae muscle volume distribution significantly affects gastrocnemius lateralis and soleus maximum muscle forces for both braking and propulsion at all three velocities (p < 0.001), with relatively larger muscle volumes typically producing relatively larger muscle forces. There was no significant difference in gastrocnemius medialis maximum force among configurations (p > 0.124) except at the self-selected spontaneous velocity during braking. Significant differences exist at some velocities for the hamstrings and gluteus maximus during braking (p < 0.046) and the other plantarflexors, dorsiflexors, evertors, hamstrings, quadriceps, sartorius, and gluteus maximus during propulsion (p < 0.042). Muscle volumes used in musculoskeletal models impact estimated muscle forces of both the muscles of interest and other muscles in the biomechanical chain. This is consistent with recent analyses demonstrating that input values can substantially impact results and suggests individualized muscle parameters may be needed depending on the research question.

AnkleImage - An ultrafast ultrasound image dataset to understand the ankle joint muscle contractility

The role of the human ankle joint in activities of daily living, including walking, maintaining balance, and participating in sports, is of paramount importance. Ankle joint dorsiflexion and plantarflexion functionalities mainly account for ground clearance and propulsion power generation during locomotion tasks, where those functionalities are driven by the contraction of ankle joint skeleton muscles. Studies of corresponding muscle contractility during ankle dynamic functions will facilitate us to better understand the joint torque/power generation mechanism, better diagnose potential muscular disorders on the ankle joint, or better develop wearable assistive/rehabilitative robotic devices that assist in community ambulation. This data descriptor reports a new dataset that includes the ankle joint kinematics/kinetics, associated muscle surface electromyography, and ultrafast ultrasound images with various annotations, such as pennation angle, fascicle length, tissue displacements, echogenicity, and muscle thickness, of ten healthy participants when performing volitional isometric, isokinetic, and dynamic ankle joint functions (walking at multiple treadmill speeds, including 0.50 m/s, 0.75 m/s, 1.00 m/s, 1.25 m/s, and 1.50 m/s). Data were recorded by a research-use ultrasound machine, a self-designed ankle testbed, an inertia measurement unit system, a Vicon motion capture system, a surface electromyography system, and an instrumented treadmill. The descriptor in this work presents the results of a data curation or collection exercise from previous works, rather than describing a novel primary/experimental data collection.

Predicting Knee Joint Contact Force Peaks During Gait Using a Video Camera or Wearable Sensors

PURPOSE

Estimating loading of the knee joint may be helpful in managing degenerative joint diseases. Contemporary methods to estimate loading involve calculating knee joint contact forces using musculoskeletal modeling and simulation from motion capture (MOCAP) data, which must be collected in a specialized environment and analyzed by a trained expert. To make the estimation of knee joint loading more accessible, simple input predictors should be used for predicting knee joint loading using artificial neural networks.

METHODS

We trained feedforward artificial neural networks (ANNs) to predict knee joint loading peaks from the mass, height, age, sex, walking speed, and knee flexion angle (KFA) of subjects using their existing MOCAP data. We also collected an independent MOCAP dataset while recording walking with a video camera (VC) and inertial measurement units (IMUs). We quantified the prediction accuracy of the ANNs using walking speed and KFA estimates from (1) MOCAP data, (2) VC data, and (3) IMU data separately (i.e., we quantified three sets of prediction accuracy metrics).

RESULTS

Using portable modalities, we achieved prediction accuracies between 0.13 and 0.37 root mean square error normalized to the mean of the musculoskeletal analysis-based reference values. The correlation between the predicted and reference loading peaks varied between 0.65 and 0.91. This was comparable to the prediction accuracies obtained when obtaining predictors from motion capture data.

DISCUSSION

The prediction results show that both VCs and IMUs can be used to estimate predictors that can be used in estimating knee joint loading outside the motion laboratory. Future studies should investigate the usability of the methods in an out-of-laboratory setting.

Generative adversarial networks to create synthetic motion capture datasets including subject and gait characteristics

Resource-intensive motion capture (mocap) systems challenge predictive deep learning applications, requiring large and diverse datasets. We tackled this by modifying generative adversarial networks (GANs) into conditional GANs (cGANs) that can generate diverse mocap data, including 15 marker trajectories, lower limb joint angles, and 3D ground reaction forces (GRFs), based on specified subject and gait characteristics. The cGAN comprised 1) an encoder compressing mocap data to a latent vector, 2) a decoder reconstructing the mocap data from the latent vector with specific conditions and 3) a discriminator distinguishing random vectors with conditions from encoded latent vectors with conditions. Single-conditional models were trained separately for age, sex, leg length, mass, and walking speed, while an additional model (Multi-cGAN) combined all conditions simultaneously to generate synthetic data. All models closely replicated the training dataset (<8.1 % of the gait cycle different between experimental and synthetic kinematics and GRFs), while a subset with narrow condition ranges was best replicated by the Multi-cGAN, producing similar kinematics (<1°) and GRFs (<0.02 body-weight) averaged by walking speeds. Multi-cGAN also generated synthetic datasets and results for three previous studies using reported mean and standard deviation of subject and gait characteristics. Additionally, unseen test data was best predicted by the walking speed-conditional, showcasing synthetic data diversity. The same model also matched the dynamical consistency of the experimental data (32 % average difference throughout the gait cycle), meaning that transforming the gait cycle data to the original time domain yielded accurate derivative calculations. Importantly, synthetic data poses no privacy concerns, potentially facilitating data sharing.

Piloting a Novel Computational Framework for Identifying Prosthesis-Specific Contributions to Gait Deviations

This paper introduces a novel computational framework for evaluating above-knee prostheses, addressing a major challenge in gait deviation studies: distinguishing between prosthesis-specific and patient-specific contributions to gait deviations. This innovative approach utilizes three separate computational models to quantify the changes in gait dynamics necessary to achieve a set of ideal gait kinematics across different prosthesis designs. The pilot study presented here employs a simple two-dimensional swing-phase model to conceptually demonstrate how the outcomes of this three-model framework can assess the extent to which prosthesis design impacts a user's ability to replicate the dynamics of able-bodied gait. Furthermore, this framework offers potential for optimizing passive prosthetic devices for individual patients, thereby reducing the need for real-life experiments, clinic visits, and overcoming rehabilitation challenges.

MovePort: Multimodal Dataset of EMG, IMU, MoCap, and Insole Pressure for Analyzing Abnormal Movements and Postures in Rehabilitation Training

In most real world rehabilitation training, patients are trained to regain motion capabilities with the aid of functional/epidural electrical stimulation (FES/EES), under the support of gravity-assist systems to prevent falls. However, the lack of motion analysis dataset designed specifically for rehabilitation-related applications largely limits the conduct of pilot research. We provide an open access dataset, consisting of multimodal data collected via 16 electromyography (EMG) sensors, 6 inertial measurement unit (IMU) sensors, and 230 insole pressure sensors (IPS) per foot, together with a 26-sensor motion capture system, under different MOVEments and POstures for Rehabilitation Training (MovePort). Data were collected under diverse experimental paradigms. Twenty four participants first imitated multiple normal and abnormal body postures including (1) normal standing still, (2) leaning forward, (3) leaning back, and (4) half-squat, which in practical applications, can be detected as feedback to tune the parameters of FES/EES and gravity-assist systems to keep patients in a target body posture. Data under imitated abnormal gaits, e.g., (1) with legs raised higher under excessive electrical stimulation, and (2) with dragging legs under insufficient stimulation, were also collected. Data under normal gaits with low, medium and high speeds are also included. Pathological gait data from a subject with spastic paraplegia further increases the clinical value of our dataset. We also provide source codes to perform both intra- and inter-participant motion analyses of our dataset. We expect our dataset can provide a unique platform to promote collaboration among neurorehabilitation engineers.

Marker Data Enhancement For Markerless Motion Capture

Objective

Human pose estimation models can measure movement from videos at a large scale and low cost; however, open-source pose estimation models typically detect only sparse keypoints, which leads to inaccurate joint kinematics. OpenCap, a freely available service for researchers to measure movement from videos, addresses this issue using a deep learning model-the marker enhancer-that transforms sparse keypoints into dense anatomical markers. However, OpenCap performs poorly on movements not included in the training data. Here, we create a much larger and more diverse training dataset and develop a more accurate and generalizable marker enhancer.

Methods

We compiled marker-based motion capture data from 1176 subjects and synthesized 1433 hours of keypoints and anatomical markers to train the marker enhancer. We evaluated its accuracy in computing kinematics using both benchmark movement videos and synthetic data representing unseen, diverse movements.

Results

The marker enhancer improved kinematic accuracy on benchmark movements (mean error: 4.1°, max: 8.7°) compared to using video keypoints (mean: 9.6°, max: 43.1°) and OpenCap's original enhancer (mean: 5.3°, max: 11.5°). It also better generalized to unseen, diverse movements (mean: 4.1°, max: 6.7°) than OpenCap's original enhancer (mean: 40.4°, max: 252.0°).

Conclusion

Our marker enhancer demonstrates both accuracy and generalizability across diverse movements.

Significance

We integrated the marker enhancer into OpenCap, thereby offering its thousands of users more accurate measurements across a broader range of movements.

Investigation of intersegmental coordination patterns in human walking

BACKGROUND

Intersegmental coordination between thigh, shank, and foot plays a crucial role in human gait, facilitating stable and efficient human walking. Limb elevation angles during the gait cycle form a planar manifold describes the by the planar covariation law, a recognized fundamental aspect of human locomotion.

RESEARCH QUESTION

How does the walking speed, age, BMI, and height, affect the size and orientation of the intersegmental coordination manifold and covariation plane?

METHODS

This study introduces novel metrics for quantifying intersegmental coordination, including the mean radius of the manifold, rotation of the manifold about the origin, and the orientation of the plane with respect to the coordinate planes. A statistical investigation is conducted on a publicly available human walking dataset for subjects aged 19-67 years, walking at speeds between 0.18 and 2.3 m s-1 to determine correlations of the proposed quantities. We used two sample t-test and ANOVA to find statistical significance of changes in the metrics with respect to gender and walking speed, respectively. Regression analysis was used to establish relationships between the introduced metrics and walking speed.

RESULTS

High correlations are observed between walking speed and the computed metrics, highlighting the sensitivity of these metrics to gait characteristics. Conversely, negligible correlations are found for demographic parameters like age, body mass index (BMI), and height. Male and female groups exhibit no practically significant differences in any of the considered metrics. Additionally, metrics tend to increase in magnitude as walking speed increases.

SIGNIFICANCE

This study contributes numerical metrics to characterize ISC of lower limbs with respect to walking speed along with regression models to estimate these metrics and related kinematic quantities. These findings hold significance for enhancing clinical gait analysis, generating optimal walking trajectories for assistive devices, prosthetics, or rehabilitation, aiming to replicate natural gaits and improve the functionality of biomechanical devices.

3D motion analysis dataset of healthy young adult volunteers walking and running on overground and treadmill

Used on clinical and sportive context, three-dimensional motion analysis is considered as the gold standard in the biomechanics field. The proposed dataset has been established on 30 asymptomatic young participants. Volunteers were asked to walk at slow, comfortable and fast speeds, and to run at comfortable and fast speeds on overground and treadmill using shoes. Three dimensional trajectories of 63 reflective markers, 3D ground reaction forces and moments were simultaneously recorded. A total of 4840 and 18159 gait cycles were measured for overground and treadmill walking, respectively. Additionally, 2931 and 18945 cycles were measured for overground and treadmill running, respectively. The dataset is presented in C3D and CSV files either in raw or pre-processed format. The aim of this dataset is to provide a complete set of data that will help for the gait characterization during clinical gait analysis and in a sportive context. This data could be used for the creation of a baseline database for clinical purposes to research activities exploring the gait and the run.

Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets

This paper presents a transfer learning method to enhance locomotion intent prediction in novel transfemoral amputee subjects, particularly in data-sparse scenarios. Transfer learning is done with three pre-trained models trained on separate datasets: transfemoral amputees, able-bodied individuals, and a mixed dataset of both groups. Each model is subsequently fine-tuned using data from a new transfemoral amputee subject. While subject-dependent models, trained and tested using individual user data, can achieve the least error rate, they require extensive training datasets. In contrast, our transfer learning approach yields comparable error rates while requiring significantly less data. This highlights the benefit of using preexisting, pre-trained features when data is scarce. As anticipated, the performance of transfer learning improves as more data from the subject is made available. We also explore the performance of the intent prediction system under various sensor configurations. We identify that a combination of a thigh inertial measurement unit and load cell offers a practical and efficient choice for sensor setup. These findings underscore the potential of transfer learning as a powerful tool for enhancing intent prediction accuracy for new transfemoral amputee subjects, even under data-limited conditions.

Validity of muscle activation estimated with predicted ground reaction force in inverse dynamics based musculoskeletal simulation during gait

The inverse dynamics based musculoskeletal simulation needs ground reaction forces (GRF) as an external force input. GRF can be predicted from kinematic data. However, the validity of estimated muscle activation using the predicted GRF has remained unclear. Therefore, the purpose of this study was to determine the validity of estimated muscle activation with predicted GRF in the inverse dynamics based musculoskeletal simulation. To perform musculoskeletal simulations, an open-source motion capture dataset that contains gait data from 50 healthy subjects was used. CusToM was used for the musculoskeletal simulations. Two sets of inverse dynamics and static optimization were performed, one used predicted GRF (PRED) and another used experimentally measured GRF (EXP). Pearson's correlation was calculated to evaluate the similarity between EMG and estimated muscle activations for both PRED and EXP. To compare PRED and EXP, paired t-tests were used to compare the trial-wise muscle activation similarity and residuals. Relationships between joint moments and residuals were also tested. The overall muscle activation similarity was comparable in PRED (R = 0.477) and EXP (R = 0.475). The residuals were 2-4 times higher in EXP compared to PRED (P < 0.001). The hip flexion-extension moment was correlated to sagittal plane residual moment (R = 0.467). The muscle activations estimated using predicted GRF were comparable to that with measured GRF in the inverse dynamics based musculoskeletal simulation. Prediction of GRF helps to perform musculoskeletal simulations where the force plates are not available.

Intersegmental coordination in human slip perturbation responses

Intersegmental coordination (ISC) of lower limbs and planar covariation law (PCL) are important phenomena observed in biomechanics of human walking and other activities. Gait perturbations tend to cause deviation from the expected ISC pattern thus violating PCL. We used a data set of seven subjects, who experienced unexpected slips, to investigate and characterize the evolution of ISC during slip recoveries and falls. We have analyzed and presented the development of ISC patterns, encompassing the step preceding the slip initiation and duration of slip until it stops. The results show that the ISC patterns during slip recovery deviate considerably from the normal walking patterns. A newly proposed Euclidian distance-based metric (EDM) was used to quantify the deviation from the normal walking ISC pattern during four slip recoveries and three falls evaluated at gait events such as slip start, foot strike, and peak height of the swing foot. The timing of gait events after slip, pattern of EDM, placement of the feet after slip and temporal patterns of each limb angle have been presented. This initial investigation provides insight into the ISC during slip recovery which highlights the human natural recovery trajectories during such perturbations. The observed patterns of the ISC trajectories during slip can be used for the design of human-inspired controllers for exoskeleton devices that can provide external assistance to human subjects during balance recovery.

The effect of amplitude normalization technique, walking speed, and reporting metric on whole-body angular momentum and its interpretation during normal gait

Whole-body angular momentum (WBAM) represents the cancellations of angular momenta that are produced during a reciprocal gait pattern. WBAM is sensitive to small changes and is used to compare dynamic gait patterns under different walking conditions. Study designs and the normalization techniques used to define WBAM vary and make comparisons between studies difficult. To address this problem, WBAM about each anatomical axis of rotation from a healthy control population during normal gait were investigated within four metrics: 1) range of WBAM, 2) integrated WBAM, 3) statistical parametric mapping (SPM), and 4) principal component analysis (PCA). These data were studied as a function of walking speed and normalization. Normalization techniques included: 1) no normalization, 2) normalization by height, body mass and walking speed, and 3) normalization by height, body mass and a scalar number, gravity×height, that is independent of walking velocity. Significant results were obtained as a function of walking speed regardless of normalization technique. However, the interpretation of significance within each metric was dependent on the normalization technique. Method 3 was the most robust technique as the differences were not altered from the expected relationships within the raw data. Method 2 actually inverted the expected relationship in WBAM amplitude as a function of walking speed, which skewed the results and their interpretation. Overall, SPM and PCA statistical methods provided better insights into differences that may be important. However, depending on the normalization technique used, caution is advised when interpreting significant findings when comparing participants with disparate walking speeds.

Quantifying walking speeds in relation to ankle biomechanics on a real-time interactive gait platform: a musculoskeletal modeling approach in healthy adults

Background: Given the inherent variability in walking speeds encountered in day-to-day activities, understanding the corresponding alterations in ankle biomechanics would provide valuable clinical insights. Therefore, the objective of this study was to examine the influence of different walking speeds on biomechanical parameters, utilizing gait analysis and musculoskeletal modelling. Methods: Twenty healthy volunteers without any lower limb medical history were included in this study. Treadmill-assisted gait-analysis with walking speeds of 0.8 m/s and 1.1 m/s was performed using the Gait Real-time Analysis Interactive Lab (GRAIL®). Collected kinematic data and ground reaction forces were processed via the AnyBody® modeling system to determine ankle kinetics and muscle forces of the lower leg. Data were statistically analyzed using statistical parametric mapping to reveal both spatiotemporal and magnitude significant differences. Results: Significant differences were found for both magnitude and spatiotemporal curves between 0.8 m/s and 1.1 m/s for the ankle flexion (p < 0.001), subtalar force (p < 0.001), ankle joint reaction force and muscles forces of the M. gastrocnemius, M. soleus and M. peroneus longus (α = 0.05). No significant spatiotemporal differences were found between 0.8 m/s and 1.1 m/s for the M. tibialis anterior and posterior. Discussion: A significant impact on ankle joint kinematics and kinetics was observed when comparing walking speeds of 0.8 m/s and 1.1 m/s. The findings of this study underscore the influence of walking speed on the biomechanics of the ankle. Such insights may provide a biomechanical rationale for several therapeutic and preventative strategies for ankle conditions.

On the relation between gait speed and gait cycle duration for walking on even ground

Gait models and reference motions are essential for the objective assessment of walking patterns and therapy progress, as well as research in the field of wearable robotics and rehabilitation devices in general. A human can achieve a desired gait speed by adjusting stride length and/or stride frequency. It is hypothesized that sex, age, and physique of a person have a significant influence on the combination of these parameters. A mathematical description of the relation between gait speed and its determinants is presented in the form of a parameterized analytic function. Based on the statistical significance of the parameters, three models are derived. The first two models are valid for slow to fast walking, which is defined as the interval of approximately 0.6-2.0ms-1, assuming a linear relation of gait speed and stride length, and a non-linear relation of gait speed and stride duration, respectively. The third model is valid for a defined range of walking speed centered at a certain (preferred or spontaneous) gait speed. The latter assumes a constant walk ratio, i.e. the ratio between step or stride length and step or stride frequency, and is recommended for walking at a speed of 1.0-1.6ms-1. On the basis of a large pool of gait datasets, regression coefficients with significance for age and/or body mass index are identified. The presented models allow to estimate the gait cycle duration based on gait speed, sex, age and body mass index of healthy persons walking on even ground.

On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods

Knee osteoarthritis (OA) is a public health problem affecting millions of people worldwide. The intensity of the tibiofemoral contact forces is related to cartilage degeneration, and so is the importance of quantifying joint loads during daily activities. Although simulation with musculoskeletal models has been used to calculate joint loads, it demands high-cost equipment and a very time-consuming process. This study aimed to evaluate consolidated machine learning algorithms to predict tibiofemoral forces during gait analysis of healthy individuals and knee OA patients. Also, we evaluated three different datasets to train each model, considering different combinations of primary kinematic and kinetic data, and post-processing data. We evaluated 14 patients with severe unilateral knee OA and 14 healthy individuals during 3-5 gait trials. Data were split into 70% and 30% of the samples as training and test data. Test data was independently evaluated considering a mixture of pathological and healthy individuals, and only OA and Control patients. The main results showed that accurate predictions of the tibiofemoral contact forces were achieved using machine learning methods and that the predictions were sensitive to changes in the input data as training. The present study provided insights into the most promising regressions methods to predict knee contact forces representing an important starting point for the broader application of biomechanical analysis in clinical environments.

Consistency of muscle activation signatures across different walking speeds

BACKGROUND

Using a machine learning algorithm, individuals can be accurately identified from their muscle activation patterns during gait, leading to the concept of individual muscle activation signatures.

RESEARCH QUESTION

Are muscle activation signatures robust across different walking speeds?

METHODS

We used an open dataset containing electromyographic (EMG) signals from 8 lower limb muscles in 50 asymptomatic adults walking at 5 speeds (extremely slow, very slow, slow, spontaneous, and fast). A machine learning approach classified the EMG profiles based on similar (intra-speed classification) or different (inter-speed classification) walking speeds as training and testing conditions.

RESULTS

Intra-speed median classification rates of muscle activation profiles increased with walking speed, from 92 % for extremely slow, to 100 % for self-selected fast walking conditions. Inter-speed median classification rates increased when the speed of the training condition was closer to that of the testing condition. Higher median classification rates were found across slow, spontaneous, and fast walking speed conditions, from 56 % to 96 %, compared with classification rates involving extremely and very slow walking speed conditions, from 6 % to 62 %.

SIGNIFICANCE

Our findings reveal that i) muscle activation signatures are detectable for a large range of walking speeds, even those involving different gait strategies (intra-speed median classification rates from 92 % to 100 %), and ii) muscle activation signatures observed during very low walking speeds are not consistent with those observed at higher speeds, suggesting a difference in motor control strategy. Caution should therefore be exercised when assessing gait deviations of a slow walking patient against a normative database obtained at higher speed. Identifying the robustness of individual muscle activation signatures across different movements could help in detecting changes in motor control, otherwise difficult to detect on classical time-varying EMG patterns.

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.

NONAN GaitPrint: An IMU gait database of healthy young adults

An ongoing thrust of research focused on human gait pertains to identifying individuals based on gait patterns. However, no existing gait database supports modeling efforts to assess gait patterns unique to individuals. Hence, we introduce the Nonlinear Analysis Core (NONAN) GaitPrint database containing whole body kinematics and foot placement during self-paced overground walking on a 200-meter looping indoor track. Noraxon Ultium MotionTM inertial measurement unit (IMU) sensors sampled the motion of 35 healthy young adults (19-35 years old; 18 men and 17 women; mean ± 1 s.d. age: 24.6 ± 2.7 years; height: 1.73 ± 0.78 m; body mass: 72.44 ± 15.04 kg) over 18 4-min trials across two days. Continuous variables include acceleration, velocity, position, and the acceleration, velocity, position, orientation, and rotational velocity of each corresponding body segment, and the angle of each respective joint. The discrete variables include an exhaustive set of gait parameters derived from the spatiotemporal dynamics of foot placement. We technically validate our data using continuous relative phase, Lyapunov exponent, and Hurst exponent-nonlinear metrics quantifying different aspects of healthy human gait.

ToulGaitViz: a tool for the systematic description of lower limb clearance during the swing phase of hemiparetic gait after stroke. A cohort study

BACKGROUND

In post-stroke hemiparetic subjects, a systematic and quantified description of the shortening default and compensatory movements during the swing phase of gait is essential to guide treatments and assess the impact of therapeutic interventions. However, such a systematic approach does not exist in the current clinical practice.

AIM

The aim of this study was to present a method improving the quantification and visualization of the kinematics of both lower limbs during the swing phase of gait, more specifically the origin of shortening default and the weight of compensations, based on a tool specifically developed: ToulGaitViz.

DESIGN

Observational cohort study.

SETTING

Three-dimensional kinematic gait analyses of outpatients evaluated in Toulouse university hospital.

POPULATION

ToulGaitViz was applied to 151 post-stroke hemiparetic participants and 48 healthy control participants.

METHODS

ToulGaitViz is a standalone software allowing to compute 1) limb clearance as the sum of the shortening related to hip, knee and ankle flexion in the sagittal plane; 2) compensations related to the abduction of the limb and hip hiking at mid-swing. Both centimetric and angular values of the clearance were reported as well as their correlations with walking speed.

RESULTS

Overall, the contribution of compensations in clearance was higher in post-stroke hemiparetic subjects than in healthy control participants with both centimetric (130% vs. 33%; P<0.001) and angular methods (23% vs. 1.4%; P<0.001). The centimetric method better represents the specific contribution of each segment to the clearance than the angular method. Symbolically, mean kinematic data from the cohort supports the claim that 2° of pelvic obliquity is equivalent to 10° of knee flexion to increase clearance by 1 cm, emphasizing the non-proportionality between the angular values and the actual contribution to the shortening. ToulGaitViz allows visualization of clearance, segmental shortening and compensation evolution before and after any therapeutic intervention with quantitative and comprehensive data.

CONCLUSIONS

The ToulGaitViz could be systematically used in clinical practice to extract relevant kinematic data from the origin of shortening default and the weight of compensations.

CLINICAL REHABILITATION IMPACT

This tool allows better understanding of the mechanisms of action of treatments to better link them to the subjects' needs.

A full-body motion capture gait dataset of 138 able-bodied adults across the life span and 50 stroke survivors

This reference dataset contains biomechanical data of 138 able-bodied adults (21-86 years) and 50 stroke survivors walking bare-footed at their preferred speed. It is unique due to its size, and population, including adults across the life-span and over 70 years, as well as stroke survivors. Full-body kinematics (PiG-model), kinetics and muscle activity of 14 back and lower limbs muscles was collected with a Vicon motion capture system, ground-embedded force plates, and a synchronized surface EMG system. The data is reliable to compare within and between groups as the same methodology and infrastructure were used to gather all data. Both source files (C3D) and post-processed ready-to-use stride-normalized kinematics, kinetics and EMG data (MAT-file, Excel file) are available, allowing high flexibility and accessibility of analysis for both researchers and clinicians. These records are valuable to examine ageing, typical and hemiplegic gait, while also offering a wide range of reference data which can be utilized for age-matched controls during normal walking.

Single-Belt Versus Split-Belt: Intelligent Treadmill Control via Microphase Gait Capture for Poststroke Rehabilitation

Stroke is the leading long-term disability and causes a significant financial burden associated with rehabilitation. In poststroke rehabilitation, individuals with hemiparesis have a specialized demand for coordinated movement between the paretic and the nonparetic legs. The split-belt treadmill can effectively facilitate the paretic leg by slowing down the belt speed for that leg while the patient is walking on a split-belt treadmill. Although studies have found that split-belt treadmills can produce better gait recovery outcomes than traditional single-belt treadmills, the high cost of split-belt treadmills is a significant barrier to stroke rehabilitation in clinics. In this article, we design an AI-based system for the single-belt treadmill to make it act like a split-belt by adjusting the belt speed instantaneously according to the patient's microgait phases. This system only requires a low-cost RGB camera to capture human gait patterns. A novel microgait classification pipeline model is used to detect gait phases in real time. The pipeline is based on self-supervised learning that can calibrate the anchor video with the real-time video. We then use a ResNet-LSTM module to handle temporal information and increase accuracy. A real-time filtering algorithm is used to smoothen the treadmill control. We have tested the developed system with 34 healthy individuals and four stroke patients. The results show that our system is able to detect the gait microphase accurately and requires less human annotation in training, compared to the ResNet50 classifier. Our system "Splicer" is boosted by AI modules and performs comparably as a split-belt system, in terms of timely varying left/right foot speed, creating a hemiparetic gait in healthy individuals, and promoting paretic side symmetry in force exertion for stroke patients. This innovative design can potentially provide cost-effective rehabilitation treatment for hemiparetic patients.

Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks

Joint loading may affect the development of osteoarthritis, but patient-specific load estimation requires cumbersome motion laboratory equipment. This reliance could be eliminated using artificial neural networks (ANNs) to predict loading from simple input predictors. We used subject-specific musculoskeletal simulations to estimate knee joint contact forces for 290 subjects during over 5000 stance phases of walking and then extracted compartmental and total joint loading maxima from the first and second peaks of the stance phase. We then trained ANN models to predict the loading maxima from predictors that can be measured without motion laboratory equipment (subject mass, height, age, gender, knee abduction-adduction angle, and walking speed). When compared to the target data, our trained models had NRMSEs (RMSEs normalized to the mean of the response variable) between 0.14 and 0.42 and Pearson correlation coefficients between 0.42 and 0.84. The loading maxima were predicted most accurately using the models trained with all predictors. We demonstrated that prediction of knee joint loading maxima may be possible without laboratory-measured motion capture data. This is a promising step in facilitating knee joint loading predictions in simple environments, such as a physician's appointment. In future, the rapid measurement and analysis setup could be utilized to guide patients in rehabilitation to slow development of joint disorders, such as osteoarthritis.

CHECGAIT: A Functional Electrical Stimulation Clinical Pathway to Reduce Foot Drop during Walking in Adult Patients with Upper Motor Neuron Lesions

Foot drop during the swing phase of gait and at initial foot contact is a current kinematic abnormality that can occur following an upper motor neuron (UMN) lesion. Functional electrical stimulation (FES) of the common peroneal nerve through an assistive device is often used in neuro-rehabilitation to help patients regain mobility. Although there are FES-specific guideline recommendations, it remains a challenge for clinicians to appropriately select patients eligible for the daily use of FES devices, as very few health insurance systems cover its cost in Europe. In Luxembourg, since 2018, successfully completing an FES clinical pathway called CHECGAIT is a prerequisite to receiving financial coverage for FES devices from the national health fund (Caisse Nationale de Santé-CNS). This study describes the structure and steps of CHECGAIT and reports our experience with a cohort of 100 patients enrolled over a three-year period. The clinical and gait outcomes of all patients were retrospectively quantified, and a specific analysis was performed to highlight differences between patients with and without an FES device prescription at the end of a CHECGAIT. Several significant gait differences were found between these groups. These results and CHECGAIT may help clinicians to better select patients who can most benefit from this technology in their daily lives. In addition, CHECGAIT could provide significant savings to public health systems by avoiding unnecessary deliveries of FES devices.

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.

Database covering the prayer movements which were not available previously

Lower body implants are designed according to the boundary conditions of gait data and tested against. However, due to diversity in cultural backgrounds, religious rituals might cause different ranges of motion and different loading patterns. Especially in the Eastern part of the world, diverse Activities of Daily Living (ADL) consist of salat, yoga rituals, and different style sitting postures. A database covering these diverse activities of the Eastern world is non-existent. This study focuses on data collection protocol and the creation of an online database of previously excluded ADL activities, targeting 200 healthy subjects via Qualisys and IMU motion capture systems, and force plates, from West and Middle East Asian populations with a special focus on the lower body joints. The current version of the database covers 50 volunteers for 13 different activities. The tasks are defined and listed in a table to create a database to search based on age, gender, BMI, type of activity, and motion capture system. The collected data is to be used for designing implants to allow these sorts of activities to be performed.

Hip width and metabolic energy expenditure of abductor muscles

Despite a paucity of physiological evidence, simplistic biomechanical analyses have led researchers to assume that humans who have wider hips use more energy to walk. Pitting biomechanical first principles against physiological data has led to little deepening of our understanding of bipedalism and its evolution. Both approaches, however, use proxies for the energy used by muscles. We decided to approach the question directly. Using a musculoskeletal model of the human body that estimates the metabolic energy expenditure of muscle activation for 48 people (23 women), 752 trials were evaluated. Metabolic energy consumption for the abductor muscles was summed over a stride to create total abductor energy expenditure. We calculated the maximum hip joint moment acting in the coronal plane and the functional distance between the hip joint centers. We hypothesize that wider hips would be correlated with both maximum coronal plane hip moment and increased total abductor energy expenditure when mass and velocity were controlled. Linear regressions with multiple independent variables, clustered by participant to control for the non-independence of the data points, were performed in Stata. We found that hip width does not predict total abductor energy expenditure, although mass and velocity combine to predict 61% of the variation (both p<0.001). Maximum hip joint coronal plane moment is predicted by pelvic width (p<0.001) and, in combination with mass and velocity (both p<0.001), explains 79% of the variation. Our results indicate that people use their morphology in ways that limit differences in energy expenditure. Consistent with recent discussion, intraspecific variation might not be useful to understand differences among species.

A dataset of asymptomatic human gait and movements obtained from markers, IMUs, insoles and force plates

Human motion capture and analysis could be made easier through the use of wearable devices such as inertial sensors and/or pressure insoles. However, many steps are still needed to reach the performance of optoelectronic systems to compute kinematic parameters. The proposed dataset has been established on 10 asymptomatic adults. Participants were asked to walk at different speeds on a 10-meters walkway in a laboratory and to perform different movements such as squats or knee flexion/extension tasks. Three-dimensional trajectories of 69 reflective markers placed according to a conventional full body markerset, acceleration and angular velocity signals of 8 inertial sensors, pressure signals of 2 insoles, 3D ground reaction forces and moments obtained from 3 force plates were simultaneously recorded. Eight calculated virtual markers related to joint centers were also added to the dataset. This dataset contains a total of 337 trials including static and dynamic tasks for each participant. Its purpose is to enable comparisons between various motion capture systems and stimulate the development of new methods for gait analysis.

Piecewise Linear Labeling Method for Speed-Adaptability Enhancement in Human Gait Phase Estimation

Human gait phase estimation has been studied in the field of robotics due to its importance in controlling wearable devices (e.g., robotic prostheses or exoskeletons) in a synchronized manner with the user. Researchers have attempted to estimate the user's gait phase using a learning-based method, as data-driven approaches have recently emerged in the field. In this study, we propose a new labeling method (i.e., a piecewise linear label) to have the estimator learn the ground truth based on variable toe-off onset at different walking speeds. Using whole-body marker data, we computed the angular positions and velocities of thigh and torso segments and utilized them as input data for model training. Three models (i.e., general, slow, and normal-fast) were obtained based on long short-term memory (LSTM). These models are compared in order to identify the effect of the piecewise linear label at various walking speeds. As a result, when the proposed labeling method was used while training the general model, the estimation accuracy was significantly improved. This fact was also found when estimating the user's gait phase during the mid-stance phase. Furthermore, the proposed method maintained good performance in detecting the heel-strike and toe-off. According to the findings of this study, the newly proposed labeling method could improve speed-adaptability in gait phase estimation, resulting in outstanding accuracy for both gait phase, heel-strike, and toe-off estimation.

Real-world measurements of ground reaction forces of normal gait of young adults wearing various footwear

For years, researchers have been recognizing patterns in gait for purposes of medical diagnostics, rehabilitation, and biometrics. A method for observing gait is to measure ground reaction forces (GRFs) between the foot and solid plate with tension sensors. The presented dataset consists of 13,702 measurements of bipedal GRFs of one step of normal gait of 324 students wearing shoes of various types. Each measurement includes raw digital signals of two force plates. A signal comprises stance-related samples but also preceding and following ones, in which one can observe noise, interferences, and artifacts caused by imperfections of devices and walkway. Such real-world time series can be used to study methods for detecting foot-strike and foot-off events, and for coping with artifacts. For user convenience, processed data are also available, which describe only the stance phase of gait and form ready-to-use patterns suitable for experiments in GRF-based recognition of persons and footwear, and for generating synthetic GRF waveforms. The dataset is accompanied by Matlab and Python programs for organizing and validating data.

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.

Estimation of lower-limb sagittal joint moments during gait using vertical ground reaction force

Lower-limb sagittal joint moments during gait are important variables for evaluating the risk of disease progression, such as that of orthopedic diseases. Therefore, quantifying lower-limb sagittal joint moments during walking is important to continuously evaluate the risk of disease progression. A motion capture system and force plate are employed in the calculation of lower-limb sagittal joint moments during gait. However, they cannot be used during daily walking. Therefore, it is important to estimate these moments during walking from the vertical ground reaction force (vGRF), which can be measured using a wearable sensor, such as an insole device. Thus, this study aimed to estimate the lower-limb sagittal joint moments during gait using only the vGRF and confirmed its accuracy. This study included 188 healthy adults, and each participant walked at a comfortable speed (10 trials). We estimated the moments from the vGRF using a feedforward neural network. Our major findings are that our method can estimate lower-limb sagittal joint moments using the vGRF with accuracies of NRMSE¯ within 6.0-11.7% (NRMSEs¯ of the hip, knee, and ankle were 8.4, 11.7, and 6.0%, respectively). To the best of our knowledge, this study is the first to estimate lower-limb sagittal joint moments (including those of the hip, knee, and ankle joints) during gait using only the vGRF. Our method may be useful to estimate lower-limb sagittal joint moments during daily walking using only the vGRF, which can be measured by an insole device in the future.

Biomechanical mechanism of peak braking force modulation during increased walking speed in healthy young adults

Walking speed is an important indicator of health and function across a variety of populations. Faster walking requires both larger propulsive and braking forces, thoughof the two, propulsive force generation has been far more extensively investigated. This study seeks to develop and validatea quasi-static biomechanical model of braking forcein healthy individualsacrossself-selected and fast walking speeds. Additionally, the model was used to quantify the relative contribution of knee extension torque versus leading limb angle (LLA) to changes in braking force across walking speeds. Kinetic and kinematic data from 44 young healthy participants walking overground at 2 different speeds were analyzed. The model prediction correlated strongly with actual braking force production at the self-selected speed (r = 0.9; p < 0.01), the fast speed (r = 0.97; p < 0.01) andthe change between speeds (r = 0.95, p < 0.01). On average, increases in knee extension torque and the LLA contributed 132 % and 12 %, respectively, to increases in peak braking force (PBF). Increases in the external lever arm length operated to reduce predicted braking force by 56 %. The results highlight the importance of rapid eccentric contraction of the knee extensors during braking force modulation in healthy gait.

A multi-sensor human gait dataset captured through an optical system and inertial measurement units

Different technologies can acquire data for gait analysis, such as optical systems and inertial measurement units (IMUs). Each technology has its drawbacks and advantages, fitting best to particular applications. The presented multi-sensor human gait dataset comprises synchronized inertial and optical motion data from 25 participants free of lower-limb injuries, aged between 18 and 47 years. A smartphone and a custom micro-controlled device with an IMU were attached to one of the participant's legs to capture accelerometer and gyroscope data, and 42 reflexive markers were taped over the whole body to record three-dimensional trajectories. The trajectories and inertial measurements were simultaneously recorded and synchronized. Participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and pre-processed in each of two sessions, performed on different days. This dataset supports the comparison of gait parameters and properties of inertial and optical capture systems, whereas allows the study of gait characteristics specific for each system.

Increased Femoral Anteversion Does Not Lead to Increased Joint Forces During Gait in a Cohort of Adolescent Patients

Orthopedic complications were previously reported for patients with increased femoral anteversion. A more comprehensive analysis of the influence of increased femoral anteversion on joint loading in these patients is required to better understand the pathology and its clinical management. Therefore, the aim was to investigate lower-limb kinematics, joint moments and forces during gait in adolescent patients with increased, isolated femoral anteversion compared to typically developing controls. Secondly, relationships between the joint loads experienced by the patients and different morphological and kinematic features were investigated. Patients with increased femoral anteversion (n = 42, 12.8 ± 1.9 years, femoral anteversion: 39.6 ± 6.9°) were compared to typically developing controls (n = 9, 12.0 ± 3.0 years, femoral anteversion: 18.7 ± 4.1°). Hip and knee joint kinematics and kinetics were calculated using subject-specific musculoskeletal models. Differences between patients and controls in the investigated outcome variables (joint kinematics, moments, and forces) were evaluated through statistical parametric mapping with Hotelling T2 and t-tests (α = 0.05). Canonical correlation analyses (CCAs) and regression analyses were used to evaluate within the patients’ cohort the effect of different morphological and kinematic predictors on the outcome variables. Predicted compressive proximo-distal loads in both hip and knee joints were significantly reduced in patients compared to controls. A gait pattern characterized by increased knee flexion during terminal stance (KneeFlex_tSt) was significantly correlated with hip and knee forces, as well as with the resultant force exerted by the quadriceps on the patella. On the other hand, hip internal rotation and in-toeing, did not affect the loads in the joints. Based on the finding of the CCAs and linear regression analyses, patients were further divided into two subgroups based KneeFlex_tSt. Patients with excessive KneeFlex_tSt presented a significantly higher femoral anteversion than those with normal KneeFlex_tSt. Patients with excessive KneeFlex_tSt presented significantly larger quadriceps forces on the patella and a larger posteriorly-oriented shear force at the knee, compared to patients with normal KneeFlex_tSt, but both patients’ subgroups presented only limited differences in terms of joint loading compared to controls. This study showed that an altered femoral morphology does not necessarily lead to an increased risk of joint overloading, but instead patient-specific kinematics should be considered.

Effect of Torso Kinematics on Gait Phase Estimation at Different Walking Speeds

Human gait phase estimation has been studied in the field of robotics due to its importance for controlling wearable devices (e.g., prostheses or exoskeletons) in a synchronized manner with the user. As data-driven approaches have recently risen in the field, researchers have attempted to estimate the user gait phase using a learning-based method. Thigh and torso information have been widely utilized in estimating the human gait phase for wearable devices. Torso information, however, is known to have high variability, specifically in slow walking, and its effect on gait phase estimation has not been studied. In this study, we quantified torso variability and investigated how the torso information affects the gait phase estimation result at various walking speeds. We obtained three different trained models (i.e., general, slow, and normal-fast models) using long short-term memory (LSTM). These models were compared to identify the effect of torso information at different walking speeds. In addition, the ablation study was performed to identify the isolated effect of the torso on the gait phase estimation. As a result, when the torso segment's angular velocity was used with thigh information, the accuracy of gait phase estimation was increased, while the torso segment's angular position had no apparent effect on the accuracy. This study suggests that the torso segment's angular velocity enhances human gait phase estimation when used together with the thigh information despite its known variability.

Handle With Care: The Anterior Hip Capsule Plays a Key Role in Daily Hip Performance

Background:

Passive energy storage and return has long been recognized as one of the central mechanisms for minimizing the energy cost needed for terrestrial locomotion. Although the iliofemoral ligament (IFL) is the strongest ligament in the body, its potential role in energy-efficient walking remains unexplored.

Purpose:

To identify the contribution of the IFL to the amount of work performed by the hip muscles for normal, straight-level walking.

Study Design:

Controlled laboratory study.

Methods:

Straight-level walking of 50 healthy and injury-free adults was simulated using the AnyBody Modeling System. For each participant, the bone morphology and soft tissue properties were nonuniformly scaled. The superior and inferior parts of the IFL were represented by 2 springs each, and a linear force-strain relation was defined. A parameter study was conducted to account for the uncertainty surrounding the mechanical properties of the IFL. The work required from the gluteus, quadriceps, iliopsoas, and sartorius with and without inclusion of the IFL was calculated. Analysis of variance with subsequent post hoc paired t test was used to test the significance of IFL presence on the required mechanical work.

Results:

During walking, the strain in the IFL reached a median of 18.7% (95% CI, 8.0%-26.5%), with the largest values obtained at toe-off. With the IFL undamaged and fully operational, the effort required by the hip flexor muscles was reduced by a median of 54% (99% CI, 45%-62%) for the iliopsoas and by a median of 41% (99% CI, 27%-54%) for the sartorius muscles. The inclusion of the IFL did not significantly alter the work required by the gluteus and the quadriceps.

Conclusion:

The findings emphasized the key role the IFL plays in hip flexion by working synergistically with the hip musculature.

Clinical Relevance:

The importance of the contribution of the IFL to the hip flexors warrants careful handling and repair of these ligaments in cases of surgery and structural damage.

The Short-Term Effects of Rhythmic Vibrotactile and Auditory Biofeedback on the Gait of Individuals After Weight-Induced Asymmetry

BACKGROUND

Biofeedback (BFB), the practice of providing real-time sensory feedback has been shown to improve gait rehabilitation outcomes. BFB training through rhythmic stimulation has the potential to improve spatiotemporal gait asymmetries while minimizing cognitive load by encouraging a synchronization between the user's gait cycle and an external rhythm.

OBJECTIVE

The purpose of this work was to evaluate if rhythmic stimulation can improve the stance time symmetry ratio (STSR) and to compare vibrotactile to auditory stimulation. Gait parameters including velocity, cadence, stride length, double support time, and step length symmetry, were also examined.

METHODOLOGY

An experimental rhythmic stimulation system was developed, and twelve healthy adults (5 males), age 28.42 ± 10.93 years, were recruited to participate in walking trials. A unilateral ankle weight was used to induce a gait asymmetry to simulate asymmetry as commonly exhibited by individuals with lower limb amputation and other clinical disorders. Four conditions were evaluated: 1) No ankle weight baseline, 2) ankle weight without rhythmic stimulation, 3) ankle weight + rhythmic vibrotactile stimulation (RVS) using alternating motors and 4) ankle weight + rhythmic auditory stimulation (RAS) using a singletone metronome at the participant's self-selected cadence.

FINDINGS

As expected the STSR became significantly more asymmetrical with the ankle weight (i.e. induced asymmetry condition). STSR improved significantly with RVS and RAS when compared to the ankle weight without rhythmic stimulation. Cadence also significantly improved with RVS and RAS compared to ankle weight without rhythmic stimulation. With the exception of double support time, the other gait parameters were unchanged from the ankle weight condition. There were no statistically significant differences between RVS and RAS.

CONCLUSION

This study found that rhythmic stimulation can improve the STSR when an asymmetry is induced. Moreover, RVS is at least as effective as auditory stimulation in improving STSR in healthy adults with an induced gait asymmetry. Future work should be extended to populations with mobility impairments and outside of laboratory settings.

The mechanical energetics of walking across the adult lifespan

PURPOSE

Understanding what constitutes normal walking mechanics across the adult lifespan is crucial to the identification and intervention of early decline in walking function. Existing research has assumed a simple linear alteration in peak joint powers between young and older adults. The aim of the present study was to quantify the potential (non)linear relationship between age and the joint power waveforms of the lower limb during walking.

METHODS

This was a pooled secondary analysis of the authors' (MT, KD, JJ) and three publicly available datasets, resulting in a dataset of 278 adults between the ages of 19 to 86 years old. Three-dimensional motion capture with synchronised force plate assessment was performed during self-paced walking. Inverse dynamics were used to quantity joint power of the ankle, knee, and hip, which were time-normalized to 100 stride cycle points. Generalized Additive Models for location, scale and shape (GAMLSS) was used to model the effect of cycle points, age, walking speed, stride length, height, and their interaction on the outcome of each joint's power.

RESULTS

At both 1m/s and 1.5 m/s, A2 peaked at the age of 60 years old with a value of 3.09 (95% confidence interval [CI] 2.95 to 3.23) W/kg and 3.05 (95%CI 2.94 to 3.16), respectively. For H1, joint power peaked with a value of 0.40 (95%CI 0.31 to 0.49) W/kg at 1m/s, and with a value of 0.78 (95%CI 0.72 to 0.84) W/kg at 1.5m/s, at the age of 20 years old. For H3, joint power peaked with a value of 0.69 (95%CI 0.62 to 0.76) W/kg at 1m/s, and with a value of 1.38 (95%CI 1.32 to 1.44) W/kg at 1.5m/s, at the age of 70 years old.

CONCLUSIONS

Findings from this study do not support a simple linear relationship between joint power and ageing. A more in-depth understanding of walking mechanics across the lifespan may provide more opportunities to develop early clinical diagnostic and therapeutic strategies for impaired walking function. We anticipate that the present methodology of pooling data across multiple studies, is a novel and useful research method to understand motor development across the lifespan.

Statistical kinematic modelling: concepts and model validity

Data reduction techniques are applied to reduce the volume of data while maintaining its integrity. For cyclic motion data, a reliable overview comparing these methods is lacking. Therefore, this study aims to evaluate the features of the different data reduction techniques by applying them to large public data sets. The periodicity of cyclic motion can be exploited by either analysing a single cycle or studying a series of cycles. Analysing single cycles requires a pre-processing step to isolate the amplitude variability. Three different alignment techniques were evaluated, namely Linear length normalisation (LLN), piecewise LLN (PLLN) and continuous registration (CR). CR showed to remove the most phase variation. For the data reduction, three techniques were assessed (i.e., principal component analysis (PCA), principal polynomial analysis (PPA) and multivariate functional PCA (MFPCA)) based on the in- and out-of-sample error, the compactness and the computation time. The differences were found to be minimal. From our results, PPA appeared to be most useful for data compression. Further, we recommend PCA and MFPCA for classification and feature extraction purposes. We suggest the use of PCA when computation time is key and we advise the use of MFPCA when the inclusion of different data sources is desired. In contrast, the analysis of a series of cycles requires a pre-processing step to decompose the series. Further, a regression model was used to compensate for the difference in fundamental frequency. PCA on FC and MFPCA with splines were applied on the frequency compensated curves. Both methods performed as good.

Lower-limb kinematics and kinetics during continuously varying human locomotion

Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. This data paper reports a new dataset that includes the lower-limb kinematics and kinetics of ten able-bodied participants walking at multiple inclines (±0°; 5° and 10°) and speeds (0.8 m/s; 1 m/s; 1.2 m/s), running at multiple speeds (1.8 m/s; 2 m/s; 2.2 m/s and 2.4 m/s), walking and running with constant acceleration (±0.2; 0.5), and stair ascent/descent with multiple stair inclines (20°; 25°; 30° and 35°). This dataset also includes sit-stand transitions, walk-run transitions, and walk-stairs transitions. Data were recorded by a Vicon motion capture system and, for applicable tasks, a Bertec instrumented treadmill.

Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study

Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 < R2 < 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification.

Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals

The Gutenberg Gait Database comprises data of 350 healthy individuals recorded in our laboratory over the past seven years. The database contains ground reaction force (GRF) and center of pressure (COP) data of two consecutive steps measured - by two force plates embedded in the ground - during level overground walking at self-selected walking speed. The database includes participants of varying ages, from 11 to 64 years. For each participant, up to eight gait analysis sessions were recorded, with each session comprising at least eight gait trials. The database provides unprocessed (raw) and processed (ready-to-use) data, including three-dimensional GRF and two-dimensional COP signals during the stance phase. These data records offer new possibilities for future studies on human gait, e.g., the application as a reference set for the analysis of pathological gait patterns, or for automatic classification using machine learning. In the future, the database will be expanded continuously to obtain an even larger and well-balanced database with respect to age, sex, and other gait-specific factors.

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions

BACKGROUND

Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization.

CONTRIBUTION

This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging.

Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification

Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.

Slow walking synergies reveal a functional role for arm swing asymmetry in healthy adults: A principal component analysis with relation to mechanical work

INTRODUCTION

The purpose of this study was to reveal a functional role for arm-swing asymmetry during gait in healthy adults. To this end, the primary aim was to investigate the role of neuromuscular control on the asymmetry of propulsive and collision joint work at either end of the double-support phase (W_DS) in the context of sidedness. The secondary aim was to investigate the effect of neuromuscular control on propulsive and collision joint work at either end of the single-support phase (W_SS) in the context of arm-swing asymmetry.

METHODS

Slow -walking trials of 25 participants were analysed using principal component analysis to generate movement synergies (PM_k). Independent variables included the tightness of neuromuscular control (N₁) formulated from the first PM_k and the directional Arm-swing asymmetry index (dASI). Dependent variables included the difference between double-support collision and propulsive joint work (W_DS) and a ratio consisting of the difference between single-support collision and propulsive work of both sides (W_SS). A linear mixed-effects model was utilized for aim 1 while a multiple linear regression analysis was undertaken for aim 2.

RESULTS

Healthy adult gait was accompanied by a left-side dominant arm-swing on average. For aim 1, N₁ demonstrated a significant negative effect on W_DS while sidedness had a negative direct effect and positive indirect effect through N₁ on W_DS. The most notable finding was the interaction between dASI and N₁ which demonstrated a highly significant positive effect on W_SS.

INTERPRETATION

Evidence was put forward that arm-swing asymmetry during gait is related to footedness among healthy adults. Future studies should look to formally confirm this finding.

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.

Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes

Human motion capture is commonly used in various fields, including sport, to analyze, understand, and synthesize kinematic and kinetic data. Specialized computer vision and marker-based optical motion capture techniques constitute the gold-standard for accurate and robust human motion capture. The dataset presented consists of recordings of 37 Kyokushin karate athletes of different ages (children, young people, and adults) and skill levels (from 4th dan to 9th kyu) executing the following techniques: reverse lunge punch (Gyaku-Zuki), front kick (Mae-Geri), roundhouse kick (Mawashi-Geri), and spinning back kick (Ushiro-Mawashi-Geri). Each technique was performed approximately three times per recording (i.e., to create a single data file), and under three conditions where participants kicked or punched (i) in the air, (ii) a training shield, or (iii) an opponent. Each participant undertook a minimum of two trials per condition. The data presented was captured using a Vicon optical motion capture system with Plug-In Gait software. Three dimensional trajectories of 39 reflective markers were recorded. The resultant dataset contains a total of 1,411 recordings, with 3,229 single kicks and punches. The recordings are available in C3D file format. The dataset provides the opportunity for kinematic analysis of different combat sport techniques in attacking and defensive situations.