Letter to the editor

Scaling gait data to body size

A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m2) is presented and publicly shared for three walking speed conditions.

Raw and processed data are presented for each subject separately and for each walking speed, including data of every single step of both legs. The subject demographics and results from the physical examination are also presented which allows researchers and clinicians to create a self-selected reference group based on specific demographics. Besides the data per individual, data are also presented in age and gender groups. This provides a quick overview of healthy gait parameters which is relevant for use in clinical practice.

Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual industrial environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model [1], [2].

Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5m/s and every second the speed was increased with 0.01 m/s until the preferred speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded.

The 3D gait data was collected using the D-flow CAREN software. For each subject, raw data of each walking speed condition is provided in .mox files, including the output from the model such as subject data (e.g. gender, body mass, knee and ankle width), center of mass (CoM), marker and force data, kinematic data (joint angles) and kinetic data (joint moments, ground reaction forces (GRFs) and joint powers) for each single step of both legs. Unfiltered and filtered data are included. C3D files with raw marker and GRF data were recorded in Nexus (Vicon software, version 2.8.1) and are available upon request.

Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to .xls files. For each adult and for each walking speed, an .xls file was created, containing spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of each step of both legs. Overview files per walking speed condition are created in .xls, presenting the averaged gait parameters (calculated as average over all valid steps) of every subject. The processed data is also presented and visualized per gender for different age groups (18-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years, ≥70 years). This can serve as normative data for treadmill based 3D gait analyses in adults, applicable for clinical and research purposes. Data is available at OSF.io (https://osf.io/t72cw/).

This study employs 3D gait analysis to investigate normal gait patterns in individuals afflicted with knee Osteoarthritis (OA). Focusing on the adult osteoarthritic population, the research aims to establish reference values for joint angles, temporospatial parameters, Gait Profile Score (GPS), and Movement Analysis Profile (MAP) collected concurrently along a standardized walking path. Furthermore, the study delves into potential variations linked to gender and OA severity, comparing gait parameters between male and female participants and among individuals with grade 3 and grade 4 OA.

The study involved 34 adults with a mean age of 68.6 ± 5.75 years, all experiencing OA knees and awaiting Total Knee Arthroplasty (TKA). Utilizing Qualisys Motion capture system, 3D gait analysis was conducted. Data were processed through Visual 3D C-Motion Software.

Gait analysis revealed noteworthy differences between genders for various parameters, including stance time, GPS, MAP of the hip, and joint angle for the sagittal plane (ankle), coronal plane (knee), and transverse plane (hip and knee). Moreover, significant differences were observed between grade 3 and grade 4 OA knees in MAP and for the transverse plane joint angle (ankle).

This gait analysis study sheds light on distinctive gait patterns in the adult osteoarthritic population. The identified variations in temporospatial parameters, joint angles, GPS, and MAP provide valuable reference values for individuals suffering from knee OA. The observed differences between genders and across different OA severity grades emphasize the need for personalized approaches in managing knee OA and planning interventions like TKA.

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 0.6–2.0 m s⁻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 1.0–1.6 m s⁻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.

Studies of human locomotion have observed asymmetries in lower-limb kinematics, especially at the more distal joints. However, it is unclear whether these asymmetries are related to functional differences between the dominant and non-dominant limb. This study aimed to determine the effect of lower-limb dominance on foot kinematics during human locomotion. Range of motion for the metatarsophalangeal joint (MPJ) and medial longitudinal arch (MLA), as well as time duration of windlass mechanism engagement, were recorded from healthy young adults (N = 12) across a range of treadmill walking and running speeds. On the group level, there were no differences in MPJ or MLA range of motion, or windlass engagement timing, between the dominant and non-dominant limb (p > 0.05). While not explained by limb dominance, between-limb differences in MPJ and MLA ranges of motion were observed for individual participants on the order of ∼2–6°, which could be clinically relevant or impact interpretation of research data.

As people age there is a proximal shift of joint moment generation from ankle plantarflexion and knee extension toward hip extension and flexion moments. This age-related redistribution has been documented in the context of propulsive force generation during the push-off phase with less evidence in the context of weight bearing. Additionally, these sagittal plane joint moments have been a primary focus of studies though the hip frontal plane moment also contributes to vertical support but has received less attention. Furthermore, how aging affects the relationships between changes in sagittal and frontal joint moments and changes in vertical support force as a function of walking speed remains unclear

How does aging affect the contributions of sagittal and frontal plane joint moments to weight-bearing across different walking speeds?

Gait analysis was performed on 24 young and 17 healthy older subjects walked on the treadmill at their preferred and 30 % faster speeds. Stepwise linear regression analysis was performed to determine the joint moments that predict the peak amplitudes of the vertical ground reaction force (VGRF) across different walking speeds.

Hip abduction and knee extension moments were the primary contributors to leading limb weight-bearing in young, whereas hip extension moment was the primary contributor in older adults. Ankle plantarflexion moment was the main contributor to trailing limb weight-bearing in young and hip flexion moment was the main contributor in older adults. From preferred to faster walking speed changes in knee extension moment were the primary contributor to changes in the trailing limb weight-bearing in young whereas changes in hip extension moment were the primary contributor in olderadults.

These findings suggested that older and younger adults used different joint moment contributions to produce leading limb and trailing limb vertical support forces across different walking speeds.

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.

Are muscle activation signatures robust across different walking speeds?

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.

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 %.

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.