Lower limb sagittal gait kinematics can be predicted based on walking speed, gender, age and BMI

Effect of Data and Gap Characteristics on the Nonlinear Calculation of Motion During Locomotor Activities

This study investigated how data series length and gaps in human kinematic data impact the accuracy of Lyapunov exponents (LyE) calculations with and without cubic spline interpolation. Kinematic time series were manipulated to create various data series lengths (28% and 100% of original) and gap durations (0.05-0.20 s). Longer gaps generally resulted in significantly higher LyE% error values in each plane in noninterpolated data. During cubic spline interpolation, only the 0.20-second gap in frontal plane data resulted in a significantly higher LyE% error. Data series length did not significantly affect LyE% error in noninterpolated data. During cubic spline interpolation, sagittal plane LyE% errors were significantly higher at shorter versus longer data series lengths. These findings suggest that not interpolating gaps in data could lead to erroneously high LyE values and mischaracterization of movement variability. When applying cubic spline, a long gap length (0.20 s) in the frontal plane or a short sagittal plane data series length (1000 data points) could also lead to erroneously high LyE values and mischaracterization of movement variability. These insights emphasize the necessity of detailed reporting on gap durations, data series lengths, and interpolation techniques when characterizing human movement variability using LyE values.

A validation study demonstrating portable motion capture cameras accurately characterize gait metrics when compared to a pressure-sensitive walkway

Digital quantification of gait can be used to measure aging- and disease-related decline in mobility. Gait performance also predicts prognosis, disease progression, and response to therapies. Most gait analysis systems require large amounts of space, resources, and expertise to implement and are not widely accessible. Thus, there is a need for a portable system that accurately characterizes gait. Here, depth video from two portable cameras accurately reconstructed gait metrics comparable to those reported by a pressure-sensitive walkway. 392 research participants walked across a four-meter pressure-sensitive walkway while depth video was recorded. Gait speed, cadence, and step and stride durations and lengths strongly correlated (r > 0.9) between modalities, with root-mean-squared-errors (RMSE) of 0.04 m/s, 2.3 steps/min, 0.03 s, and 0.05-0.08 m for speed, cadence, step/stride duration, and step/stride length, respectively. Step, stance, and double support durations (gait cycle percentage) significantly correlated (r > 0.6) between modalities, with 5% RMSE for step and stance and 10% RMSE for double support. In an exploratory analysis, gait speed from both modalities significantly related to healthy, mild, moderate, or severe categorizations of Charleson Comorbidity Indices (ANOVA, Tukey's HSD, p < 0.0125). These findings demonstrate the viability of using depth video to expand access to quantitative gait assessments.

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.

Full-body kinematics and head stabilisation strategies during walking in patients with chronic unilateral and bilateral vestibulopathy

Chronic imbalance is a frequent and limiting symptom of patients with chronic unilateral and bilateral vestibulopathy. A full-body kinematic analysis of the movement of patients with vestibulopathy would provide a better understanding of the impact of the pathology on dynamic tasks such as walking. Therefore, this study aimed to investigate the global body movement during walking, its variability (assessed with the GaitSD), and the strategies to stabilise the head (assessed with the head Anchoring Index). The full-body motion capture data of 10 patients with bilateral vestibulopathy (BV), 10 patients with unilateral vestibulopathy (UV), and 10 healthy subjects (HS) walking at several speeds (slow, comfortable, and fast) were analysed in this prospective cohort study. We observed only a few significant differences between groups in parts of the gait cycle (shoulder abduction-adduction, pelvis rotation, and hip flexion-extension) during the analysis of kinematic curves. Only BV patients had significantly higher gait variability (GaitSD) for all three walking speeds. Head stabilisation strategies depended on the plan of motion and walking speed condition, but BV and UV patients tended to stabilise their head in relation to the trunk and HS tended to stabilise their head in space. These results suggest that GaitSD could be a relevant biomarker of chronic instability in BV and that the head Anchoring Index tends to confirm clinical observations of abnormal head-trunk dynamics in patients with vestibulopathy while walking.

Towards a comprehensive biomechanical assessment of the elderly combining in vivo data and in silico methods

The aging process is commonly accompanied by a general or specific loss of muscle mass, force and/or function that inevitably impact on a person's quality of life. To date, various clinical tests and assessments are routinely performed to evaluate the biomechanical status of an individual, to support and inform the clinical management and decision-making process (e.g., to design a tailored rehabilitation program). However, these assessments (e.g., gait analysis or strength measures on a dynamometer) are typically conducted independently from one another or at different time points, providing clinicians with valuable yet fragmented information. We hereby describe a comprehensive protocol that combines both in vivo measurements (maximal voluntary isometric contraction test, superimposed neuromuscular electrical stimulation, electromyography, gait analysis, magnetic resonance imaging, and clinical measures) and in silico methods (musculoskeletal modeling and simulations) to enable the full characterization of an individual from the biomechanical standpoint. The protocol, which requires approximately 4 h and 30 min to be completed in all its parts, was tested on twenty healthy young participants and five elderlies, as a proof of concept. The implemented data processing and elaboration procedures allowing for the extraction of several biomechanical parameters (including muscle volumes and cross-sectional areas, muscle activation and co-contraction levels) are thoroughly described to enable replication. The main parameters extracted are reported as mean and standard deviation across the two populations, to highlight the potential of the proposed approach and show some preliminary findings (which were in agreement with previous literature).

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.

An Individual Prosthesis Control Method with Human Subjective Choices

An intelligent lower-limb prosthesis can provide walking support and convenience for lower-limb amputees. Trajectory planning of prosthesis joints plays an important role in the intelligent prosthetic control system, which directly determines the performance and helps improve comfort when wearing the prosthesis. Due to the differences in physiology and walking habits, humans have their own walking mode that requires the prosthesis to consider the individual's demands when planning the prosthesis joint trajectories. The human is an integral part of the control loop, whose subjective feeling is important feedback information, as humans can evaluate many indicators that are difficult to quantify and model. In this study, trajectories were built using the phase variable method by normalizing the gait curve to a unified range. The deviations between the optimal trajectory and current were represented using Fourier series expansion. A gait dataset that contains multi-subject kinematics data is used in the experiments to prove the feasibility and effectiveness of this method. In the experiments, we optimized the subjects' gait trajectories from an average to an individual gait trajectory. By using the individual trajectory planning algorithm, the average gait trajectory can be effectively optimized into a personalized trajectory, which is beneficial for improving walking comfort and safety and bringing the prosthesis closer to intelligence.

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.

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.

Minimum number of inertial measurement units needed to identify significant variations in walk patterns of overweight individuals walking on irregular surfaces

Gait data collection from overweight individuals walking on irregular surfaces is a challenging task that can be addressed using inertial measurement unit (IMU) sensors. However, it is unclear how many IMUs are needed, particularly when body attachment locations are not standardized. In this study, we analysed data collected from six body locations, including the torso, upper and lower limbs, to determine which locations exhibit significant variation across different real-world irregular surfaces. We then used deep learning method to verify whether the IMU data recorded from the identified body locations could classify walk patterns across the surfaces. Our results revealed two combinations of body locations, including the thigh and shank (i.e., the left and right shank, and the right thigh and right shank), from which IMU data should be collected to accurately classify walking patterns over real-world irregular surfaces (with classification accuracies of 97.24 and 95.87%, respectively). Our findings suggest that the identified numbers and locations of IMUs could potentially reduce the amount of data recorded and processed to develop a fall prevention system for overweight individuals.

Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing

Goal: Accounting for gait individuality is important to positive outcomes with wearable robots, but manually tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can possibly be made to predict gait individuality in unobserved conditions. Methods: Kinematic individuality-how one person's joint angles differ from the group-is quantified for every subject, joint, ambulation mode (walking, running, stair ascent, and stair descent), and intramodal task (speed, incline) in an open-access dataset with 10 able-bodied subjects. Four N-way ANOVAs test how prediction methods affect the fit to experimental data between and within ambulation modes. We test whether walking individuality (measured at a single speed on level ground) carries across modes, or whether a mode-specific prediction (based on a single task for each mode) is significantly more effective. Results: Kinematic individualization improves fit across joint and task if we consider each mode separately. Across all modes, tasks, and joints, modal individualization improved the fit in 81% of trials, improving the fit on average by 4.3[Formula: see text] across the gait cycle. This was statistically significant at all joints for walking and running, and half the joints for stair ascent/descent. Conclusions: For walking and running, kinematic individuality can be easily generalized within mode, but the trends are mixed on stairs depending on joint.

Can different training methods reduce the kinematic risk factors of ACL injuries in children?

According to the research, a lack of neuromuscular control is a major cause of non-contact anterior cruciate ligament (ACL) injury during locomotion. This study aimed to determine the influence of various prescriptive and Constrained Led Approach (CLA) training approaches on lower extremity kinematics and stride length in children aged 3-5 years old while walking and running. Thirty-six children with a mean age of 4.79 years were separated into three groups: 1- prescriptive training group (n = 10), 2- CLA training group (n = 11), and 3- Control group (n = 10). The kinematics of the hip, knee and ankle joints in the sagittal plane at the moment of heel contact and toe-off were recorded before and after six weeks of intervention. According to the MANOVA, there was no statistically significant difference between the two training techniques in the joint angles at heel contact and toe-off during walking and running after intervention (p ≥ 0.05). However, there was a significant difference in the kinematic characteristics of walking and running between the training and the control groups (p ≤ 0.05). The two training techniques showed a statistically significant difference in stride length during running (p ≤ 0.05). The results indicated that prescriptive and CLA training are effective at altering the kinematics and distance factors underlying children's walking and running abilities.

Evaluating the difference in walk patterns among normal-weight and overweight/obese individuals in real-world surfaces using statistical analysis and deep learning methods with inertial measurement unit data

Unusual walk patterns may increase individuals' risks of falling. Anthropometric features of the human body, such as the body mass index (BMI), influences the walk patterns of individuals. In addition to the BMI, uneven walking surfaces may cause variations in the usual walk patterns of an individual that will potentially increase the individual's risk of falling. The objective of this study was to statistically evaluate the variations in the walk patterns of individuals belonging to two BMI groups across a wide range of walking surfaces and to investigate whether a deep learning method could classify the BMI-specific walk patterns with similar variations. Data collected by wearable inertial measurement unit (IMU) sensors attached to individuals with two different BMI were collected while walking on real-world surfaces. In addition to traditional statistical analysis tools, an advanced deep learning-based neural network was used to evaluate and classify the BMI-specific walk patterns. The walk patterns of overweight/obese individuals showed a greater correlation with the corresponding walking surfaces than the normal-weight population. The results were supported by the deep learning method, which was able to classify the walk patterns of overweight/obese (94.8 ± 4.5%) individuals more accurately than those of normal-weight (59.4 ± 23.7%) individuals. The results suggest that application of the deep learning method is more suitable for recognizing the walk patterns of overweight/obese population than those of normal-weight individuals. The findings from the study will potentially inform healthcare applications, including artificial intelligence-based fall assessment systems for minimizing the risk of fall-related incidents among overweight and obese individuals.

Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.

Varying Joint Patterns and Compensatory Strategies Can Lead to the Same Functional Gait Outcomes: A Case Study

This paper analyses joint-space walking mechanisms and redundancies in delivering functional gait outcomes. Multiple biomechanical measures are analysed for two healthy male adults who participated in a multi-factorial study and walked during three sessions. Both participants employed varying intra- and inter-personal compensatory strategies (e.g., vaulting, hip hiking) across walking conditions and exhibited notable gait pattern alterations while keeping task-space (functional) gait parameters invariant. They also preferred various levels of asymmetric step length but kept their symmetric step time consistent and cadence-invariant during free walking. The results demonstrate the importance of an individualised approach and the need for a paradigm shift from functional (task-space) to joint-space gait analysis in attending to (a)typical gaits and delivering human-centred human-robot interaction.

A neuromuscular model of human locomotion combines spinal reflex circuits with voluntary movements

Existing models of human walking use low-level reflexes or neural oscillators to generate movement. While appropriate to generate the stable, rhythmic movement patterns of steady-state walking, these models lack the ability to change their movement patterns or spontaneously generate new movements in the specific, goal-directed way characteristic of voluntary movements. Here we present a neuromuscular model of human locomotion that bridges this gap and combines the ability to execute goal directed movements with the generation of stable, rhythmic movement patterns that are required for robust locomotion. The model represents goals for voluntary movements of the swing leg on the task level of swing leg joint kinematics. Smooth movements plans towards the goal configuration are generated on the task level and transformed into descending motor commands that execute the planned movements, using internal models. The movement goals and plans are updated in real time based on sensory feedback and task constraints. On the spinal level, the descending commands during the swing phase are integrated with a generic stretch reflex for each muscle. Stance leg control solely relies on dedicated spinal reflex pathways. Spinal reflexes stimulate Hill-type muscles that actuate a biomechanical model with eight internal joints and six free-body degrees of freedom. The model is able to generate voluntary, goal-directed reaching movements with the swing leg and combine multiple movements in a rhythmic sequence. During walking, the swing leg is moved in a goal-directed manner to a target that is updated in real-time based on sensory feedback to maintain upright balance, while the stance leg is stabilized by low-level reflexes and a behavioral organization switching between swing and stance control for each leg. With this combination of reflex-based stance leg and voluntary, goal-directed control of the swing leg, the model controller generates rhythmic, stable walking patterns in which the swing leg movement can be flexibly updated in real-time to step over or around obstacles.

The effects of learning with various noise on Gait Kinematics in 3-to-5-year-old children: a randomized controlled trial

Background

Lack of the neuromuscular control during locomotion in the knee joint leads to an increased risk of anterior cruciate ligament (ACL) injury in children. Hence, we aimed to explore the effects of a repetitive, model-oriented, and self-organized approach on lower limb kinematics during gait in children.

Methods

In randomized controlled trial, 36 children with 4 ± 0.79 years of age from the children gym were randomly (a lottery method) allocated into three groups, including (1) the model-oriented (n = 10), (2) Differential Learning (n = 11), and (3) control (n = 10) groups. Kinematic data of hip, knee, and ankle joints in the sagittal plane were recorded by a GoPro camera at the moments of heel-ground contact and toe-off the ground before and after a 6-week intervention (two sessions per week).

Results

The results indicate a 35% post-intervention increase of ankle dorsiflexion (95% CI: − 5.63 _ − 0.96) in the moment of heel-ground contact in the model-oriented group; however, knee flexion (95% CI: − 1.05 _ 8.34) and hip flexion (95% CI: 3.01 _ 11.78) were respectively decreased by 20% and 20%. After the intervention, moreover, ankle plantar flexion (95% CI: − 9.18 _ − 2.81) and hip extension (95% CI: − 12.87 _ − 3.72) have respectively increased by 37% and 37%, while knee flexion (95% CI: 3.49 _ 11.30) showed a %16 decrease in the moment of toe off the ground. As for the Differential Learning group, ankle dorsiflexion (95% CI: − 5.19 _ − 1.52) increased by 33%, and knee (95% CI: 0.60 _ 5.76) and hip flexion (95% CI: 2.15 _ 7.85) respectively decreased by 17% and 17% at the moment of the heel-ground contact following the intervention. At toe lifting off the ground, the plantar flexion (95% CI: − 7.77 _ − 2.77) increased by 35%, knee flexion (95% CI: 2.17 _ 7.27) decreased to 14%, and hip extension (95% CI: − 9.98 _ − 4.20) increased by %35 following the intervention for the Differential Learning group subjects. Based on the results obtained from the one-way ANOVA, there was a significant difference between these groups and the control group in all kinematic gait variables (p ≤ 0.05). However, no statistically significant differences were found between the two experimental groups.

Conclusions

The results implied that the model-oriented repetitive and the self-organized Differential Learning approach were both appropriate to alter the kinematic gait pattern in the 3–5-year-old children. Previous research has almost exclusively recommended a model-oriented approach to change kinematic patterns and preventing non-contact motor injuries. However, the present study showed that the Differential Learning approach can help children to achieve the same goal by continuously changing environments and stimulating challenges.

Trial registration: Current Controlled Trials using the IRCT website with ID number of, IRCT20130109012078N5 “Prospectively registered” at 14/5/2021.

Feasibility evaluation of a dual-mode ankle exoskeleton to assist and restore community ambulation in older adults

Background

Age-related deficits in plantar flexor muscle function during the push-off phase of walking likely contribute to the decline in mobility that affects many older adults. Isolated strengthening of the plantar flexor muscles has failed to improve push-off power or walking economy in this population. New mobility aids and/or functional training interventions may help slow or prevent ambulatory decline in the elderly.

Objective

The overarching objective of this study was to explore the feasibility of using an untethered, dual-mode ankle exoskeleton for treating walking disability in the elderly; testing the device in assistance mode as a mobility aid to reduce energy consumption, and as a resistive gait training tool to facilitate functional recruitment of the plantar flexor muscles.

Methods

We recruited 6 older adults between the ages of 68 to 83 years to evaluate the feasibility of the dual-mode exoskeleton across two visits. On the first visit, we quantified acute metabolic and neuromuscular adaption to ankle exoskeleton assistance during walking in older adults, and subsequently determined if higher baseline energy cost was related to an individual's potential to benefit from untethered assistance. On the second visit, we validated the potential for push-off phase ankle resistance combined with plantar pressure biofeedback to facilitate functional utilization of the ankle plantar flexors during walking. We also conducted a twelve-session ankle resistance training protocol with one pilot participant to explore the effects of gait training with wearable ankle resistance on mobility and plantar flexor strength.

Results

Participants reached the lowest net metabolic power, soleus variance ratio, and soleus iEMG at 6.6 ± 1.6, 19.8 ± 1.6, and 5.8 ± 4.9 minutes, respectively, during the 30-minute exoskeleton assistance adaptation trial. Four of five participants exhibited a reduction (up to 19%) in metabolic power during walking with assistance relative to baseline, but there was no group-level change. Participants who had greater baseline metabolic power exhibited a greater reduction during walking with assistance. Walking with resistance increased stance-phase soleus iEMG by 18 - 186% and stance-phase average positive ankle power by 9 - 88% compared to baseline. Following ankle resistance gait training, the participant exhibited a 5% increase in self-selected walking speed, a 15% increase in fast walking speed, a 36% increase in 6-min-walk-test distance, and a 31% increase in plantar flexor strength compared to pre-intervention measurements.

Conclusions

Our results suggest that dual-mode ankle exoskeletons appear highly applicable to treating plantar flexor dysfunction in the elderly, with assistance holding potential as a mobility aid and resistance holding potential as a functional gait training tool. We used an untethered design to maximize the relevance of this for informing the design of intervention studies that may take place at home and in the community to improve mobility and quality of life in older adults. Future studies with larger sample sizes are recommended to expand on the results of this feasibility investigation.

Clinical and objective gait outcomes remained stable seven years after total knee arthroplasty: A prospective longitudinal study of 28 patients

BACKGROUND

There is a paucity of data on mid to long-term gait outcomes after total knee arthroplasty. The aims of this longitudinal study were: to assess the evolution of both clinical and gait outcomes before and up to seven years after primary total knee arthroplasty (TKA) in a cohort of patients with knee osteoarthritis.

METHODS

This study included 28 patients evaluated before and up to seven years after primary TKA with both gait analysis and patient reported outcomes; of these, 20 patients were evaluated one year after surgery as well. Kinematic outcomes during gait (gait velocity, dimensionless gait veolicity, maximal knee flexion and knee range of motion), pain relief, Western Ontario and MacMaster Osteoarthritis Index (WOMAC), quality of life and patient satisfaction were assessed and compared at each visit with the paired Wilcoxon signed rank test (p < 0.05).

RESULTS

The significant improvement achieved at one year after TKA was stable up to seven years after surgery, with all clinical and kinematic outcomes unchanged, except for gait velocity, with a significant decrease over time (1.3 (1.1-1.4) m/s one year after TKA versus 1.0 (0.9-1.1) m/s, p < 0.05 up to seven years after).

CONCLUSION

Patients with knee osteoarthritis significantly improve their clinical and kinematic outcomes at one year postoperatively and maintain the gain up to seven years after primary TKA, except for gait velocity which decreases over time, most likely along with ageing.

Effects of a supervised therapeutic exercise program on musculoskeletal health and gait in patients with haemophilia: A pilot study

INTRODUCTION

Episodes of bleeding in patients with haemophilia (PwH) are associated with haemophilic arthropathy, limitations in physical performance, reduced quality of life (QoL), and gait disorders.

AIM

This non-randomized, controlled, interventional, prospective, single-centre pilot study aimed to assess the effects of an 8-week supervised therapeutic exercise program on musculoskeletal health, gait kinematic parameters (GKP), functional capacity, and QoL in adult PwH.

METHODS

Nineteen PwH were allocated to an exercise group (n = 10) or a control group (n = 9). The patients in the exercise group followed an 8-week supervised therapeutic exercise program. The Haemophilia Joint Health Score (HJHS), a two-dimensional video-based gait kinematic analysis (2D-GKA), the 6-min walking test (6MWT), and the Haemophilia Quality of Life Questionnaire for Adults (Haem-A-Qol) were used as the outcome measures at baseline, after the exercise program (at the 8th week), and at the 6th-month follow-up.

RESULTS

A significant improvement was observed in the exercise group in the HJHS-Total and Haem-A-Qol Total scores and the 6MWT value after the exercise program. Moreover, the 2D-GKA revealed improvement in most of the GKP (knee extension during the midstance and late swing phases, ankle dorsiflexion during the midstance phase, and ankle plantar flexion during the preswing phase). However, the gain obtained by the exercise program was not maintained at the 6th-month follow-up for the HJHS-Total and Hem-A-QoL-Total scores and GKP.

CONCLUSION

The 8-week supervised therapeutic exercise program was successful in achieving improvement in joint health, GKP, functional capacity, and QoL in PwH.

Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping-Are Bone Geometry Predictors Helpful?

Purpose: Statistical shape methods have proven to be useful tools in providing statistical predications of several clinical and biomechanical features as to analyze and describe the possible link with them. In the present study, we aimed to explore and quantify the relationship between biometric features derived from imaging data and model-derived kinematics. Methods: Fifty-seven healthy males were gathered under strict exclusion criteria to ensure a sample representative of normal physiological conditions. MRI-based bone geometry was established and subject-specific musculoskeletal simulations in the Anybody Modeling System enabled us to derive personalized kinematics. Kinematic and shape findings were parameterized using principal component analysis. Partial least squares regression and canonical correlation analysis were then performed with the goal of predicting motion and exploring the possible association, respectively, with the given bone geometry. The relationship of hip flexion, abduction, and rotation, knee flexion, and ankle flexion with a subset of biometric features (age, length, and weight) was also investigated. Results: In the statistical kinematic models, mean accuracy errors ranged from 1.60° (race cycling) up to 3.10° (lunge). When imposing averaged kinematic waveforms, the reconstruction errors varied between 4.59° (step up) and 6.61° (lunge). A weak, yet clinical irrelevant, correlation between the modes describing bone geometry and kinematics was observed. Partial least square regression led to a minimal error reduction up to 0.42° compared to imposing gender-specific reference curves. The relationship between motion and the subject characteristics was even less pronounced with an error reduction up to 0.21°. Conclusion: The contribution of bone shape to model-derived joint kinematics appears to be relatively small and lack in clinical relevance.

Sensory modulation of gait characteristics in human locomotion: A neuromusculoskeletal modeling study

The central nervous system of humans and other animals modulates spinal cord activity to achieve several locomotion behaviors. Previous neuromechanical models investigated the modulation of human gait changing selected parameters belonging to CPGs (Central Pattern Generators) feedforward oscillatory structures or to feedback reflex circuits. CPG-based models could replicate slow and fast walking by changing only the oscillation’s properties. On the other hand, reflex-based models could achieve different behaviors through optimizations of large dimensional parameter spaces. However, they could not effectively identify individual key reflex parameters responsible for gait characteristics’ modulation. This study investigates which reflex parameters modulate the gait characteristics through neuromechanical simulations. A recently developed reflex-based model is used to perform optimizations with different target behaviors on speed, step length, and step duration to analyze the correlation between reflex parameters and their influence on these gait characteristics. We identified nine key parameters that may affect the target speed ranging from slow to fast walking (0.48 and 1.71 m/s) as well as a large range of step lengths (0.43 and 0.88 m) and step duration (0.51, 0.98 s). The findings show that specific reflexes during stance significantly affect step length regulation, mainly given by positive force feedback of the ankle plantarflexors’ group. On the other hand, stretch reflexes active during swing of iliopsoas and gluteus maximus regulate all the gait characteristics under analysis. Additionally, the results show that the hamstrings’ group’s stretch reflex during the landing phase is responsible for modulating the step length and step duration. Additional validation studies in simulations demonstrated that the modulation of identified reflexes is sufficient to regulate the investigated gait characteristics. Thus, this study provides an overview of possible reflexes involved in modulating speed, step length, and step duration of human gaits.

This study investigates the possible reflex parameters that the central nervous system could use to modulate human locomotion. Specifically, we target the modulation of three gait characteristics: speed, step length, and step duration. We utilize human locomotion simulations with a previously developed reflex-based model and perform multiple optimizations ranging targeting low to high values of the three gait characteristics investigated. From the data acquired in optimizations, we investigate which reflex parameter correlates most with the gait characteristics changes. We identified nine key reflex parameters affecting gait modulation, performed validation experiments, and verified that the optimization of key reflex parameters alone could generate modulation in the studied locomotion behaviors. Kinematics, ground reaction forces, and muscle activity obtained in simulations show similarities with past experimental studies on gait modulation. Therefore, the identified parameters could potentially be used by the nervous system to regulate locomotion behaviors in a task-dependent manner. Other circuits not modeled in this study could play a crucial role in gait modulation, and further investigations should be done in the co-optimization of feedforward and feedback circuits.

Continuous Classification of Locomotion in Response to Task Complexity and Anticipatory State

Objective

Intent recognition in lower-extremity assistive devices (e.g., prostheses and exoskeletons) is typically limited to either recognition of steady-state locomotion or changes of terrain (e.g., level ground to stair) occurring in a straight-line path and under anticipated condition. Stability is highly affected during non-steady changes of direction such as cuts especially when they are unanticipated, posing high risk of fall-related injuries. Here, we studied the influence of changes of direction and user anticipation on task recognition, and accordingly introduced classification schemes accommodating such effects.

Methods

A linear discriminant analysis (LDA) classifier continuously classified straight-line walking, sidestep/crossover cuts (single transitions), and cuts-to-stair locomotion (mixed transitions) performed under varied task anticipatory conditions. Training paradigms with varying levels of anticipated/unanticipated exposures and analysis windows of size 100–600 ms were examined.

Results

More accurate classification of anticipated relative to unanticipated tasks was observed. Including bouts of target task in the training data was necessary to improve generalization to unanticipated locomotion. Only up to two bouts of target task were sufficient to reduce errors to <20% in unanticipated mixed transitions, whereas, in single transitions and straight walking, substantial unanticipated information (i.e., five bouts) was necessary to achieve similar outcomes. Window size modifications did not have a significant influence on classification performance.

Conclusion

Adjusting the training paradigm helps to achieve classification schemes capable of adapting to changes of direction and task anticipatory state.

Significance

The findings could provide insight into developing classification schemes that can adapt to changes of direction and user anticipation. They could inform intent recognition strategies for controlling lower-limb assistive to robustly handle “unknown” circumstances, and thus deliver increased level of reliability and safety.

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.

Human Gait Analysis and Prediction Using the Levenberg-Marquardt Method

A high-accuracy gait data prediction model can be used to design prosthesis and orthosis for people having amputations or ailments of the lower limb. The objective of this study is to observe the gait data of different subjects and design a neural network to predict future gait angles for fixed speeds. The data were recorded via a Biometrics goniometer, while the subjects were walking on a treadmill for 20 seconds each at 2.4 kmph, 3.6 kmph, and 5.4 kmph. The data were then imported into Matlab, filtered to remove movement artifacts, and then used to design a neural network with 60% data for training, 20% for validation, and remaining 20% for testing using the LevenbergMarquardt method. The mean-squared error for all the cases was in the order of 10-3 or lower confirming that our method is correct. For further comparison, we randomly tested the neural network function with untrained data and compared the expected output with actual output of the neural network function using Pearson's correlation coefficient and correlation plots. We conclude that our framework can be successfully used to design prosthesis and orthosis for lower limb. It can also be used to validate gait data and compare it to expected data in rehabilitation engineering.

Predicting Temporal Gait Kinematics: Anthropometric Characteristics and Global Running Pattern Matter

Equations predicting stride frequency (SF) and duty factor (DF) solely based on running speed have been proposed. However, for a given speed, kinematics vary depending on the global running pattern (GRP), i.e., the overall individual movement while running, which depends on the vertical oscillation of the head, antero-posterior motion of the elbows, vertical pelvis position at ground contact, antero-posterior foot position at ground contact, and strike pattern. Hence, we first verified the validity of the aforementioned equations while accounting for GRP. Kinematics during three 50-m runs on a track (n = 20) were used with curve fitting and linear mixed effects models. The percentage of explained variance was increased by ≥133% for DF when taking into account GRP. GRP was negatively related to DF (p = 0.004) but not to SF (p = 0.08), invalidating DF equation. Second, we assessed which parameters among anthropometric characteristics, sex, training volume, and GRP could relate to SF and DF in addition to speed, using kinematic data during five 30-s runs on a treadmill (n = 54). SF and DF linearly increased and quadratically decreased with speed (p < 0.001), respectively. However, on an individual level, SF was best described using a second-order polynomial equation. SF and DF showed a non-negligible percentage of variance explained by random effects (≥28%). Age and height were positively and negatively related to SF (p ≤ 0.05), respectively, while GRP was negatively related to DF (p < 0.001), making them key parameters to estimate SF and DF, respectively, in addition to speed.

Gait Variability and Relationships With Fear, Avoidance, and Pain in Adolescents With Chronic Pain

OBJECTIVE

Some children with chronic pain struggle with fear of pain, avoidance behaviors, and associated disability; however, movement adaptations in the context of chronic pain in childhood is virtually unknown. Variability in adaptive movement responses previously observed between individuals might be largely explained by the presence of problematic psychological drivers (eg, fear, avoidance). The goals of this study were to quantify the variability of gait and examine relationships among pain, fear, avoidance, function (perceived and objective), and gait variability.

METHODS

This study used a cross-sectional design. Eligible patients were between 8 and 17 years of age and had musculoskeletal, neuropathic, or headache pain that was not due to acute trauma (eg, active sprain) or any specific or systemic disease. Participants completed the Numeric Pain Rating Scale, Fear of Pain Questionnaire (FOPQ), Functional Disability Inventory, and 6-Minute Walk Test and received kinematic gait analysis. Relationships were analyzed among these measures, and the self-report and functional measures were examined to determine whether they predicted gait variability (GaitSD).

RESULTS

The 16 participants who were evaluated (13.8 [SD = 2.2] years of age; 13 female) had high Numeric Pain Rating Scale scores (6.2 [SD = 2.1]), FOPQ-Fear scores (25.9 [SD = 12.1]), FOPQ-Avoidance scores (22.8 [SD = 10.2]), and Functional Disability Inventory scores (28.6 [SD = 9.4]) and low 6-Minute Walk Test distance (437.1 m [SD = 144.6]). Participants had greater GaitSD than age-predicted norms. Fear was related to self-selected GaitSD, and avoidance was related to both self-selected and standardized GaitSD. Avoidance predicted 43% and 47% of the variability in self-selected and standardized GaitSD, respectively.

CONCLUSION

GaitSD was significantly related to both fear of pain and avoidance behaviors, suggesting the interplay of these psychological drivers with movement. FOPQ-Avoidance was robust in accounting for GaitSD.

IMPACT

This study offers preliminary evidence in understanding movement adaptations associated with adolescents with chronic pain. They may lend to more directed interventions.

Analysis of Continuously Varying Kinematics for Prosthetic Leg Control Applications

Powered prosthetic legs can improve the quality of life for people with transfemoral amputations by providing net positive work at the knee and ankle, reducing the effort required from the wearer, and making more tasks possible. However, the controllers for these devices use finite state machines that limit their use to a small set of pre-defined tasks that require many hours of tuning for each user. In previous work, we demonstrated that a continuous parameterization of joint kinematics over walking speeds and inclines provides more accurate predictions of reference kinematics for control than a finite state machine. However, our previous work did not account for measurement errors in gait phase, walking speed, and ground incline, nor subject-specific differences in reference kinematics, which occur in practice. In this work, we conduct a pilot experiment to characterize the accuracy of speed and incline measurements using sensors onboard our prototype prosthetic leg and simulate phase measurements on ten able-bodied subjects using archived motion capture data. Our analysis shows that given demonstrated accuracy for speed, incline, and phase estimation, a continuous parameterization provides statistically significantly better predictions of knee and ankle kinematics than a comparable finite state machine, but both methods' primary source of predictive error is subject deviation from average kinematics.

Use of Functional Linear Models to Detect Associations between Characteristics of Walking and Continuous Responses Using Accelerometry Data

Various methods exist to measure physical activity. Subjective methods, such as diaries and surveys, are relatively inexpensive ways of measuring one’s physical activity; however, they are prone to measurement error and bias due to self-reporting. Wearable accelerometers offer a non-invasive and objective measure of one’s physical activity and are now widely used in observational studies. Accelerometers record high frequency data and each produce an unlabeled time series at the sub-second level. An important activity to identify from the data collected is walking, since it is often the only form of activity for certain populations. Currently, most methods use an activity summary which ignores the nuances of walking data. We propose methodology to model specific continuous responses with a functional linear model utilizing spectra obtained from the local fast Fourier transform (FFT) of walking as a predictor. Utilizing prior knowledge of the mechanics of walking, we incorporate this as additional information for the structure of our transformed walking spectra. The methods were applied to the in-the-laboratory data obtained from the Developmental Epidemiologic Cohort Study (DECOS).

Predicting Individualized Joint Kinematics over a Continuous Range of Slopes and Speeds

Individuality in clinical gait analysis is often quantified by an individual's kinematic deviation from the norm, but it is unclear how these deviations generalize across different walking speeds and ground slopes. Understanding individuality across tasks has important implications in the tuning of prosthetic legs, where clinicians have limited time and resources to personalize the kinematic motion of the leg to therapeutically enhance the wearer's gait. This study seeks to determine an efficient way to predictively model an individual's kinematics over a continuous range of slopes and speeds given only one personalized task at level ground. We were able to predict the kinematics of able-bodied individuals at a wide variety of conditions that were not specifically tuned. Applied to 10 human subjects, the individualization method reduced the RMSE between the model and subject's kinematics over all tasks by an average of 2% (max 52%) at the ankle, 27% (max 59%) at the knee, and 45% (max 83%) at the hip. Our results indicate that knowing how an individual subject differs from the average subject at level ground alone is enough information to improve kinematic predictions across all tasks. This research offers a new method for personalizing robotic prosthetic legs over a variety of tasks without the need of an engineer, which could make these complex devices more clinically viable.

Walking Speed and Maximal Knee Flexion During Gait After Total Knee Arthroplasty: Minimal Clinically Important Improvement Is Not Determinable; Patient Acceptable Symptom State Is Potentially Useful

BACKGROUND

Total knee arthroplasty (TKA) is the operation of choice in patients with end-stage knee osteoarthritis (OA). Up to 1 in 5 patients still encounter functional limitations after TKA, partly explaining patient dissatisfaction. Which gait ability to target after TKA remains unclear. To determine whether Minimal Clinical Important Improvement (MCII) or Patient Acceptable Symptom State (PASS) values could be derived from gait parameters recorded in patients with TKA. And, if so, to define those values.

METHODS

In this ancillary study, we retrospectively analyzed gait parameters of patients scheduled for a unilateral TKA between 2011 and 2013. We investigated MCII and PASS values for walking speed and maximal knee flexion using anchor-based methods: 5 anchoring questions based on perceived body function and patients' satisfaction.

RESULTS

Over the study period, 79 patients performed a clinical gait analysis the week before and 1 year after surgery, and were included in the present study. All clinical and gait parameters improved 1 year after TKA. Nevertheless, changes in gait outcomes were not associated with perceived body function or patients' satisfaction, precluding any MCII estimation in gait parameters. PASS values, however, could be determined as 1.2 m/s for walking speed and 50° for maximal knee flexion.

CONCLUSION

In this study, we found that MCII and PASS values are not necessarily determinable for gait parameters after TKA in patients with end-stage OA. Using anchor questions based on perceived body function and patient's satisfaction, MCII could not be defined while PASS values were potentially useful.

LEVEL OF EVIDENCE

Level III.

Influence of spino-pelvic and postural alignment parameters on gait kinematics

INTRODUCTION

Postural alignment is altered with spine deformities that might occur with age. Alteration of spino-pelvic and postural alignment parameters are known to affect daily life activities such as gait. It is still unknown how spino-pelvic and postural alignment parameters are related to gait kinematics.

RESEARCH QUESTION

To assess the relationships between spino-pelvic/postural alignment parameters and gait kinematics in asymptomatic adults.

METHODS

134 asymptomatic subjects (aged 18-59 years) underwent 3D gait analysis, from which kinematics of the pelvis and lower limbs were extracted in the 3 planes. Subjects then underwent full-body biplanar X-rays, from which skeletal 3D reconstructions and spino-pelvic and postural alignment parameters were obtained such as sagittal vertical axis (SVA), center of auditory meatus to hip axis plumbline (CAM-HA), thoracic kyphosis (TK) and radiologic pelvic tilt (rPT). In order to assess the influence of spino-pelvic and postural alignment parameters on gait kinematics a univariate followed by a multivariate analysis were performed.

RESULTS

SVA was related to knee flexion during loading response (β = 0.268); CAM-HA to ROM pelvic obliquity (β = -0.19); rPT to mean pelvic tilt (β = -0.185) and ROM pelvic obliquity (β = -0.297); TK to ROM hip flexion/extension in stance (β = -0.17), mean foot progression in stance (β = -0.329), walking speed (β = -0.19), foot off (β = 0.223) and step length (β = -0.181).

SIGNIFICANCE

This study showed that increasing SVA, CAM-HA, TK and rPT, which is known to occur in adults with spinal deformities, could alter gait kinematics. Increases in these parameters, even in asymptomatic subjects, were related to a retroverted pelvis during gait, a reduced pelvic obliquity and hip flexion/extension mobility, an increased knee flexion during loading response as well as an increase in external foot progression angle. This was associated with a decrease in the walking pace: reduced speed, step length and longer stance phase.

Walking at work: Maximum gait speed is related to work ability in hospital nursing staff

OBJECTIVES

Like the concept of work ability in occupational health, gait speed is a measure of general fitness and can predict functional decline and morbidity. This is especially important when our care-takers, i.e. nurses, show decline in fitness and become care-receivers. The study aims to describe the demographics of hospital nurses in the context of gait speed and work ability as well as to determine the association between them.

METHODS

Three-hundred and twelve inpatient nurses and nursing assistants were sampled from a level 1 trauma and teaching hospital from several service lines and acuity levels. Spearman correlation tests were utilized to determine the relationship of gait speed and ratings of item 1 on the Work Ability Index (WAI) as well as Cochran-Armitage test for linear trend of gait speed.

RESULTS

Maximum gait speed has a significant positive association with work ability with a Rho coefficient of 0.217 (P < .0001). Additionally, the linear trend test of gait speed tertiles was significant (P < .001) for work ability categories of Moderate to Poor (0-7) and Good to Excellent (8-10).

CONCLUSIONS

Gait speed is correlated with the item 1 self-rating of the WAI in hospital nursing staff. The 10-m walk test is a practical and easy measure that can be utilized in occupational health. More research is required to validate gait speed in other occupational health populations and investigate gait speed changes and its interaction with the work environment longitudinally.

Mechanics of very slow human walking

Human walking speeds can be influenced by multiple factors, from energetic considerations to the time to reach a destination. Neurological deficits or lower-limb injuries can lead to slower walking speeds, and the recovery of able-bodied gait speed and behavior from impaired gait is considered an important rehabilitation goal. Because gait studies are typically performed at faster speeds, little normative data exists for very slow speeds (less than 0.6 ms[Formula: see text]). The purpose of our study was to investigate healthy gait mechanics at extremely slow walking speeds. We recorded kinematic and kinetic data from eight adult subjects walking at four slow speeds from 0.1 ms[Formula: see text]   to 0.6 ms[Formula: see text]   and at their self-selected speed. We found that known relations for spatiotemporal and work measures are still valid at very slow speeds. Trends derived from slow speeds largely provided reasonable estimates of gait measures at self-selected speeds. Our study helps enable valuable comparisons between able-bodied and impaired gait, including which pathological behaviors can be attributed to slow speeds and which to gait deficits. We also provide a slow walking dataset, which may serve as normative data for clinical evaluations and gait rehabilitative devices.