Movement deviation profile: a measure of distance from normality using a self-organizing neural network

Two sides of the same runner! The association between biomechanical and physiological markers of endurance performance in distance runners

BACKGROUND

The number of people who run to achieve competitive performance has increased, encouraging the scientific community to analyze the association of factors that can affect a runner performance.

RESEARCH QUESTION

Is there association between running spatiotemporal and angular kinematics with the physiological markers of endurance performance during a cardiorespiratory exercise test?

METHODS

This was an observational cross-sectional study with 40 distance runners simultaneously submitted to a running biomechanical analysis and cardiorespiratory exercise test on a treadmill. Mixed models were developed to verify the association between angular kinematic data obtained by the Movement Deviation Profile and the running spatiotemporal data with oxygen consumption and ventilatory thresholds.

RESULTS

Spatiotemporal variables [.e., step frequency Odds Ratio 0.09 [0.06-0.12 95 % Confidence Interval], center of mass vertical displacement Odds Ratio 0.10 [0.07-0.14 95 % Confidence Interval], and step length [Odds Ratio -0.01 [-0.01 to -0.00 95 % Confidence Interval]] were associated with VO2. Also, step frequency Odds Ratio 1.03 [1.01-1.05 95 % Confidence Interval] was associated with the first ventilatory threshold, and angular running kinematics [Movement Deviation Profile analysis] Odds Ratio 1.47 [1.13-1.91 95 % Confidence Interval] was associated with peak of exercise during the cardiorespiratory exercise test.

SIGNIFICANCE

Our findings demonstrated that: both higher step frequency and center of mass vertical displacement are associated with the increase of oxygen demand; step frequency is associated with the first ventilatory threshold, due to the entrainment mechanism and angular kinematic parameters are associated with peak aerobic speed. Future studies could also compare the biomechanical and physiological characteristics of different groups of distance runners. This could help identify the factors that contribute to oxygen demands during running and performance across different ages, genders, and levels of competition.

Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience

BACKGROUND

Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA).

RESEARCH QUESTION

How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled?

METHODS

The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects.

RESULTS

FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA.

SIGNIFICANCE

We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.

Patellar tendon shortening surgery restores the knee extensor mechanism in flexed knee gait in children with cerebral palsy

BACKGROUND

This study evaluated a patellar tendon shortening (PTS) surgical procedure that uses an overlapping repair combined with an additional Tycron non-absorbable suture to support the shortening in children with Cerebral Palsy (CP). This study aimed to outline this surgical technique and to evaluate its effectiveness in restoring the knee extensor mechanism.

METHODS

The sagittal plane lower limb kinematics, peak knee extensor moment, gait deviation index (GDI), localised movement deviation profile (MDP), temporospatial parameters, passive knee extension ROM, quadriceps lag, and knee extensor strength were calculated pre- and postoperatively. To determine significant differences a robust linear regression model with high breakdown point and high efficiency was fitted to the data.

RESULTS

In this retrospective cohort study, a total of 41 patients with CP who were treated with unilateral or bilateral PTS in isolation or as part of single event multilevel surgery (SEMLS), with a mean age of 11.1 years were included. The knee extension angle improved at initial contact (p < 0.0001), and during stance phase (p < 0.0001). The peak internal knee extensor moment decreased during early (p = 0.0014) and late stance phase (p < 0.0001). The quadriceps lag decreased (p < 0.0001) and knee extensor strength increased (p < 0.0001). The GDI improved (p < 0.0001), as well as the localised MDP for sagittal angles (p < 0.0001) and moments (p = 0.0001). Walking speed (p = 1.0) remained unchanged, but the cadence decreased (p = 0.024) and step length increased (p = 0.0001).

CONCLUSIONS

The knee extension angle and moment during stance phase improved significantly. The children with CP in this study showed improvements in knee extensor strength and quadriceps lag. Thereby it can be concluded that the PTS procedure was able to restore the knee extensor mechanism effectively.

Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders

Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.

Enhancing Wearable Gait Monitoring Systems: Identifying Optimal Kinematic Inputs in Typical Adolescents

Machine learning-based gait systems facilitate the real-time control of gait assistive technologies in neurological conditions. Improving such systems needs the identification of kinematic signals from inertial measurement unit wearables (IMUs) that are robust across different walking conditions without extensive data processing. We quantify changes in two kinematic signals, acceleration and angular velocity, from IMUs worn on the frontal plane of bilateral shanks and thighs in 30 adolescents (8-18 years) on a treadmills and outdoor overground walking at three different speeds (self-selected, slow, and fast). Primary curve-based analyses included similarity analyses such as cosine, Euclidean distance, Poincare analysis, and a newly defined bilateral symmetry dissimilarity test (BSDT). Analysis indicated that superior-inferior shank acceleration (SI shank Acc) and medial-lateral shank angular velocity (ML shank AV) demonstrated no differences to the control signal in BSDT, indicating the least variability across the different walking conditions. Both SI shank Acc and ML shank AV were also robust in Poincare analysis. Secondary parameter-based similarity analyses with conventional spatiotemporal gait parameters were also performed. This normative dataset of walking reports raw signal kinematics that demonstrate the least to most variability in switching between treadmill and outdoor walking to help guide future machine learning models to assist gait in pediatric neurological conditions.

Muscle synergies analysis shows altered neural strategies in women with patellofemoral pain during walking

Several studies suggest that the central nervous system coordinates muscle activation by modulating neural commands directed to groups of muscles combined to form muscle synergies. Individuals with patellofemoral pain (PFP) move differently from asymptomatic individuals. Understanding the neural strategies involved in the execution of tasks such as walking can help comprehend how the movement is planned and better understand this clinical condition. The objective of this study was to compare muscle synergies between women with and without PFP during walking. Eleven women with PFP and thirteen asymptomatic women were assessed using three-dimensional kinematics and electromyography (EMG) while walking at self-selected speed. Kinematics of the trunk, pelvis and lower limbs were analyzed through the Movement Deviation Profile. Muscle synergies were extracted from the EMG signals of eight lower limb muscles collected throughout the whole gait cycle. Kinematic differences between the two groups (p<0.001, z-score = 3.06) were more evident during loading response, terminal stance, and pre-swing. PFP group presented a lower number of muscle synergies (p = 0.037), and greater variability accounted for (VAFtotal) when using 3 (p = 0.017), 4 (p = 0.004), and 5 (p = 0.012) synergies to reconstruct all EMG signals. The PFP group also presented higher VAFmuscle for rectus femoris (p = 0.048) and gastrocnemius medialis (p = 0.019) when considering 4 synergies. Our results suggest that women with PFP show lower motor complexity and deficit in muscle coordination to execute gait, indicating that gait in PFP is the result of different neural commands compared to asymptomatic women.

Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics

Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics-i.e., gait signatures-for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations.

The Movement Deviation Profile Can Differentiate Faller and Non-Faller Older Adults

BACKGROUND

The World Health Organization considers falls the second leading cause of death by accidental injury worldwide and one of the most frequent complications in older adults during activities of daily living. Several tasks related to fall risk have been individually assessed describing kinematic changes in older adults. The study proposal was to identify which functional task differentiates faller and non-faller older adults using the movement deviation profile (MDP).

METHODS

This cross-sectional study recruited 68 older adults aged ≥60 years by convenience sampling. Older adults were divided into 2 groups: with and without a history of falls (34 older adults in each group). The MDP analyzed the 3-dimensional angular kinematics data of tasks (ie, gait, walking turn, stair ascent and descent, sit-to-stand, and stand-to-sit), and the Z score of the mean MDP identified which task presented the greatest difference between fallers and non-fallers. A multivariate analysis with Bonferroni post hoc verified the interaction between groups considering angular kinematic data and the cycle time of the task. Statistical significance was set at 5% (p < .05).

RESULTS

Z score of the MDPmean showed an interaction between groups (λ = 0.67, F = 5.085, p < .0001). Fallers differed significantly from non-fallers in all tasks and the greatest difference was in stair descent (Z score = 0.89). The time to complete each task was not different between groups.

CONCLUSIONS

The MDP distinguished older adult fallers from non-fallers. The stair descent task should be highlighted because it presented the greatest difference between groups.

Case report: The gait deviation index may predict neurotherapeutic effects of FES-assisted gait training in children with cerebral palsy

Background

Children with cerebral palsy (CP) show progressive loss of ambulatory function characterized by kinematic deviations at the hip, knee, and ankle. Functional electrical stimulation (FES) can lead to more typical lower limb kinematics during walking by eliciting appropriately timed muscle contractions. FES-assisted walking interventions have shown mixed to positive results in improving lower limb kinematics through immediate correction of gait during the application of FES, or long-term, persisting effects of non-FES-assisted gait improvements following multi-week FES-assisted gait training, at the absence of stimulation, i.e., neurotherapeutic effects. It is unknown, however, if children with CP will demonstrate a neurotherapeutic response following FES-assisted gait training because of the CP population's heterogeneity in gait deviations and responses to FES. Identifying the neurotherapeutic responders is, therefore, important to optimize the training interventions to those that have higher probability of benefiting from the intervention.

Objective

The purpose of this case study was to investigate the relationship between immediate and neurotherapeutic effects of FES-assisted walking to identify responders to a FES-assisted gait training protocol.

Methods

The primary outcome was Gait Deviation Index (GDI) and secondary outcome was root mean squared error (RMSE) of the lower extremity joint angles in the sagittal plane between participants with CP and a typically developing (TD) dataset. Potential indicators were defined as immediate improvements from baseline during FES-assisted walking followed by neurotherapeutic improvements at the end of training.

Case description

Gait analysis of two adolescent female participants with spastic diplegia (Gross Motor Function Classification System level II and III) was conducted at the start and end of a 12-week FES-assisted treadmill training protocol. Participant 1 had scissoring crouch gait, while participant 2 had jump gait.

Outcomes

The GDI showed both immediate (presence of FES) and neurotherapeutic (absence of FES after training period) improvements from baseline in our two participants. Joint angle RMSE showed mixed trends between immediate and neurotherapeutic changes from baseline. The GDI warrants investigation in a larger sample to determine if it can be used to identify responders to FES-assisted gait training.

Breaking new grounds in injury risk screening in soccer by deploying unsupervised learning with a special focus on sex and fatigue effects

In injury prevention, a vertical drop jump (DJ) is often used for screening athletes at risk for injury; however, the large variation in individual movement patterns might mask potentially relevant strategies when analysed on a group-based level. Two movement strategies are commonly discussed as predisposing athletes to ACL injuries: a deficient leg axis and increased leg stiffness during landing. This study investigated the individual movement pattern of 39 female and male competitive soccer players performing DJs at rest and after being fatigued. The joint angles were used to train a Kohonen self-organising map. Out of 19,596 input vectors, the SOM identified 700 unique postures. Visualising the movement trajectories and adding the latent parameters contact time, medial knee displacement (MKD) and knee abduction moment allow identification of zones with presumably increased injury risk and whether the individual movement patterns pass these zones. This information can be used, e.g., for individual screening and for feedback purposes. Additionally, an athlete's reaction to fatigue can be explored by comparing the rested and fatigued movement trajectories. The results highlight the ability of unsupervised learning to visualise movement patterns and to give further insight into an individual athlete's status without the necessity of a priori assumptions.

Three decades of gait index development: A comparative review of clinical and research gait indices

BACKGROUND

A wide variety of indices have been developed to quantify gait performance markers and associate them with their respective pathologies. Indices scores have enabled better decisions regarding patient treatments and allowed for optimized monitoring of the evolution of their condition. The extensive range of human gait indices presented over the last 30 years is evaluated and summarized in this narrative literature review exploring their application in clinical and research environments.

METHODS

The analysis will explore historical and modern gait indices, focusing on the clinical efficacy with respect to their proposed pathology, age range, and associated parameter limits. Features, methods, and clinically acceptable errors are discussed while simultaneously assessing indices advantages and disadvantages. This review analyses all indices published between 1994 and February 2021 identified using the Medline, PubMed, ScienceDirect, CINAHL, EMBASE, and Google Scholar databases.

FINDINGS

A total of 30 indices were identified as noteworthy for clinical and research purposes and another 137 works were included for discussion. The indices were divided in three major groups: observational (13), instrumented (16) and hybrid (1). The instrumented indices were further sub-divided in six groups, namely kinematic- (4), spatiotemporal- (5), kinetic- (2), kinematic- and kinetic- (2), electromyographic- (1) and Inertial Measurement Unit-based indices (2).

INTERPRETATION

This work is one of the first reviews to summarize observational and instrumented gait indices, exploring their applicability in research and clinical contexts. The aim of this review is to assist members of these communities with the selection of the proper index for the group in analysis.

Assessing functional recovery following total knee replacement surgery using objective classification of level gait data and patient-reported outcome measures

BACKGROUND

Patient recovery can be quantified objectively, via gait analysis, or subjectively, using patient reported outcome measures. Association between these measures would explain the level of disability reported in patient reported outcome measures and could assist with therapeutic decisions.

METHODS

Total knee replacement outcome was assessed using objective classification and patient-reported outcome measures (Knee Outcome Survey and Oxford Knee Scores). A classifier was trained to distinguish between healthy and osteoarthritic characteristics using knee kinematics, ground reaction force and temporal gait data, combined with anthropometric data from 32 healthy and 32 osteoarthritis knees. For the osteoarthritic cohort, classification of 20 subjects quantified changes at up to 3 timepoints post-surgery.

FINDINGS

Osteoarthritic classification was reduced for 17 subjects when comparing pre- to post-operative assessments, however only 6 participants achieved non-pathological classification and only 4 of these were classified as non-pathological at 12 months. In 15 cases, the level of osteoarthritic classification did not decrease between every post-operative assessment. For an individual's recovery, classification outputs correlated (r > 0.5) with knee outcome survey for 75% of patients and oxford knee score for 78% of patients (based on 20 and 9 subjects respectively). Classifier outputs from all visits of the combined total knee replacement sample correlated moderately with knee outcome survey (r > 0.4) and strongly with oxford knee score (r > 0.6).

INTERPRETATION

Biomechanical deficits existed in most subjects despite improvements in Patient Reported Outcome Measures, with larger changes reported subjectively as compared to measured objectively. Objective Classification provides additional insight alongside Patient Reported Outcomes when reporting recovered outcomes.

ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments

Mobile robotic platforms have made inroads in the rehabilitation area as gait assistance devices. They have rarely been used for human gait monitoring and analysis. The integration of mobile robots in this field offers the potential to develop multiple medical applications and achieve new discoveries. This study proposes the use of a mobile robotic platform based on depth cameras to perform the analysis of human gait in practical scenarios. The aim is to prove the validity of this robot and its applicability in clinical settings. The mechanical and software design of the system is presented, as well as the design of the controllers of the lane-keeping, person-following, and servoing systems. The accuracy of the system for the evaluation of joint kinematics and the main gait descriptors was validated by comparison with a Vicon-certified system. Some tests were performed in practical scenarios, where the effectiveness of the lane-keeping algorithm was evaluated. Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns. The results demonstrate that the system can perform gait analysis with high accuracy. In the curved sections of the paths, the knee joint is affected by occlusion and the deviation of the person in the camera reference system. This issue was greatly improved by adjusting the servoing system and the following distance. The control strategy of this robot was specifically designed for the analysis of human gait from the frontal part of the participant, which allows one to capture the gait properly and represents one of the major contributions of this study in clinical practice.

Cross-validation of a machine learning algorithm that determines anterior cruciate ligament rehabilitation status and evaluation of its ability to predict future injury

Classification algorithms determine the similarity of an observation to defined classes, e.g., injured or healthy athletes, and can highlight treatment targets or assess progress of a treatment. The primary aim was to cross-validate a previously developed classification algorithm using a different sample, while a secondary aim was to examine its ability to predict future ACL injuries. The examined outcome measure was 'healthy-limb' class membership probability, which was compared between a cohort of athletes without previous or future (No Injury) previous (PACL) and future ACL injury (FACL). The No Injury group had significantly higher probabilities than the PACL (p < 0.001; medium effect) and FACL group (p ≤ 0.045; small effect). The ability to predict group membership was poor for the PACL (area under curve [AUC]; 0.61<AUC<0.62) and FACL group (0.57<AUC<0.59). The ACL injury incidence proportion was highest in athletes with probabilities below 0.20 (9.4%; +2.7% to baseline), while athletes with probabilities above 0.80 had an incidence proportion of 4.1% (-2.6%). While findings that a low probability might represent an increase in injury risk on a group level, it is not sensitive enough for injury screening to predict a future injury on the individual level.

Three dimensional gait analyses in dizygotic twin athletes

Gait analysis and gait indices are frequently used to evaluate gait pathologies and outcomes. The aim of this study is to investigate the differences in gait parameters of dizygotic twin athletes according to each other and athletes group who are similar age but non-twin. Eighty-four athletes without any disease that could cause gait pathology were included the study. Time-distance measurements, kinematic - kinetic variables, and gait deviation index (GDI) of the gait functions of twin athletes (17 boys and 25 girls, height: 153.9 ± 15 cm, weight: 45.9 ± 12 kg, leg length 80.5 ± 11 cm) were compared with each other and with 42 sex and age matched non-twins athletes (height: 155 ± 15 cm, weight: 47 ± 14 kg, leg length 80.6 ± 9.8 cm, mean age 11.8 ± 2.29, range 6-15 years). No statistically significant difference was found about the time, distance parameters and GDIs in comparison of twin athletes with each other and the non-twin group. Additionally, kinetic and kinematic variables were similar in between twins. We measured lower adduction angles and higher abduction angles in non-twin athletes in comparison to the twin athletes (p = 0.01, 0.04). Additionally, the angle of knee flexion at the first contact was higher in non-twins (p = 0.003).Being dizygotic twin seems to have no clinical effect on gait function in athletes.

A New Method of Evaluating the Symmetry of Movement Used to Assess the Gait of Patients after Unilateral Total Hip Replacement

Purpose

We propose a new concept of symmetry, the symmetry function, as a continuous function of the percentage of differences between sides of body movement and normalised throughout the whole range of motion. The method is used to assess the dynamical symmetry of gait of patients after unilateral total hip replacement (asymmetric group) and healthy people (symmetric group) and also to reveal discrepancies between normal and abnormal movement patterns.

Methods

The gait of twelve male patients (49.7 ± 2.8 y), six weeks after unilateral total hip replacement (uTHR), was analysed against the gait of thirteen healthy men (36.1 ± 3.1 y). The speed of healthy men was matched to the speed of the patients. Comparison of the affected limb in uTHR patients with the healthy limb of able-bodied men was carried out on the basis of the highest symmetry values in the sagittal plane.

Results

In uTHR patients, the symmetry function provides information on the symmetry of movements in the whole range of motion in contrast to symmetry indices which are calculated for selected parameters or peak values. Research revealed average asymmetric discrepancies for pelvic tilt up to 250% for the entire gait cycle with a peak of approx. 400% at the end of the loading response and terminal swing phases. Asymmetry of gait observed in other joints was below 200% of the mean range of motion.

Conclusions

Regions of the greatest asymmetry in pathological movements are usually different from the region of the greatest range of motion. Therefore, it is insufficient to measure symmetry only for selected regions during motion. The symmetry function is a simple method which can complement other robust methods in time series data evaluation and interpretation.

Objective classification and scoring of movement deficiencies in patients with anterior cruciate ligament reconstruction

Motion analysis systems are widely employed to identify movement deficiencies-e.g. patterns that potentially increase the risk of injury or inhibit performance. However, findings across studies are often conflicting in respect to what a movement deficiency is or the magnitude of association to a specific injury. This study tests the information content within movement data using a data driven framework that was taught to classify movement data into the classes: NORM, ACLOP and ACLNO OP, without the input of expert knowledge. The NORM class was presented by 62 subjects (124 NORM limbs), while 156 subjects with ACL reconstruction represented the ACLOP and ACLNO OP class (156 limbs each class). Movement data from jumping, hopping and change of direction exercises were examined, using a variety of machine learning techniques. A stratified shuffle split cross-validation was used to obtain a measure of expected accuracy for each step within the analysis. Classification accuracies (from best performing classifiers) ranged from 52 to 81%, using up to 5 features. The exercise with the highest classification accuracy was the double leg drop jump (DLDJ; 81%), the highest classification accuracy when considering only the NORM class was observed in the single leg hop (81%), while the DLDJ demonstrated the highest classification accuracy when considering only for the ACLOP and ACLNO OP class (84%). These classification accuracies demonstrate that biomechanical data contains valuable information and that it is possible to differentiate normal from rehabilitating movement patterns. Further, findings highlight that a few features contain most of the information, that it is important to seek to understand what a classification model has learned, that symmetry measures are important, that exercises capture different qualities and that not all subjects within a normative cohort utilise 'true' normative movement patterns (only 27 to 71%).

Step down tests are the tasks that most differentiate the kinematics of women with patellofemoral pain compared to asymptomatic controls

BACKGROUND

Studies evaluating kinematics lead to different conclusions, not all changes appear in all assessed tasks and in all subgroups of patients with patellofemoral pain (PFP). The inconsistencies between studies could be reduced if we knew which task separates patients best from healthy controls.

RESEARCH QUESTION

Identify which functional task, between gait, forward step down (FSD), lateral step down (LSD), stair ascent and descent and propulsion and landing phase of the single leg hop test (SLHT), differentiates the three-dimensional kinematics of women with patellofemoral pain from asymptomatic women.

METHODS

This cross-sectional study evaluated thirty-five PFP and thirty-five asymptomatic women during the execution of the following tasks: gait, FSD, LSD, stair ascent and descent and the propulsion and landing phase of single leg hop test. Frontal, sagittal and transverse plane angles of the trunk, pelvis and hip, frontal and sagittal plane angles of the knee, ankle dorsiflexion, foot progression angle and hindfoot eversion were analyzed through the Movement Deviation Profile (MDP). To compare the groups, the multivariate analysis with Bonferroni post hoc test were used, with a significance level of p < 0.01. To identify which task presented the most difference between the groups, the Z-score of the mean MDP was calculated.

RESULTS

For all tasks, the groups presented significant differences. According to the Z-score, the groups got farther apart considering the MDP for each task in the following order: LSD (7.97), FSD (7.62), landing phase of SLHT (3.43), gait (2.85), propulsion phase of SLHT (1.64), descending stairs (1.63) and ascending stairs (1.00).

SIGNIFICANCE

We suggest that step down tests should be included in the assessment of PFP patients, since these tests most differentiate the kinematics of women with and without PFP. Identifying the tasks with the highest sensitivity to detect the kinematic differences is expected to improve clinical decision-making.

Leaving hip rotation out of a conventional 3D gait model improves discrimination of pathological gait in cerebral palsy: A novel neural network analysis

BACKGROUND

Complex clinical gait analysis results can be expressed as single number gait deviations by applying multivariate processing methods. The original Movement Deviation Profile (MDP) quantifies the deviation of abnormal gait using the most trusted nine dynamic joint angles of lower limbs.

RESEARCH QUESTION

Which subset of joint angles maximises the ability of the MDP to separate abnormal gait from normality? What is the effect of using the best subset in a large group of patients, and in individuals?

METHODS

A self-organising neural network was trained using normal gait data from 166 controls, and then the MDP of 1923 patients with cerebral palsy (3846 legs) was calculated. The same procedure was repeated with 511 combinations of the nine joint angles. The standardised distances of abnormal gait from normality were then calculated as log-transformed Z-scores to select the best combination. A mixed design ANOVA was used to assess how removing the least discriminating angle improved the separation of patients from controls. The effect of using the optimal subset of angles was also quantified for each individual leg by comparing the change in MDP to the independent FAQ levels of patients.

RESULTS

Removal of hip rotation significantly (p<0.0005) increased the separation of the patient group from normality (ΔZ-score 0.24) and also at FAQ levels 7-10 (ΔZ-score 0.38, 0.27, 0.22, 0.14). The MDP of individual patients changed in a wider range of -4.65 to 1.12 Z-scores and their change matched their independent FAQ scores, with less functional patients moving further from, and more functional patients moving closer to normality.

SIGNIFICANCE

In existing gait databases we recommend excluding hip rotation from data used to calculate the MDP. Alternatively, the calculation of hip rotation can be improved by post-hoc correction, but the ultimate solution is to use more accurate and reliable models of hip rotation.

Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis

INTRODUCTION

The increasing popularity of inertial sensors in clinical practice is not supported by precise information on their reliability or guidelines for their use in rehabilitation. The authors investigated the state of the literature concerning the use of inertial sensors for gait analysis in both healthy and pathological adults comparing traditional systems. Furthermore, trying to define directions for clinicians.

EVIDENCE ACQUISITION

In accordance with the PRISMA statement, authors searched in PubMed, Web of Science and Scopus all paper published from January 1st, 2005 until December 31st, 2017. They included both healthy and pathological adults' subjects as population, wearable or inertial sensors used for gait analysis and compared with classical gait analysis performed in a Motion Lab as intervention and comparison, gait parameters as outcomes. Considering the methodological quality, authors focused on: sample; description of the study; type of gait analysis used for comparison; type of sensor; sensor placement on the body; gait task requested.

EVIDENCE SYNTHESIS

From a total of 888 articles, 16 manuscripts were selected and 7 of them were considered for meta-analysis for different gait parameters. Demographic data, tested devices, reference systems, test procedures and outcomes were analyzed.

CONCLUSIONS

Our results show a good agreement between inertial sensors and classical gait analysis for some gait parameters, supporting their use as a solution for capturing kinematic information over an extended space and time and even outside a laboratory in real-life conditions. Authors can support the use of portable inertial sensors for a practical gait analysis in clinical setting with good reliability. It will then be the experience of the clinician to direct the decision-making process.

Optimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analyses

Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statistical methods, such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), have been adopted since they have the potential to overcome these data handling issues. The aim of the current study was to develop and optimise a specific machine learning algorithm for processing human locomotion data. Twenty participants ran at a self-selected speed across a 15m runway in barefoot and shod conditions. Ground reaction forces (BW) and kinematics were measured at 1000 Hz and 100 Hz, respectively from which joint angles (°), joint moments (N.m.kg-1) and joint powers (W.kg-1) for the hip, knee and ankle joints were calculated in all three anatomical planes. Using PCA and DFA, power spectra of the kinematic and kinetic variables were used as a training database for the development of a machine learning algorithm. All possible combinations of 10 out of 20 participants were explored to find the iteration of individuals that would optimise the machine learning algorithm. The results showed that the algorithm was able to successfully predict whether a participant ran shod or barefoot in 93.5% of cases. To the authors' knowledge, this is the first study to optimise the development of a machine learning algorithm.

Changes in balance coordination and transfer to an unlearned balance task after slackline training: a self-organizing map analysis

How humans maintain balance and change postural control due to age, injury, immobility or training is one of the basic questions in motor control. One of the problems in understanding postural control is the large set of degrees of freedom in the human motor system. Therefore, a self-organizing map (SOM), a type of artificial neural network, was used in the present study to extract and visualize information about high-dimensional balance strategies before and after a 6-week slackline training intervention. Thirteen subjects performed a flamingo and slackline balance task before and after the training while full body kinematics were measured. Range of motion, velocity and frequency of the center of mass and joint angles from the pelvis, trunk and lower leg (45 variables) were calculated and subsequently analyzed with an SOM. Subjects increased their standing time significantly on the flamingo (average +2.93 s, Cohen's d = 1.04) and slackline (+9.55 s, d = 3.28) tasks, but the effect size was more than three times larger in the slackline. The SOM analysis, followed by a k-means clustering and marginal homogeneity test, showed that the balance coordination pattern was significantly different between pre- and post-test for the slackline task only (χ ² = 82.247; p < 0.001). The shift in balance coordination on the slackline could be characterized by an increase in range of motion and a decrease in velocity and frequency in nearly all degrees of freedom simultaneously. The observation of low transfer of coordination strategies to the flamingo task adds further evidence for the task-specificity principle of balance training, meaning that slackline training alone will be insufficient to increase postural control in other challenging situations.

The use of the Gait Deviation Index for the evaluation of participants following total hip arthroplasty: An explorative randomized trial

INTRODUCTION

In this paper, the Gait Deviation Index (GDI) was used as a convenient method to evaluate pre-to-postoperative gait pattern changes after total hip arthroplasty and identify factors which might be predictive of outcome.

DESIGN

Three-dimensional gait data from a randomized clinical trial was used to determine changes in gait quality in participants walking at self-selected speed. Upon completion of the first assessment, the participants were randomly assigned to either resurfacing hip arthroplasty or conventional hip arthroplasty. The outcome was changes in overall gait 'quality' measured with GDI during the 6-month post-surgery follow-up period.

RESULTS

38 participants with severe unilateral primary hip osteoarthritis took part in the trial. We found no difference in change scores between the two treatment groups; 1.9 [95%CI: -0.3 to 4.0] or between change scores for the non-operated and the operated limbs; 0.3 [95%CI: -2.3 to 1.7]. However, the score for the two groups (pooled data) improved after surgery by 4.4 [95%CI: 1.8-7.0]. The single level regression analysis identified the preoperative GDI score as a strong predictor of outcome (p<0.001).

CONCLUSION

Six months after surgery, there was no additional effect of resurfacing hip arthroplasty on GDI scores compared with conventional hip arthroplasty. Participants with the most pathological preoperative gait pattern improved the most. The GDI increased, which indicates an overall improvement in gait pathology after surgery.

TRIAL REGISTRATION

NCT01229293.

Age-Related Deviation of Gait from Normality in Alkaptonuria

Alkaptonuria is a rare metabolic disease leading to systemic changes including early and severe arthropathy which affects mobility. For unknown reasons, the onset of degenerative changes is delayed to around 30 years of age when both objective and subjective symptoms develop. In order to complement description of the structural changes in alkaptonuria with measures of movement function, clinical gait analysis was added to the list of assessments in 2013. The aim of this study was to describe the deviation of gait from normality as a function of age in patients with alkaptonuria. Three-dimensional movement of reflective markers attached to joints were captured during walking in 39 patients and 10 controls. Subsequent to processing the data to emphasise the shape of marker trajectories, the mean Movement Deviation Profile was generated for all participants. This single number measure gives the deviation of a patient's gait from a distributed definition of gait normality. Results showed that gait deviation roughly follows a sigmoid profile with minimal increase of gait deviations in a younger patient group and an abrupt large increase around the second half of the 4th decade of life. Larger variations of gait deviations were found in the older group than in the younger group suggesting a complex interaction of multiple factors which determine gait function after symptoms manifest. Continued gait analysis of adults with AKU, extended to younger adults and children with AKU, is expected to complete understanding of both the natural history of alkaptonuria and how interventions can affect movement function.

Intelligent data analysis of instrumented gait data in stroke patients-a systematic review

Instrumented gait analysis (GA) may be used to analyze the causes of gait deviation in stroke patients but generates a large amount of complex data. The task of transforming this data into a comprehensible report is cumbersome. Intelligent data analysis (IDA) refers to the use of computational methods in order to analyze quantitative data more effectively. The purpose of this review was to identify and appraise the available IDA methods for handling GA data collected from patients with stroke using the standard equipment of a gait lab (3D/2D motion capture, force plates, EMG). Eleven databases were systematically searched and fifteen studies that employed some type of IDA method for the analysis of kinematic and/or kinetic and/or EMG data in populations involving stroke patients were identified. Four categories of IDA methods were employed for the analysis of sensor-acquired data in these fifteen studies: classification methods, dimensionality reduction methods, clustering methods and expert systems. The methodological quality of these studies was critically appraised by examining sample characteristics, measurements and IDA properties. Three overall methodological shortcomings were identified: (1) small sample sizes and underreported patient characteristics, (2) testing of which method is best suited to the analysis was neglected and (3) lack of stringent validation procedures. No IDA method for GA data from stroke patients was identified that can be directly applied to clinical practice. Our findings suggest that the potential provided by IDA methods is not being fully exploited.

A marker based kinematic method of identifying initial contact during gait suitable for use in real-time visual feedback applications

A gait cycle is typically defined as being from heel strike or initial contact (IC) to the next ipsilateral IC using kinetic data. When these data are not available other methods of event definition are required. An algorithm based upon sagittal plane kinematics of the hip, which defines IC at contralateral peak hip extension (PHE) is presented. Kinematic and kinetic data were recorded while 10 unimpaired participants each completed a minimum of 25 overground gait cycles. The accuracy of 551 IC events was evaluated by comparing the agreement of PHE to other kinematic and kinetic algorithms. The mean temporal difference in IC between the PHE algorithm and a kinetic algorithm was +0.0006±0.008 s. The 95% Limits of Agreement was ±0.018 s. This new PHE algorithm provides simple to implement and accurate gait events for use when kinetic data are not available.