A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Running-Related Injury Incidence: Does It Correlate with Kinematic Sub-groups of Runners? A Scoping Review

BACKGROUND

Historically, kinematic measures have been compared across injured and non-injured groups of runners, failing to take into account variability in kinematic patterns that exist independent of injury, and resulting in false positives. Research led by gait patterns and not pre-defined injury status is called for, to better understand running-related injury (RRI) aetiology and within- and between-group variability.

OBJECTIVES

Synthesise evidence for the existence of distinct kinematic sub-groups across a population of injured and healthy runners and assess between-group variability in kinematics, demographics and injury incidence.

DATA SOURCES

Electronic database search: PubMed, Web of Science, Cochrane Central Register of Controlled Trials (Wiley), Embase, OVID, Scopus.

ELIGIBILITY CRITERIA

Original, peer-reviewed, research articles, published from database start to August 2022 and limited to English language were searched for quantitative and mixed-methods full-text studies that clustered injured runners according to kinematic variables.

RESULTS

Five studies (n = 690) were included in the review. All studies detected the presence of distinct kinematic sub-groups of runners through cluster analysis. Sub-groups were defined by multiple differences in hip, knee and foot kinematics. Sex, step rate and running speed also varied significantly between groups. Random injury dispersal across sub-groups suggests no strong evidence for an association between kinematic sub-groups and injury type or location.

CONCLUSION

Sub-groups containing homogeneous gait patterns exist across healthy and injured populations of runners. It is likely that a single injury may be represented by multiple movement patterns, and therefore kinematics may not predict injury risk. Research to better understand the underlying causes of kinematic variability, and their associations with RRI, is warranted.

Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach

Running biomechanics are affected by fatiguing or prolonged runs. However, no evidence to date has conclusively linked this effect to running-related injury (RRI) development or performance implications. Previous investigations using subject-specific models in running have demonstrated higher accuracy than group-based models, however, this has been infrequently applied to fatigue. In this study, two experiments were conducted to determine whether subject-specific models outperformed group-based models to classify running biomechanics during non-fatigued and fatigued conditions. In the first experiment, 16 participants performed four treadmill runs at or around the maximal lactate steady state. In the second experiment, nine participants performed five prolonged runs using commercial wearable devices. For each experiment, two segments were extracted from each trial from early and late in the run. For each participant, a random forest model was applied with a leave-one-run-out cross-validation to classify between the early (non-fatigued) and late (fatigued) segments. Additionally, group-based classifiers with a leave-one-subject-out cross validation were constructed. For experiment 1, mean classification accuracies for the single-subject and group-based classifiers were 68.2 ± 8.2% and 57.0 ± 8.9%, respectively. For experiment 2, mean classification accuracies for the single-subject and group-based classifiers were 68.9 ± 17.1% and 61.5 ± 11.7%, respectively. Variable importance rankings were consistent within participants, but these rankings differed from each participant to those of the group. Although the classification accuracies were relatively low, these findings highlight the advantage of subject-specific classifiers to detect changes in running biomechanics with fatigue and indicate the potential of using big data and wearable technology approaches in future research to determine possible connections between biomechanics and RRI.

A hierarchical clustering approach for examining the relationship between pelvis-proximal femur geometry and bone stress injury in runners

Bone stress injury (BSI) risk in runners is multifactorial and not well understood. Unsupervised machine learning approaches can potentially elucidate risk factors for BSI by identifying groups of similar runners within a population which differ in BSI incidence. Here, a hierarchical clustering approach is used to identify groups of collegiate cross country runners based on 2-dimensional frontal plane pelvis and proximal femur geometry, which was extracted from dual-energy X-ray absorptiometry scans and dimensionally reduced by principal component analysis. Seven distinct groups were identified using the cluster tree, with the initial split being highly related to female-male differences. Visual inspection revealed clear differences between groups in pelvis and proximal femur geometry, and groups were found to differ in lower body BSI incidence during the subsequent academic year (Rand index = 0.53; adjusted Rand index = 0.07). Linear models showed between-cluster differences in visually identified geometric measures. Geometric measures were aggregated into a pelvis shape factor based on trends with BSI incidence, and the resulting shape factor was significantly different between clusters (p < 0.001). Lower shape factor values, corresponding with lower pelvis height and ischial span, and greater iliac span and trochanteric span, appeared to be related to increased BSI incidence. This trend was dominated by the effect observed across clusters of male runners, indicating that geometric effects may be more relevant to BSI risk in males, or that other factors masked the relationship in females. More broadly, this work outlines a methodological approach for distilling complex geometric differences into simple metrics that relate to injury risk.

Patient-Reported Outcomes Measurement Information System -29 Domains Interaction in Chronic Musculoskeletal Pain During Acupuncture: A Pilot Study

Objective

This pilot study explored interactions of domains of physical, psychologic, and social factors in the Patient-Reported Outcomes Measurement Information System® (PROMIS®)-29 system and their dynamic changes during acupuncture treatment of chronic musculoskeletal pain.

Materials and Methods

PROMIS-29 profile, version 2.1 was applied among participants with chronic musculoskeletal pain, who received acupuncture treatment for 5 weeks. Data from function-oriented and symptom-oriented domains as well as changes in pain intensity were evaluated at weeks 0, 3, and 5, in 9 patients who completed full sessions. Scores of the domains were analyzed by hierarchical cluster analysis at each timepoint to identify the patterns of interactions of PROMIS domains.

Results

Hierarchical cluster analysis revealed the existence of 2 main clusters: one consisting of pain, fatigue, and emotional domains; the other comprising physical function and social domains. The general pattern was stable but interactions were found throughout the treatment. The score for sleep disturbance did not improve but was correlated with different domains at varying stages of treatment.

Conclusions

Interaction between 2 clusters of pain with fatigue and emotional domains; and physical function with social domains showed that acupuncture produces holistic reductions in chronic musculoskeletal pain. However, the limitation of sample size and bias in this pilot study requires future research on the need to adopt an interdisciplinary and holistic approach to the recovery of patients with chronic musculoskeletal pain, who have dynamic needs.

Method for Assessing the Motor Coordination of Runners Based on the Analysis of Multichannel EMGs

In this paper, we propose a method to evaluate the motor coordination of runners based on the analysis of amplitude and spatiotemporal dynamics of multichannel electromyography. A new diagnostic index for the coordination of runners was proposed, including the amplitude of electromyography, the spatiotemporal stability coefficient, and the symmetry coefficient of muscle force. The motor coordination of 13 professional runners was studied. Detailed anthropometric information was recorded about the professional runners. It has been found that professional athletes are characterized by the stability of movement repetition (more than 83%) and the high degree of symmetry of muscle efforts of the left and right legs (more than 81%) regardless of the changes in load during running at a speed of 8-12 km/hr. Scientific and technological means can support the scientific training of athletes. The end of the Winter Olympic Games has shown us the powerful power of a series of intelligent scientific equipment, including electro-magnetic gun, in sports training. We also look forward to the continuous innovation of these advanced technologies, which will contribute to the intelligent development of sports scientific research.

Impact of biomechanics on therapeutic interventions and rehabilitation for major chronic musculoskeletal conditions: A 50-year perspective

The pivotal role of biomechanics in the past 50 years in consolidating the basic knowledge that underpins prevention and rehabilitation measures has made this area a great spotlight for health practitioners. In clinical practice, biomechanics analysis of spatiotemporal, kinematic, kinetic, and electromyographic data in various chronic conditions serves to directly enhance deeper understanding of locomotion and the consequences of musculoskeletal dysfunctions in terms of motion and motor control. It also serves to propose straightforward and tailored interventions. The importance of this approach is supported by myriad biomechanical outcomes in clinical trials and by the development of new interventions clearly grounded on biomechanical principles. Over the past five decades, therapeutic interventions have been transformed from fundamentally passive in essence, such as orthoses and footwear, to emphasizing active prevention, including exercise approaches, such as bottom-up and top-down strengthening programs for runners and people with osteoarthritis. These approaches may be far more effective inreducing pain, dysfunction, and, ideally, incidence if they are based on the biomechanical status of the affected person. In this review, we demonstrate evidence of the impact of biomechanics and motion analysis as a foundation for physical therapy/rehabilitation and preventive strategies for three chronic conditions of high worldwide prevalence: diabetes and peripheral neuropathy, knee osteoarthritis, and running-related injuries. We conclude with a summary of recommendations for future studies needed to address current research gaps.

Achilles tendon forces and pain during common rehabilitation exercises in male runners with Achilles tendinopathy. A laboratory study

OBJECTIVES

To estimate Achilles tendon forces and their relationship with self-reported pain in runners with Achilles tendinopathy (AT) during common rehabilitation exercises.

DESIGN

Cross-sectional.

SETTING

Laboratory.

PARTICIPANTS

24 recreational male runners (45.92 (8.24) years old; 78.20 (8.01) kg; 177.17 (6.69) cm) with symptomatic AT.

MAIN OUTCOME MEASURES

Kinematic and kinetic data were collected to estimate Achilles tendon forces during 12 commonly prescribed exercises. Achilles tendon forces were estimated from biomechanical data and normalised to the participant's bodyweight. The secondary aim was to investigate the relationship between Achilles tendon forces and pain during these exercises.

RESULTS

Two exercise clusters were identified based on Achilles tendon forces. Cluster1 included various exercises including double heel raises, single heel raises, and walking (range: 1.10-2.76 BWs). Cluster2 included running, jumping and hopping exercises (range: 5.13-6.35 BWs). Correlation between tendon forces and pain was at best low for each exercise (range: -0.43 - 0.20). Higher force exercises lead to more tendon load for a given amount of pain (R² = 0.7505; y = 0.2367x + 0.6191).

CONCLUSION

This study proposes a hierarchical exercise progression based on Achilles tendon forces to guide treatment of runners with AT. Achilles tendon forces and pain are not correlated in runners with AT.

Differences in kinetic variables between injured and uninjured rearfoot runners: A hierarchical cluster analysis

Running is a popular form of physical activity with a high incidence of running-related injuries. However, the etiology of running-related injuries remains elusive, possibly due to the heterogeneity of movement patterns. The purpose of this study was to investigate whether different clusters existed within a large group of injured and uninjured runners based on their kinetic gait patterns. A sample of 134 injured and uninjured runners were acquired from an existing database and 12 discrete kinetic and spatiotemporal variables which are commonly associated with running injuries were extracted from the ground reaction force waveforms. A principal components analysis followed by an unsupervised hierarchical cluster analysis was performed. The results revealed two distinct clusters of runners which were not associated with injury status (OR = 1.14 [0.57, 2.30], χ²  = 0.143, p = 0.706) or sex (OR = 1.72 [0.85, 3.49], χ²  = 2.3258, p = 0.127). These results suggest that while there appeared to be evidence for two distinct clusters within a large sample of injured and uninjured runners, there is no association between the kinetic variables and running related injuries.

Spatiotemporal gait patterns in individuals with unilateral transfemoral amputation: A hierarchical cluster analysis

Gait pattern classification in individuals with lower-limb amputation could help in developing personalized prosthetic prescriptions and tailored gait rehabilitation. However, systematic classifications of gait patterns in this population have been scarcely explored. This study aimed to determine whether the gait patterns in individuals with unilateral transfemoral amputation (UTFA) can be clustered into homogeneous subgroups using spatiotemporal parameters across a range of walking speeds. We examined spatiotemporal gait parameters, including step length and cadence, in 25 individuals with UTFA (functional level K3 or K4, all non-vascular amputations) while they walked on a split-belt instrumented treadmill at eight speeds. Hierarchical cluster analysis (HCA) was used to identify clusters with homogeneous gait patterns based on the relationships between step length and cadence. Furthermore, after cluster formation, post-hoc analyses were performed to compare the spatiotemporal parameters and demographic data among the clusters. HCA identified three homogeneous gait pattern clusters, suggesting that individuals with UTFA have several gait patterns. Further, we found significant differences in the participants' body height, sex ratio, and their prosthetic knee component among the clusters. Therefore, gait rehabilitation should be individualized based on body size and prosthetic prescription.

The Automatization of the Gait Analysis by the Vicon Video System: A Pilot Study

The quality of modern measuring instruments has a strong influence on the speed of diagnosing diseases of the human musculoskeletal system. The research is focused on automatization of the method of gait analysis. The study involved six healthy subjects. The subjects walk straight. Each subject made several gait types: casual walking and imitation of a non-standard gait, including shuffling, lameness, clubfoot, walking from the heel, rolling from heel to toe, walking with hands in pockets, and catwalk. Each type of gait was recorded three times. For video fixation, the Vicon Nexus system was used. A total of 27 reflective markers were placed on the special anatomical regions. The goniometry methods were used. The walk data were divided by steps and by step phases. Kinematic parameters for estimation were formulated and calculated. An approach for data clusterization is presented. For this purpose, angle data were interpolated and the interpolation coefficients were used for clustering the data. The data were processed and four cluster groups were found. Typical angulograms for cluster groups were presented. For each group, average angles were calculated. A statistically significant difference was found between received cluster groups.

A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning

The application of machine learning algorithms in studying injury assessment methods based on data analysis has recently provided a new research insight for sports injury prevention. However, the data used in these studies are primarily multi-source and multimodal (i.e., longitudinal repeated-measures data and cross-sectional data), resulting in the models not fully utilising the information in the data to reveal specific injury risk patterns. Therefore, this study proposed an injury risk prediction model based on a multi-modal strategy and machine learning algorithms to handle multi-source data better and predict injury risk. This study retrospectively analysed the routine monitoring data of sixteen young female basketball players. These data included training load, perceived well-being status, physiological response, physical performance and lower extremity non-contact injury registration. This study partitions the original dataset based on the frequency of data collection. Extreme gradient boosting (XGBoost) was used to construct unimodal submodels to obtain decision scores for each category of indicators. Ultimately, the decision scores from each submodel were fused using the random forest (RF) to generate a lower extremity non-contact injury risk prediction model at the decision-level. The 10-fold cross-validation results showed that the fusion model was effective in classifying non-injured (mean Precision: 0.9932, mean Recall: 0.9976, mean F2-score: 0.9967), minimal lower extremity non-contact injuries risk (mean Precision: 0.9317, mean Recall: 0.9167, mean F2-score: 0.9171), and mild lower extremity non-contact injuries risk (mean Precision: 0.9000, mean Recall: 0.9000, mean F2-score: 0.9000). The model performed significantly more optimal than the submodel. Comparing the fusion model proposed with a traditional data integration scheme, the average Precision and Recall improved by 8.2 and 20.3%, respectively. The decision curves analysis showed that the proposed fusion model provided a higher net benefit to athletes with potential lower extremity non-contact injury risk. The validity, feasibility and practicality of the proposed model have been confirmed. In addition, the shapley additive explanation (SHAP) and network visualisation revealed differences in lower extremity non-contact injury risk patterns across severity levels. The model proposed in this study provided a fresh perspective on injury prevention in future research.

A step towards dynamic foot classification: Functional grouping using ankle joint frontal plane motion in running

BACKGROUND

The premise behind static foot classification suggests structure dictate's function. However, the validity of this has been challenged, as weak association between static foot type and dynamic motion exists. This has led to calls for dynamic assessments and classification of feet based on functional motion, yet methods to do this have been seldom explored.

RESEARCH QUESTION

Within a group of runners do homogenous sub-groups of ankle joint complex (AJC) frontal plane motion exist?

METHODS

A k means clustering analysis was conducted on the frontal plane AJC motion patterns of a group of healthy adults running barefoot (n = 42) to identify functional movement groups. Once identified, statistical parametric mapping was employed to determine the differences between clusters across stance. The identified clusters were used to determine dynamic foot type; an agreement analysis was conducted between the newly defined foot types and the Foot Posture Index (FPI-6).

RESULTS

Two distinct clusters were identified. Waveform analysis identified that cluster 1 displayed significantly (p < 0.001) less AJC eversion between 0% and 97% of the stance phase compared to cluster 2, with the differences between clusters associated with large effect sizes (g > 1). Based on the displayed kinematic profiles, cluster 1 was defined as a Neutral Dynamic Foot Type (Neutral_DFT), and cluster 2 a Pronated Dynamic Foot Type (Pronated Dynamic_DFT). The newly defined foot type measure had only a slight agreement (κ = 0.08) with the FPI-6.

SIGNIFICANCE

We demonstrated a protocol to classify a runner's foot type derived directly from AJC motion during running. Poor agreement between the dynamic and static classification measures further evidence that these assessments are not analogous. Our results question the validity of static classification when looking to characterise the foot during running, suggesting dynamic assessments are more appropriate to reflect the foots functional response.

A hierarchical clustering approach for examining potential risk factors for bone stress injury in runners

Bone stress injuries (BSI) are overuse injuries that commonly occur in runners. BSI risk is multifactorial and not well understood. Unsupervised machine learning approaches can potentially elucidate risk factors for BSI by looking for groups of similar runners within a population that differ in BSI incidence. Here, a hierarchical clustering approach is used to identify groups of collegiate cross country runners (32 females, 21 males) based on healthy pre-season running (4.47 m·s^-1) gait data which were aggregated and dimensionally reduced by principal component analysis. Five distinct groups were identified using the cluster tree. Visual inspection revealed clear differences between groups in kinematics and kinetics, and linear mixed effects models showed between-group differences in metrics potentially related to BSI risk. The groups also differed in BSI incidence during the subsequent academic year (Rand index = 0.49; adjusted Rand index = -0.02). Groups ranged from those including runners spending less time contacting the ground and generating higher peak ground reaction forces and joint moments to those including runners spending more time on the ground with lower loads. The former groups showed higher BSI incidence, indicating that short stance phases and high peak loads may be risk factors for BSI. Since ground contact duration may itself account for differences in peak loading metrics, we hypothesize that the percentage of time a runner is in contact with the ground may be a useful metric to include in machine learning models for predicting BSI risk.

Biomechanical running gait assessments across prevalent adolescent musculoskeletal injuries

BACKGROUND

While there is substantial information available regarding expected biomechanical adaptations associated with adult running-related injuries, less is known about adolescent gait profiles that may influence injury development.

RESEARCH QUESTIONS

Which biomechanical profiles are associated with prevalent musculoskeletal lower extremity injuries among adolescent runners, and how do these profiles compare across injury types and body regions?

METHODS

We conducted a cross-sectional study of 149 injured adolescents (110 F; 39 M) seen at a hospital-affiliated injured runner's clinic between the years 2016-2021. Biomechanical data were obtained from 2-dimensional video analyses and an instrumented treadmill system. Multivariate analyses of variance covarying for gender and body mass index were used to compare continuous biomechanical measures, and Chi-square analyses were used to compare categorical biomechanical variables across injury types and body regions. Spearman's rho correlation analyses were conducted to assess the relationship of significant outcomes.

RESULTS

Patients with bony injuries had significantly higher maximum vertical ground reaction forces (bony: 1.87 body weight [BW] vs. soft tissue: 1.79BW, p = 0.05), and a higher proportion of runners with contralateral pelvic drop at midstance (χ² =5.3, p = 0.02). Maximum vertical ground reaction forces and pelvic drop were significantly yet weakly correlated (ρ = 0.20, p = 0.01). Foot strike patterns differed across injured body regions, with a higher proportion of hip and knee injury patients presenting with forefoot strike patterns (χ² =22.0, p = 0.01).

SIGNIFICANCE

These biomechanical factors may represent risk factors for injuries sustained by young runners. Clinicians may consider assessing these gait adaptations when treating injured adolescent patients.

Brain and muscle activation patterns during postural control affect static postural control

BACKGROUND

Previous studies have reported existence of coordinated brain and muscle activity patterns that affect postural control. However, differences in these activity patterns that affect postural control are still unclear. The purpose of this study was to clarify brain and muscle activity pattern affecting postural control.

RESEARCH QUESTION

Does the difference in brain and muscle activity patterns during postural control affect postural control ability?

METHOD

Nineteen healthy men (mean age: 24.8 ± 4.1 years, height: 171.8 ± 5.5 cm, and weight: 63.5 ± 12.5 kg) performed a postural control task on a balance board, and their brain and muscle activities and body sway during the task were measured using functional near-infrared spectroscopy, surface electromyography, and three-dimensional accelerometry. Hierarchical cluster analysis was conducted to extract subgroups based on brain and muscle activities and postural control, and correlation analysis was performed to investigate the relationship between brain activity, muscle activity, and postural control.

RESULTS

Two subgroups were found. Subgroup 1 (n = 9) showed higher brain activity in the supplementary motor area (p = 0.04), primary motor cortex (p = 0.04) and stable postural control in the mediolateral (p < 0.01) planes, and subgroup 2 (n = 10) showed higher muscle activity in the tibialis anterior (p < 0.01), a higher shank muscles co-contraction (p = 0.02) and unstable postural control. Furthermore, the supplementary motor area activity is negatively correlated with body sway of mediolateral plane (r = -0.51, p = 0.02), and tibialis anterior activity is positively correlated with body sway on the mediolateral plane (r = 0.62, p = 0.004).

SIGNIFICANCE

Higher brain activity in motor-related areas, lower activity in the lower limb muscles and lower co-contraction of shank muscles were observed in stable postural control. These results will facilitate the planning of new rehabilitation methods for improving postural control ability.

PIMP Your Stride: Preferred Running Form to Guide Individualized Injury Rehabilitation

Despite the wealth of research on injury prevention and biomechanical risk factors for running related injuries, their incidence remains high. It was suggested that injury prevention and reconditioning strategies should consider spontaneous running forms in a more holistic view and not only the injury location or specific biomechanical patterns. Therefore, we propose an approach using the preferred running form assessed through the Volodalen^® method to guide injury prevention, rehabilitation, and retraining exercise prescription. This approach follows three steps encapsulated by the PIMP acronym. The first step (P) refers to the preferred running form assessment. The second step (I) is the identification of inefficiency in the vertical load management. The third step (MP) refers to the movement plan individualization. The answers to these three questions are guidelines to create individualized exercise pathways based on our clinical experience, biomechanical data, strength conditioning knowledge, and empirical findings in uninjured and injured runners. Nevertheless, we acknowledge that further scientific justifications with appropriate clinical trials and mechanistic research are required to substantiate the approach.

Explaining the differences of gait patterns between high and low-mileage runners with machine learning

Running gait patterns have implications for revealing the causes of injuries between higher-mileage runners and low-mileage runners. However, there is limited research on the possible relationships between running gait patterns and weekly running mileages. In recent years, machine learning algorithms have been used for pattern recognition and classification of gait features to emphasize the uniqueness of gait patterns. However, they all have a representative problem of being a black box that often lacks the interpretability of the predicted results of the classifier. Therefore, this study was conducted using a Deep Neural Network (DNN) model and Layer-wise Relevance Propagation (LRP) technology to investigate the differences in running gait patterns between higher-mileage runners and low-mileage runners. It was found that the ankle and knee provide considerable information to recognize gait features, especially in the sagittal and transverse planes. This may be the reason why high-mileage and low-mileage runners have different injury patterns due to their different gait patterns. The early stages of stance are very important in gait pattern recognition because the pattern contains effective information related to gait. The findings of the study noted that LRP completes a feasible interpretation of the predicted results of the model, thus providing more interesting insights and more effective information for analyzing gait patterns.

Characterization and Categorization of Various Human Lower Limb Movements Based on Kinematic Synergies

A proper movement categorization reduces the complexity of understanding or reproducing human movements in fields such as physiology, rehabilitation, and robotics, through partitioning a wide variety of human movements into representative sub-motion groups. However, how to establish a categorization (especially a quantitative categorization) for various human lower limb movements is rarely investigated in literature and remains challenging due to the diversity and complexity of the lower limb movements (diverse gait modes and interaction styles with the environment). Here we present a quantitative categorization for the various lower limb movements. To this end, a similarity measure between movements was first built based on limb kinematic synergies that provide a unified and physiologically meaningful framework for evaluating the similarities among different types of movements. Then, a categorization was established via hierarchical cluster analysis for thirty-four lower limb movements, including walking, running, hopping, sitting-down-standing-up, and turning in different environmental conditions. According to the movement similarities, the various movements could be divided into three distinct clusters (cluster 1: walking, running, and sitting-down-standing-up; cluster 2: hopping; cluster 3: turning). In each cluster, cluster-specific movement synergies were required. Besides the uniqueness of each cluster, similarities were also found among part of the synergies employed by these different clusters, perhaps related to common behavioral goals in these clusters. The mix of synergies shared across the clusters and synergies for specific clusters thus suggests the coexistence of the conservation and augmentation of the kinematic synergies underlying the construction of the diverse and complex motor behaviors. Overall, the categorization presented here yields a quantitative and hierarchical representation of the various lower limb movements, which can serve as a basis for the understanding of the formation mechanisms of human locomotion and motor function assessment and reproduction in related fields.

Classification of Ankle Joint Stiffness during Walking to Determine the Use of Ankle Foot Orthosis after Stroke

Categorization based on quasi-joint stiffness (QJS) may help clinicians select appropriate ankle foot orthoses (AFOs). The objectives of the present study were to classify the gait pattern based on ankle joint stiffness, also called QJS, of the gait in patients after stroke and to clarify differences in the type of AFO among 72 patients after stroke. Hierarchical cluster analysis was used to classify gait patterns based on QJS at least one month before the study, which revealed three distinct subgroups (SGs 1, 2, and 3). The proportion of use of AFOs, articulated AFOs, and non-articulated AFOs were significantly different among SGs 1-3. In SG1, with a higher QJS in the early and middle stance, the proportion of the patients using articulated AFOs was higher, whereas in SG3, with a lower QJS in both stances, the proportion of patients using non-articulated AFOs was higher. In SG2, with a lower QJS in the early stance and higher QJS in the middle stance, the proportion of patients using AFOs was lower. These findings indicate that classification of gait patterns based on QJS in patients after stroke may be helpful in selecting AFO. However, large sample sizes are required to confirm these results.

Analysis of Dexterity Motion by Singular Value Decomposition for Hand Movement Measured Using Inertial Sensors

Finger movements play an important role in many daily human actions. Among the studies on the dexterity of fingers required for various tasks in neurology and simple evaluation tests, few have focused on detailed finger movements themselves. Therefore, in this study, we improved the hand motion measurement system using inertial sensors and the motion analysis method developed in our previous study and measured the motion of the upper limbs (including the fingers) during a general finger dexterity test. By applying singular value decomposition to the obtained joint angles and decomposing them into simpler movement units, we obtained the timing of each movement unit and the purpose of each movement as the coordination state of the joints. By applying hierarchical clustering to multiple trials in a finger dexterity test, we also determined the similarity between trials and investigated the characteristics of movements with higher dexterity. We investigated the motor characteristics in finger dexterity by analyzing our results.

Characterising initial sprint acceleration strategies using a whole-body kinematics approach

Sprint acceleration is an important motor skill in team sports, thus consideration of techniques adopted during the initial steps of acceleration is of interest. Different technique strategies can be adopted due to multiple interacting components, but the reasons for, and performance implications of, these differences are unclear. 29 professional rugby union backs completed three maximal 30 m sprints, from which spatiotemporal variables and linear and angular kinematics during the first four steps were obtained. Leg strength qualities were also obtained from a series of strength tests for 25 participants, and 13 participants completed the sprint protocol on four separate occasions to assess the reliability of the observed technique strategies. Using hierarchical agglomerative cluster analysis, four clear participant groups were identified according to their normalised spatiotemporal variables. Whilst significant differences in several lower limb sprint kinematic and strength qualities existed between groups, there were no significant between-group differences in acceleration performance, suggesting inter-athlete technique degeneracy in the context of performance. As the intra-individual whole-body kinematic strategies were stable (mean CV = 1.9% to 6.7%), the novel approach developed and applied in this study provides an effective solution for monitoring changes in acceleration technique strategies in response to technical or physical interventions.

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis

Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods.

Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.

Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.

Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

Subgroups of Foot-Ankle Movement Patterns Can Influence the Responsiveness to a Foot-Core Exercise Program: A Hierarchical Cluster Analysis

The purpose of this study is to identify homogenous subgroups of foot-ankle (FA) kinematic patterns among recreational runners and further investigate whether differences in baseline movement patterns can influence the mechanical responses to a foot-core exercise intervention program. This is a secondary analysis of data from 85 participants of a randomized controlled trial (clinicaltrials.gov - NCT02306148) investigating the effects of an exercise-based therapeutic approach focused on FA complex. A validated skin marker-based multi-segment foot model was used to acquire kinematic data during the stance phase of treadmill running. Kinematic features were extracted from the time-series data using a principal component analysis, and the reduced data served as input for a hierarchical cluster analysis to identify subgroups of FA movement patterns. FA angle time series were compared between identified clusters and the mechanical effects of the foot-core exercise intervention was assessed for each subgroup. Two clusters of FA running patterns were identified, with cluster 1 (n = 36) presenting a pattern of forefoot abduction, while cluster 2 (n = 49) displayed deviations in the proximal segments, with a rearfoot adduction and midfoot abduction throughout the stance phase of running. Data from 29 runners who completed the intervention protocol were analyzed after 8-weeks of foot-core exercises, resulting in changes mainly in cluster 1 (n = 16) in the transverse plane, in which we observed a reduction in the forefoot abduction, an increase in the rearfoot adduction and an approximation of their pattern to the runners in cluster 2 (n = 13). The findings of this study may help guide individual-centered treatment strategies, taking into account their initial mechanical patterns.

Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework

The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings. We trained deep neural networks on a large set of synthetic inertial data derived from a clinical marker-based motion-tracking database of hundreds of subjects. We used data augmentation techniques and an automated calibration approach to reduce error due to variability in sensor placement and limb alignment. On left-out subjects, lower extremity kinematics could be predicted with a mean (±STD) root mean squared error of less than 1.27° (±0.38°) in flexion/extension, less than 2.52° (±0.98°) in ad/abduction, and less than 3.34° (±1.02°) internal/external rotation, across walking and running trials. Errors decreased exponentially with the amount of training data, confirming the need for large datasets when training deep neural networks. While this framework remains to be validated with true inertial measurement unit data, the results presented here are a promising advance toward convenient estimation of gait kinematics in natural environments. Progress in this direction could enable large-scale studies and offer new perspective into disease progression, patient recovery, and sports biomechanics.

Fatigue Induced Changes in Muscle Strength and Gait Following Two Different Intensity, Energy Expenditure Matched Runs

Purpose

To investigate changes in hip and knee strength, kinematics, and running variability following two energy expenditure matched training runs; a medium intensity continuous run (MICR) and a high intensity interval training session (HIIT).

Methods

Twenty (10 Females, 10 Males) healthy master class runners were recruited. Each participant completed the HIIT consisting of six repetitions of 800 m with a 1:1 work: rest ratio. The MICR duration was set to match energy expenditure of the HIIT session. Hip and knee muscular strength were examined pre and post both HIIT and MICR. Kinematics and running variability for hip and knee, along with spatiotemporal parameters were assessed at start and end of each run-type. Changes in variables were examined using both 2 × 2 ANOVAs with repeated measures and on an individual level when the change in a variable exceeded the minimum detectable change (MDC).

Results

All strength measures exhibited significant reductions at the hip and knee (P < 0.05) with time for both run-types; 12% following HIIT, 10.6% post MICR. Hip frontal plane kinematics increased post run for both maximum angle (P < 0.001) and range of motion (P = 0.003). Runners exhibited increased running variability for nearly all variables, with the HIIT having a greater effect. Individual assessment revealed that not all runners were effected post run and that following HIIT more runners had reduced muscular strength, altered kinematics and increased running variability.

Conclusion

Runners exhibited fatigue induced changes following typical training runs, which could potentially present risk of injury development. Group and individual assessment revealed different findings where the use of MDC is recommended over that of P-values.

Subclassification of recreational runners with a running-related injury based on running kinematics evaluated with marker-based two-dimensional video analysis

OBJECTIVES

To explore whether homogeneous subgroups could be discriminated within a population of recreational runners with a running-related injury based on running kinematics evaluated with marker-based two-dimensional video analysis.

DESIGN

Cross-sectional.

SETTING

Research laboratory.

PARTICIPANTS

Fifty-three recreational runners (15 males, 38 females) with a running-related injury.

MAIN OUTCOME MEASURES

Foot and tibia inclination at initial contact, and hip adduction and knee flexion at midstance were measured in the frontal and sagittal plane with marker-based two-dimensional video analysis during shod running on a treadmill at preferred speed. The four outcome measures were clustered using K-means cluster analysis (n = 2-10). Silhouette coefficients were used to detect optimal clustering.

RESULTS

The cluster analysis led to the classification of two distinct subgroups (mean silhouette coefficient = 0.53). Subgroup 1 (n = 39) was characterized by significantly greater foot inclination and tibia inclination at initial contact compared to subgroup 2 (n = 14).

CONCLUSION

The existence of different subgroups demonstrate that the same running-related injury can be represented by different kinematic presentations. A subclassification based on the kinematic presentation may help clinicians in their clinical reasoning process when evaluating runners with a running-related injury and could inform targeted intervention strategy development.

Associations between biomechanical and clinical/anthropometrical factors and running-related injuries among recreational runners: a 52-week prospective cohort study

BACKGROUND

The purpose of this exploratory study was to investigate whether runners with certain biomechanical or clinical/anthropometrical characteristics sustain more running-related injuries than runners with other biomechanical or clinical/anthropometrical characteristics.

METHODS

The study was designed as a prospective cohort with 52-weeks follow-up. A total of 224 injury-free, recreational runners were recruited from the Gothenburg Half Marathon and tested at baseline. The primary exposure variables were biomechanical and clinical/anthropometrical measures, including strength, lower extremity kinematics, joint range of motion, muscle flexibility, and trigger points. The primary outcome measure was any running-related injury diagnosed by a medical practitioner. Cumulative risk difference was used as measure of association. A shared frailty approach was used with legs as the unit of interest. A total of 448 legs were included in the analyses.

RESULTS

The cumulative injury incidence proportion for legs was 29.0% (95%CI = 24.0%; 34.8%). A few biomechanical and clinical/anthropometrical factors influence the number of running-related injuries sustained in recreational runners. Runners with a late timing of maximal eversion sustained 20.7% (95%CI = 1.3; 40.0) more injuries, and runners with weak abductors in relation to adductors sustained 17.3% (95%CI = 0.8; 33.7) more injuries, compared with the corresponding reference group.

CONCLUSIONS

More injuries are likely to occur in runners with late timing of maximal eversion or weak hip abductors in relation to hip adductors.