A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis

Structural and Organizational Strategies of Locomotor Modules during Landing in Patients with Chronic Ankle Instability

BACKGROUND

Human locomotion involves the coordinated activation of a finite set of modules, known as muscle synergy, which represent the motor control strategy of the central nervous system. However, most prior studies have focused on isolated muscle activation, overlooking the modular organization of motor behavior. Therefore, to enhance comprehension of muscle coordination dynamics during multi-joint movements in chronic ankle instability (CAI), exploring muscle synergies during landing in CAI patients is imperative.

METHODS

A total of 22 patients with unilateral CAI and 22 healthy participants were recruited for this research. We employed a recursive model for second-order differential equations to process electromyographic (EMG) data after filtering preprocessing, generating the muscle activation matrix, which was subsequently inputted into the non-negative matrix factorization model for extraction of the muscle synergy. Muscle synergies were classified utilizing the K-means clustering algorithm and Pearson correlation coefficients. Statistical parameter mapping (SPM) was employed for temporal modular parameter analyses.

RESULTS

Four muscle synergies were identified in both the CAI and healthy groups. In Synergy 1, only the gluteus maximus showed significantly higher relative weight in CAI compared to healthy controls (p = 0.0035). Synergy 2 showed significantly higher relative weights for the vastus lateralis in the healthy group compared to CAI (p = 0.018), while in Synergy 4, CAI demonstrated significantly higher relative weights of the vastus lateralis compared to healthy controls (p = 0.030). Furthermore, in Synergy 2, the CAI group exhibited higher weights of the tibialis anterior compared to the healthy group (p = 0.042).

CONCLUSIONS

The study suggested that patients with CAI exhibit a comparable modular organizational framework to the healthy group. Investigation of amplitude adjustments within the synergy spatial module shed light on the adaptive strategies employed by the tibialis anterior and gluteus maximus muscles to optimize control strategies during landing in patients with CAI. Variances in the muscle-specific weights of the vastus lateralis across movement modules reveal novel biomechanical adaptations in CAI, offering valuable insights for refining rehabilitation protocols.

Toe Box Shape of Running Shoes Affects In-Shoe Foot Displacement and Deformation: A Randomized Crossover Study

BACKGROUND

Long-distance running is popular but associated with a high risk of injuries, particularly toe-related injuries. Limited research has focused on preventive measures, prompting exploration into the efficacy of raised toe box running shoes.

PURPOSE

This study aimed to investigate the effect of running shoes with raised toe boxes on preventing toe injuries caused by distance running.

METHODS

A randomized crossover design involved 25 male marathon runners (height: 1.70 ± 0.02 m, weight: 62.6 + 4.5 kg) wearing both raised toe box (extended by 8 mm along the vertical axis and 3 mm along the sagittal axis) and regular toe box running shoes. Ground reaction force (GRF), in-shoe displacement, and degree of toe deformation (based on the distance change between the toe and the metatarsal head) were collected.

RESULTS

Wearing raised toe box shoes resulted in a significant reduction in vertical (p = 0.001) and antero-posterior (p = 0.015) ground reaction forces during the loading phase, with a notable increase in vertical ground reaction force during the toe-off phase (p < 0.001). In-shoe displacement showed significant decreased movement in the forefoot medial (p < 0.001) and rearfoot (medial: p < 0.001, lateral: p < 0.001) and significant increased displacement in the midfoot (medial: p = 0.002, lateral: p < 0.001). Impact severity on the hallux significantly decreased (p < 0.001), while impact on the small toes showed no significant reduction (p = 0.067).

CONCLUSIONS

Raised toe box running shoes offer an effective means of reducing toe injuries caused by long-distance running.

Development and validation of a novel ankle joint musculoskeletal model

An improved understanding of contact mechanics in the ankle joint is paramount for implant design and ankle disorder treatment. However, existing models generally simplify the ankle joint as a revolute joint that cannot predict contact characteristics. The current study aimed to develop a novel musculoskeletal ankle joint model that can predict contact in the ankle joint, together with muscle and joint reaction forces. We modelled the ankle joint as a multi-axial joint and simulated contact mechanics between the tibia, fibula and talus bones in OpenSim. The developed model was validated with results from experimental studies through passive stiffness and contact. Through this, we found a similar ankle moment-rotation relationship and contact pattern between our study and experimental studies. Next, the musculoskeletal ankle joint model was incorporated into a lower body model to simulate gait. The ankle joint contact characteristics, kinematics, and muscle forces were predicted and compared to the literature. Our results revealed a comparable peak contact force and the same muscle activation patterns in four major muscles. Good agreement was also found in ankle dorsi/plantar-flexion and inversion/eversion. Thus, the developed model was able to accurately model the ankle joint and can be used to predict contact characteristics in gait.

Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model

BACKGROUND

Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%). DVT increases hospitalization risk, and complications, and impacts prognosis. Accurate prognostication and timely intervention are crucial to prevent DVT progression and improve patient outcomes.

METHODS

This study introduces an effective DVT prediction model, named bSES-AC-RUN-FKNN, which integrates fuzzy k-nearest neighbor (FKNN) with enhanced Runge-Kutta optimizer (RUN). Recognizing the insufficient effectiveness of RUN in local search capability and its convergence accuracy, spherical evolutionary search (SES) and differential evolution-inspired knowledge adaptive crossover (AC) are incorporated, termed SES-AC-RUN, to enhance its optimization capability.

RESULTS

Based on the benchmark set by CEC 2017 and comparative analyses with several peers, it is evident that SES-AC-RUN significantly enhances search performance compared to traditional RUN, even standing comparably against leading championship algorithms. The proposed bSES-AC-RUN-FKNN model was applied to predict a dataset comprising 424 cases of DVT patients, totaling 7208 records. Remarkably, the model demonstrates outstanding accuracy, reaching 91.02%, alongside commendable sensitivity at 91.07%.

CONCLUSIONS

The bSES-AC-RUN-FKNN emerges as a robust and efficient predictive tool, significantly enhancing the accuracy of DVT prediction. This model can be used to manage the risk of thrombosis in the care of COVID-19 patients. Nursing staff can combine the model's predictions with clinical judgment to formulate comprehensive treatment approaches.

Exploring biomechanical variations in ankle joint injuries among Latin dancers with different stance patterns: utilizing OpenSim musculoskeletal models

Background: Dancers represent the primary demographic affected by ankle joint injuries. In certain movements, some Latin dancers prefer landing on the Forefoot (FT), while others prefer landing on the Entire foot (ET). Different stance patterns can have varying impacts on dancers' risk of ankle joint injuries. The purpose of this study is to investigate the differences in lower limb biomechanics between Forefoot (FT) dancers and Entire foot (ET) dancers. Method: A group of 21 FT dancers (mean age 23.50 (S.D. 1.12) years) was compared to a group of 21 ET dancers (mean age 23.33 (S.D. 0.94) years), performing the kicking movements of the Jive in response to the corresponding music. We import data collected from Vicon and force plates into OpenSim to establish musculoskeletal models for computing kinematics, dynamics, muscle forces, and muscle co-activation. Result: In the sagittal plane: ankle angle (0%-100%, p < 0.001), In the coronal plane: ankle angle (0%-9.83%, p = 0.001) (44.34%-79.52%, p = 0.003), (88.56%-100%, p = 0.037), ankle velocity (3.73%-11.65%, p = 0.017) (94.72-100%, p = 0.031); SPM analysis revealed that FT dancers exhibited significantly smaller muscle force than ET dancers around the ankle joint during the stance phase. Furthermore, FT dancers displayed reduced co-activation compared to ET dancers around the ankle joint during the descending phase, while demonstrating higher co-activation around the knee joint than ET dancers. Conclusion: This study biomechanically demonstrates that in various stance patterns within Latin dance, a reduction in lower limb stance area leads to weakened muscle strength and reduced co-activation around the ankle joint, and results in increased ankle inversion angles and velocities, thereby heightening the risk of ankle sprains. Nevertheless, the increased co-activation around the knee joint in FT dancers may be a compensatory response for reducing the lower limb stance area in order to maintain stability.

Does Obesity Affect the Rate of Force Development in Plantar Flexor Muscles among Older Adults?

The literature offers limited information on the effect of obesity on the rate of force development (RFD), a critical parameter for mobility in older adults. The objectives of this study were to explore the influence of obesity on the RFD in older adults and to examine the association between this neuromuscular parameter and walking speed. The participants (42 older adults) were classified into two groups: the control group (CG, n = 22; mean age = 81.13 ± 4.02 years; body mass index (BMI) = 25.13 ± 3.35 kg/m2), and the obese group (OG, n = 20; mean age = 77.71 ± 2.95 years; BMI = 34.46 ± 3.25 kg/m2). Walking speed (m/s) was measured using the 10 m walking test. Neuromuscular parameters of the plantar flexors were evaluated during a maximal voluntary contraction test using a dynamometer. The RFD was calculated from the linear slop of the force-time curve in the following two phases: from the onset of the contraction to 50 ms (RFD0-50) and from 100 to 200 ms (RFD100-200). The gait speed was lower in the OG compared to the CG (p < 0.001). The RFD50/100 and RFD100/200 were lower in the OG compared to the CG (p < 0.001). The RFD50/100 was found to be the predominant influencer on gait speed in the OG. In conclusion, obesity negatively impacts the RFD in older adults and RFD stands out as the primary factor among the studied parameters influencing gait speed.

Unicompartmental Knee Arthroplasty for Osteoarthritis Eliminates Lateral Thrust: Associations between Lateral Thrust Detected by Inertial Measurement Units and Clinical Outcomes

The purpose of this study was to investigate the relationship between clinical outcomes and lateral thrust before and after unicompartmental knee arthroplasty (UKA) using inertial measurement sensor units. Eleven knees were evaluated with gait analysis. The varus angular velocity was used to evaluate lateral thrust. The femorotibial angle (FTA) and hip-knee-ankle angle (HKA) were used to evaluate lower-limb alignment, and the Oxford Knee Score (OKS) and Japanese Orthopaedic Association Score (JOA) were used to evaluate clinical outcomes. The mean pre-UKA peak varus velocity was 37.1 ± 9.8°/s, and that for post-UKA was 28.8 ± 9.1°/s (p = 0.00003), such that instabilities clearly improved. Assuming the definition of lateral thrust is when the varus angular velocity is more than 28.1°/s, 81.8% of patients had lateral thrust preoperatively, but this decreased to 55.6% postoperatively, such that the symptoms and objective findings improved. Both OKS and JOA improved after surgery. In addition, HKA was -7.9° preoperatively and -5.8° postoperatively (p = 0.024), and FTA was 181.4° preoperatively and 178.4° postoperatively (p = 0.012). There was a positive correlation between postoperative JOA and FTA, indicating that changes in postoperative alignment affected clinical outcomes. This study quantitatively evaluated the disappearance of lateral thrust by UKA, and it found that the stability can be achieved by UKA for unstable knees with lateral thrust.

The Lower Limb Muscle Co-Activation Map during Human Locomotion: From Slow Walking to Running

The central nervous system (CNS) controls movements and regulates joint stiffness with muscle co-activation, but until now, few studies have examined muscle pairs during running. This study aims to investigate differences in lower limb muscle coactivation during gait at different speeds, from walking to running. Nineteen healthy runners walked and ran at speeds ranging from 0.8 km/h to 9.3 km/h. Twelve lower limb muscles' co-activation was calculated using the time-varying multi-muscle co-activation function (TMCf) with global, flexor-extension, and rostro-caudal approaches. Spatiotemporal and kinematic parameters were also measured. We found that TMCf, spatiotemporal, and kinematic parameters were significantly affected by gait speed for all approaches. Significant differences were observed in the main parameters of each co-activation approach and in the spatiotemporal and kinematic parameters at the transition between walking and running. In particular, significant differences were observed in the global co-activation (CIglob, main effect F(1,17) = 641.04, p < 0.001; at the transition p < 0.001), the stride length (main effect F(1,17) = 253.03, p < 0.001; at the transition p < 0.001), the stride frequency (main effect F(1,17) = 714.22, p < 0.001; at the transition p < 0.001) and the Center of Mass displacement in the vertical (CoMy, main effect F(1,17) = 426.2, p < 0.001; at the transition p < 0.001) and medial-lateral (CoMz, main effect F(1,17) = 120.29 p < 0.001; at the transition p < 0.001) directions. Regarding the correlation analysis, the CoMy was positively correlated with a higher CIglob (r = 0.88, p < 0.001) and negatively correlated with Full Width at Half Maximum (FWHMglob, r = -0.83, p < 0.001), whereas the CoMz was positively correlated with the global Center of Activity (CoAglob, r = 0.97, p < 0.001). Positive and negative strong correlations were found between global co-activation parameters and center of mass displacements, as well as some spatiotemporal parameters, regardless of gait speed. Our findings suggest that walking and running have different co-activation patterns and kinematic characteristics, with the whole-limb stiffness exerted more synchronously and stably during running. The co-activation indexes and kinematic parameters could be the result of global co-activation, which is a sensory-control integration process used by the CNS to deal with more demanding and potentially unstable tasks like running.

A new method applied for explaining the landing patterns: Interpretability analysis of machine learning

As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis. Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition. The current work highlights the applicability of XML methods that can not only satisfy the traditional decision problem between classes, but also largely solve the lack of transparency in landing pattern recognition. We provide a feasible framework for realizing interpretability of ML decision results in landing analysis, providing a methodological reference and solid foundation for future clinical diagnosis and biomechanical analysis.

Compensation Method for Missing and Misidentified Skeletons in Nursing Care Action Assessment by Improving Spatial Temporal Graph Convolutional Networks

With the increasing aging population, nursing care providers have been facing a substantial risk of work-related musculoskeletal disorders (WMSDs). Visual-based pose estimation methods, like OpenPose, are commonly used for ergonomic posture risk assessment. However, these methods face difficulty when identifying overlapping and interactive nursing tasks, resulting in missing and misidentified skeletons. To address this, we propose a skeleton compensation method using improved spatial temporal graph convolutional networks (ST-GCN), which integrates kinematic chain and action features to assess skeleton integrity and compensate for it. The results verified the effectiveness of our approach in optimizing skeletal loss and misidentification in nursing care tasks, leading to improved accuracy in calculating both skeleton joint angles and REBA scores. Moreover, comparative analysis against other skeleton compensation methods demonstrated the superior performance of our approach, achieving an 87.34% REBA accuracy score. Collectively, our method might hold promising potential for optimizing the skeleton loss and misidentification in nursing care tasks.

Wearable devices for gait and posture monitoring via telemedicine in people with movement disorders and multiple sclerosis: a systematic review

INTRODUCTION

Wearable devices and telemedicine are increasingly used to track health-related parameters across patient populations. Since gait and postural control deficits contribute to mobility deficits in persons with movement disorders and multiple sclerosis, we thought it interesting to evaluate devices in telemedicine for gait and posture monitoring in such patients.

METHODS

For this systematic review, we searched the electronic databases MEDLINE (PubMed), SCOPUS, Cochrane Library, and SPORTDiscus. Of the 452 records retrieved, 12 met the inclusion/exclusion criteria. Data about (1) study characteristics and clinical aspects, (2) technical, and (3) telemonitoring and teleconsulting were retrieved, The studies were quality assessed.

RESULTS

All studies involved patients with Parkinson's disease; most used triaxial accelerometers for general assessment (n = 4), assessment of motor fluctuation (n = 3), falls (n = 2), and turning (n = 3). Sensor placement and count varied widely across studies. Nine used lab-validated algorithms for data analysis. Only one discussed synchronous patient feedback and asynchronous teleconsultation.

CONCLUSIONS

Wearable devices enable real-world patient monitoring and suggest biomarkers for symptoms and behaviors related to underlying gait disorders. thus enriching clinical assessment and personalized treatment plans. As digital healthcare evolves, further research is needed to enhance device accuracy, assess user acceptability, and integrate these tools into telemedicine infrastructure.

PROSPERO REGISTRATION

CRD42022355460.

KeepRunning: A MoCap-Based Rapid Test to Prevent Musculoskeletal Running Injuries

The worldwide popularisation of running as a sport and recreational practice has led to a high rate of musculoskeletal injuries, usually caused by a lack of knowledge about the most suitable running technique for each runner. This running technique is determined by a runner's anthropometric body characteristics, dexterity and skill. Therefore, this study aims to develop a motion capture-based running analysis test on a treadmill called KeepRunning to obtain running patterns rapidly, which will aid coaches and clinicians in assessing changes in running technique considering changes in the study variables. Therefore, a review and proposal of the most representative events and variables of analysis in running was conducted to develop the KeepRunning test. Likewise, the minimal detectable change (MDC) in these variables was obtained using test-retest reliability to demonstrate the reproducibility and viability of the test, as well as the use of MDC as a threshold for future assessments. The test-retest consisted of 32 healthy volunteer athletes with a running training routine of at least 15 km per week repeating the test twice. In each test, clusters of markers were placed on the runners' body segments using elastic bands and the volunteers' movements were captured while running on a treadmill. In this study, reproducibility was defined by the intraclass correlation coefficient (ICC) and MDC, obtaining a mean value of ICC = 0.94 ± 0.05 for all variables and MDC = 2.73 ± 1.16° for the angular kinematic variables. The results obtained in the test-retest reveal that the reproducibility of the test was similar or better than that found in the literature. KeepRunning is a running analysis test that provides data from the involved body segments rapidly and easily interpretable. This data allows clinicians and coaches to objectively provide indications for runners to improve their running technique and avoid possible injury. The proposed test can be used in the future with inertial motion capture and other wearable technologies.