Multi-Segmental Motion in Foot during Counter-Movement Jump with Toe Manipulation

One-dimension statistical parametric mapping in lower limb biomechanical analysis: A systematic scoping review

BACKGROUND

Biomechanics significantly impacts sports performance and injury prevention. Traditional methods like discrete point analysis simplify continuous kinetic and kinematic data, while one-dimensional Statistical Parametric Mapping (spm1d) evaluates entire movement curves. Nevertheless, spm1d's application in sports and injury research is limited. As no systematic review exists, we conducted a scoping systematic review, synthesizing the current applications of spm1d across various populations, activities, and injuries. This review concludes by identifying gaps in the literature and suggesting areas for future research.

RESEARCH QUESTION

What research exists using spm1d in sports biomechanics, focusing on the lower limbs, in what populations, and what are the current research gaps?

METHODS

We searched PubMed, Embase, Web of Science, and ProQuest databases for the following search string: "(((knee) OR (hip)) OR (ankle)) OR (foot) OR (feet) AND (statistical parametric mapping)". English peer-reviewed studies assessing lower limb kinetics or kinematics in different sports or sports-related injuries were included. Reviews, meta-analyses, conference abstracts, and grey literature were excluded.

RESULTS

Our search yielded 165 papers published since 2012. Among these, 112 examined healthy individuals (67 %), and 53 focused on injured populations (33 %). Running (n = 45), cutting (n = 25), and jumping/landing (n = 18) were the most common activities. The predominant injuries were anterior cruciate ligament rupture (n = 21), chronic ankle instability (n = 18), and hip-related pain (n = 9). The main research gaps included the unbalanced populations, underrepresentation of common sports and sport-related injuries, gender inequality, a lack of studies in non-laboratory settings, a lack of studies on varied sports gear, and a lack of reporting standardization.

SIGNIFICANCE

This review spotlights crucial gaps in spm1d research within sports biomechanics. Key issues include a lack of studies beyond laboratory settings, underrepresentation of various sports and injuries, and gender disparities in research populations. Addressing these gaps can significantly enhance the application of spm1d in sports performance, injury analysis, and rehabilitation.

Toward improved understanding of foot shape, foot posture, and foot biomechanics during running: A narrative review

The current narrative review has explored known associations between foot shape, foot posture, and foot conditions during running. The artificial intelligence was found to be a useful metric of foot posture but was less useful in developing and obese individuals. Care should be taken when using the foot posture index to associate pronation with injury risk, and the Achilles tendon and longitudinal arch angles are required to elucidate the risk. The statistical shape modeling (SSM) may derive learnt information from population-based inference and fill in missing data from personalized information. Bone shapes and tissue morphology have been associated with pathology, gender, age, and height and may develop rapid population-specific foot classifiers. Based on this review, future studies are suggested for 1) tracking the internal multi-segmental foot motion and mapping the biplanar 2D motion to 3D shape motion using the SSM; 2) implementing multivariate machine learning or convolutional neural network to address nonlinear correlations in foot mechanics with shape or posture; 3) standardizing wearable data for rapid prediction of instant mechanics, load accumulation, injury risks and adaptation in foot tissue and bones, and correlation with shapes; 4) analyzing dynamic shape and posture via marker-less and real-time techniques under real-life scenarios for precise evaluation of clinical foot conditions and performance-fit footwear development.

Automatic Classification of Barefoot and Shod Populations Based on the Foot Metrics and Plantar Pressure Patterns

The human being’s locomotion under the barefoot condition enables normal foot function and lower limb biomechanical performance from a biological evolution perspective. No study has demonstrated the specific differences between habitually barefoot and shod cohorts based on foot morphology and dynamic plantar pressure during walking and running. The present study aimed to assess and classify foot metrics and dynamic plantar pressure patterns of barefoot and shod people via machine learning algorithms. One hundred and forty-six age-matched barefoot (n = 78) and shod (n = 68) participants were recruited for this study. Gaussian Naïve Bayes were selected to identify foot morphology differences between unshod and shod cohorts. The support vector machine (SVM) classifiers based on the principal component analysis (PCA) feature extraction and recursive feature elimination (RFE) feature selection methods were utilized to separate and classify the barefoot and shod populations via walking and running plantar pressure parameters. Peak pressure in the M1-M5 regions during running was significantly higher for the shod participants, increasing 34.8, 37.3, 29.2, 31.7, and 40.1%, respectively. The test accuracy of the Gaussian Naïve Bayes model achieved an accuracy of 93%. The mean 10-fold cross-validation scores were 0.98 and 0.96 for the RFE- and PCA-based SVM models, and both feature extract-based and feature select-based SVM models achieved an accuracy of 95%. The foot shape, especially the forefoot region, was shown to be a valuable classifier of shod and unshod groups. Dynamic pressure patterns during running contribute most to the identification of the two cohorts, especially the forefoot region.

Effect of Long-Distance Running on Inter-segment Foot Kinematics and Ground Reaction Forces: A Preliminary Study

Long-distance running has gained massive popularity in recent years, yet the intra-foot adaptations during this event remain unclear. This study aimed to examine the kinematic and ground reaction force alterations induced within the foot following a 5 and 10 km run using the Oxford Foot Model Ten marathon-experienced recreational runners participated in this study. Five-kilometer running led to more rearfoot dorsiflexion, rearfoot eversion, and rearfoot rotation while less forefoot plantarflexion during the stance phase. Increased rearfoot plantarflexion, while decreased forefoot plantarflexion, supination, adduction, and hallux plantarflexion were observed at 10 km. In addition, the forefoot space of footwear was found to play a role in hallux kinematics. Concerning GRFs, only a lesser propulsive force was presented after a 10 km run. Findings of this study showed that 5 km of running would induce excessive foot motion while 10 km of running may gradually change the foot posture and lead to reduced propulsive forces, which could potentially increase the risks of running-related injuries (RRI) due to overuse or fatigue. Nevertheless, further research is warranted, and this study could be used as a preliminary reference to evaluate and predict foot running-related injuries.

Activity recognition using multiplex limited penetrable visibility graph

To solve the dynamic problem of different activities in human activity recognition research, an activity recognition method based on a multiplex limited penetrable visibility graph is proposed. The 21 pressure values for each sampling are mapped to nodes in the first-layer network; then the average path length of nodes in the asynchronous periodic network is obtained, and the second-layer network used to explore different activities is built. Finally, the characteristic parameters and dynamic characteristics of different activities are explored and analyzed. The experimental results demonstrate that through the joint distribution of the average clustering coefficient and the maximum degree parameter of the node, the discrimination problems of different postures can be better realized, and it has good adaptability. It provides a new approach to gait recognition research that can be used in medical clinical diagnosis, rehabilitation training, and public health.

Local dynamic stability of the lower-limb as a means of post-hoc injury classification

Since most sporting injuries occur at the lower extremity (50% to 66%) and many of those injuries occur at the knee (30% to 45%), it is important to have robust metrics to measure risk of knee injury. Dynamic measures of knee stability are not commonly used in existing metrics but could provide important context to knee health and improve injury screening effectiveness. This study used the Local Dynamic Stability (LDS) of knee kinematics during a repetitive vertical jump to perform a post-hoc previous injury classification of participants. This study analyzed the kinematics from twenty-seven female collegiate division 1 (D1) soccer, D1 basketball, and club soccer athletes from Auburn University (height = 171 ± 8.9cm, weight = 66.3 ± 8.6kg, age = 19.8 ± 1.9yr), with 7 subjects having sustained previous knee injury requiring surgery and 20 subjects with no history of injury. This study showed that LDS correctly identified 84% of previously injured and uninjured subjects using a multivariate logistic regression during a fatigue jump task. Findings showed no statistical difference in kinematic position at maximum knee flexion during all jumps between previously injured and uninjured subjects. Additionally, kinematic positioning at maximum knee flexion was not indicative of LDS values, which would indicate that future studies should look specifically at LDS with respect to injury prevention as it cannot be effectively inferred from kinematics. These points suggest that the LDS preserves information about subtle changes in movement patterns that traditional screening methods do not, and this information could allow for more effective injury screening tests in the future.

Biomechanical Characteristics between Bionic Shoes and Normal Shoes during the Drop-Landing Phase: A Pilot Study

With the development of unstable footwear, more research has focused on the advantages of this type of shoe. This type of shoe could improve the muscle function of the lower limb and prevent injury risks in dynamic situations. Therefore, the purpose of this study was to investigate differences in lower-limb kinetics and kinematics based on single-leg landing (SLL) using normal shoes (NS) and bionic shoes (BS). The study used 15 male subject volunteers (age 23.4 ± 1.14 years, height 177.6 ± 4.83cm, body weight (BW) 73.6 ± 7.02 kg). To ensure the subject standardization of the participants, there were several inclusion criteria used for selection. There were two kinds of experimental shoes used in the landing experiment to detect the change of lower limbs when a landing task was performed. Kinetics and kinematic data were collected during an SLL task, and statistical parametric mapping (SPM) analysis was used to evaluate the differences between NS and BS. We found that the flexion and extension angles of the knee (p = 0.004) and hip (p = 0.046, p = 0.018) joints, and the dorsiflexion and plantarflexion of ankle (p = 0.031) moment were significantly different in the sagittal planes. In the frontal plane, the eversion and inversion of the ankle (p = 0.016), and the abduction and adduction of knee (p = 0.017, p = 0.007) angle were found significant differences. In the horizontal plane, the external and internal rotation of hip (p = 0.036) and knee (p < 0.001, p = 0.029) moment were found significant differences, and knee angle (p = 0.043) also. According to our results, we conclude that using BS can cause bigger knee and hip flexion than NS. Also, this finding indicates that BS might be considered to reduce lower-limb injury risk during the SLL phase.