Extraction of basic movement from whole-body movement, based on gait variability

Implications of Optimal Feedback Control Theory for Sport Coaching and Motor Learning: A Systematic Review

Best practice in skill acquisition has been informed by motor control theories. The main aim of this study is to screen existing literature on a relatively novel theory, Optimal Feedback Control Theory (OFCT), and to assess how OFCT concepts can be applied in sports and motor learning research. Based on 51 included studies with on average a high methodological quality, we found that different types of training seem to appeal to different control processes within OFCT. The minimum intervention principle (founded in OFCT) was used in many of the reviewed studies, and further investigation might lead to further improvements in sport skill acquisition. However, considering the homogenous nature of the tasks included in the reviewed studies, these ideas and their generalizability should be tested in future studies.

Statistical kinematic modelling: concepts and model validity

Data reduction techniques are applied to reduce the volume of data while maintaining its integrity. For cyclic motion data, a reliable overview comparing these methods is lacking. Therefore, this study aims to evaluate the features of the different data reduction techniques by applying them to large public data sets. The periodicity of cyclic motion can be exploited by either analysing a single cycle or studying a series of cycles. Analysing single cycles requires a pre-processing step to isolate the amplitude variability. Three different alignment techniques were evaluated, namely Linear length normalisation (LLN), piecewise LLN (PLLN) and continuous registration (CR). CR showed to remove the most phase variation. For the data reduction, three techniques were assessed (i.e., principal component analysis (PCA), principal polynomial analysis (PPA) and multivariate functional PCA (MFPCA)) based on the in- and out-of-sample error, the compactness and the computation time. The differences were found to be minimal. From our results, PPA appeared to be most useful for data compression. Further, we recommend PCA and MFPCA for classification and feature extraction purposes. We suggest the use of PCA when computation time is key and we advise the use of MFPCA when the inclusion of different data sources is desired. In contrast, the analysis of a series of cycles requires a pre-processing step to decompose the series. Further, a regression model was used to compensate for the difference in fundamental frequency. PCA on FC and MFPCA with splines were applied on the frequency compensated curves. Both methods performed as good.

Task Demand Changes Motor Control Strategies in Vertical Jumping

The purpose of this study was to examine the motor control strategies employed to control the degrees of freedom when performing a lower limb task with constraints applied at the hip, knee, and ankle. Thirty-five individuals performed vertical jumping tasks: hip flexed, no knee bend, and plantar flexed. Joint moment data from the hip, knee, and ankle were analyzed using principal component analysis (PCA). In all PCA performed, a minimum of two and maximum of six principal components (PC) were required to describe the movements. Similar reductions in dimensionality were observed in the hip flexed and no knee bend conditions (3PCs), compared to the plantar flexed condition (5PCs). A proximal to distal reduction in variability was observed for the hip flexed and no knee bend conditions but not for the plantar flexed condition. Collectively, the results suggest a reduction in the dimensionality of the movement occurs despite the constraints imposed within each condition and would suggest that dimensionality reduction and motor control strategies are a function of the task demands.

Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions

The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle “big data”. Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V’s definition of big data: volume, velocity, variety, veracity, and value. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics. These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction (feature selection and extraction), and learning algorithms (classification and clustering). Finally, a promising big data exploration tool called “topological data analysis” and directions for future research are outlined and discussed.

Human runners exhibit a least variable gait speed

This study characterises the relationship between gait variability and speed in runners using data from trunk accelerations in each axis. Twelve participants of varying fitness ran on the treadmill with three sessions of six randomly ordered self-selected speeds. A VO_2max test was conducted on the fourth session. Running gait was tracked with inertial sensors. The occurrence of a mid-range speed was analysed for the anterior-posterior, vertical and lateral directional coefficient of variation (CV) of root mean square (RMS) acceleration data. One participant with noisy gait signals was omitted. The results show all remaining participants consistently showed significant quadratic U-shaped relationships between vertical RMS CV acceleration and speed. Neither anterior-posterior nor lateral RMS CV acceleration were clearly related to speed. These least variable gait speeds were similar to estimates of optimal speed derived from minimum cost of transport with speed. In conclusion, there exists a mid-range speed for each runner with the least variable gait in the vertical direction, and this occurred significantly more often than would be expected by chance (P < 0.05). However, there are no prominent patterns for the anterior-posterior and lateral directions. This finding supports anecdotal evidence from runners and coaches concerning gait consistency.

Neuromuscular Strategies during Cycling at Different Muscular Demands

PURPOSE

This study investigated muscle coordination while pedaling at 150 and 300 W with a cadence of 90 rpm. Changes in the variability of the electromyographic (EMG) signals were quantified in 14 subjects.

METHODS

Principal component analysis was used to find correlated EMG patterns among seven leg muscles that reflect neuromuscular strategies while pedaling. Sample entropy was used to assess the regularity of the short-term fluctuations of the EMG. Signal structure relates to the autocorrelation and to the information in the phase of the signal. This study used the information encrypted in the phase to quantify neuromuscular control and compared the results to phase-randomized surrogate data.

RESULTS

Although the pattern remained similar, the correlation between individual muscles showed effort-dependent differences. Increased workload altered the overall neuromuscular strategy indicated by changes in the contribution of individual muscles to the movement. Additionally, the executed strategy was characterized by increased structure. Regularity of the short-term fluctuations in the EMG increased significantly with effort level. Both experimental conditions showed more structure in the phase of the EMG compared to the surrogate data.

CONCLUSIONS

This increased structure in the EMG signal may represent a less random and more orderly recruited firing pattern during the pedaling task at higher effort levels.

Influence of the Lower Jaw Position on the Running Pattern

Introduction

The effects of manipulated dental occlusion on body posture has been investigated quite often and discussed controversially in the literature. Far less attention has been paid to the influence of dental occlusion position on human movement. If human movement was analysed, it was mostly while walking and not while running. This study was therefore designed to identify the effect of lower jaw positions on running behaviour according to different dental occlusion positions.

Methods

Twenty healthy young recreational runners (mean age = 33.9±5.8 years) participated in this study. Kinematic data were collected using an eight-camera Vicon motion capture system (VICON Motion Systems, Oxford, UK). Subjects were consecutively prepared with four different dental occlusion conditions in random order and performed five running trials per test condition on a level walkway with their preferred running shoes. Vector based pattern recognition methods, in particular cluster analysis and support vector machines (SVM) were used for movement pattern identification.

Results

Subjects exhibited unique movement patterns leading to 18 clusters for the 20 subjects. No overall classification of the splint condition could be observed. Within individual subjects different running patterns could be identified for the four splint conditions. The splint conditions lead to a more symmetrical running pattern than the control condition.

Discussion

The influence of an occlusal splint on running pattern can be confirmed in this study. Wearing a splint increases the symmetry of the running pattern. A more symmetrical running pattern might help to reduce the risk of injuries or help in performance. The change of the movement pattern between the neutral condition and any of the three splint conditions was significant within subjects but not across subjects. Therefore the dental splint has a measureable influence on the running pattern of subjects, however subjects individuality has to be considered when choosing the optimal splint condition for a specific subject.