A biomechanical and physiological investigation of atypical gaits used in badminton

This thesis is concerned with quantifying the biomechanics and physiological consequences of sport-specific movements in order to answer the question if atypical movements in badminton result in abnormally large demands that could be linked to the relatively high levels of injuries sustained. An initial study of the movement repertoire used in competitive badminton established that sidestepping (SS), crossover stepping (XS) and lunging movements make an important contribution to the game. These movements are related, within the context of the game, and were viewed as a unit. In order to assess the potential injury risk posed by these atypical movements a series of experiments was performed to record the biomechanical as well as physiological demands of SS, XS and lunging for experienced, inexperienced, male and female badminton players. The first of these studies concerned the kinematics and kinetics of preferred speed SS and XS. This was followed up by an investigation of the electrical activity of 7 muscles of the leading and trailing limb and a comparative assessment of their metabolic demands was performed. The biomechanics of lunging were thereafter investigated, followed by a final investigation of the kinematics of atypical movement use in the competitive setting. The results from these investigations indicate that lateral stepping tasks result in biomechanical demands that are within the range expected for running. An asymmetric contribution of the leading and trailing limb to the gait cycle was identified as well as a shift toward the use of proximal joints for force production. Furthermore, no significant difference in metabolic power between SS, XS and running was identified. Differences in the demands of different lunging movements were observed with implications for both injury prevention and performance enhancement. Overall it was observed that the data recorded in these investigations was in agreement with the competitive, real-life application. Based on the findings in this research it can be concluded that lateral stepping movements in badminton do not appear to expose the participant to abnormally large biomechanical or physiological demands and other factors related to movement may be involved in the relatively high levels of injury sustained.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

MULTI MUSCLE PATTERNS IN POST SURGICAL TOTAL KNEE ARTHROPLASTY

INTRODUCTION Total Knee Arthroplasty (TKA) is a common surgical intervention for end stage Osteoarthritis (OA).  It is implemented for pain relief and joint function restoration. There is an increasing expectation from patients concerning post-operative performance [1]. However, TKA patients often experience functional impairment such as movement, loading, and muscle activation pattern abnormalities [2]. This project focused on identifying temporal differences in muscle activations in lower limb electromyographs (EMG) between healthy persons and TKA patients, using wavelet patterns and machine learning classification. METHODS Ten post-surgical female TKA patients (TKA: 19±3 months post-surgery; 61.9±8.8 yrs; BMI 28.0±5.3) and 9 healthy age matched female controls (CON: 61.4±7.4 yrs; BMI 25.6±2.4) participated in this study. EMGs for 7 lower leg muscles during both level walking and stair climbing were collected. Muscles of interest were: tibialis anterior (TA), gastrocnemius (GAS), semitendinosus (SEM), biceps femoris (BF), rectus femoris (RF), vastus medialis (VM) and vastus lateralis (VL). Five acceptable EMG trials per subject were selected for analysis, normalized in time (stance phase ±30%) and processed in Matlab using a wavelet transform [3]. EMG data were normalized to the total signal intensity. Support Vector Machine (SVM) classification was performed for all subjects using an iterative thresholding approach and leave-one-out cross-validation. Rates of classification (called recognition rates) were deemed significant if they were greater than or equal to 68.4% (according to binomial test). SVM discriminants were visualized to aid in the identification of functional differences between CON and TKA groups. RESULTS Mean multi muscle patterns (MMPs) for walking (Figure 1) demonstrate substantive differences between groups. The muscles that gave significant recognition rates in level walking were: VM (68.4%) and BF (73.7%); and in stair climbing were: BF (84.2%), SEM (73.7%), GAS (68.4%), and TA (68.4%). DISCUSSION Application of a SVM with iterative thresholding provided significant recognition rates between groups, for both walking and stair-stepping tasks. The stepping task was characterized by a greater number of muscles with significant recognition rates, as well as the highest recognition rates. Temporal activation differences, indicative of muscle co-activation by TKA subjects, were observable in the discriminant pattern. In walking, BF and VM were active in mid-stance, illustrated as a red activity pattern in the discriminant. BF activity shifted from pre-HS to post-HS to coincide with the main activity in the quadriceps muscles (VL, VM, RF). Similarly, in stair climbing, TA displayed a co-activation with GAS at mid-stance. Further, SEM and BF displayed pronounced activation patterns at mid-stance and at TO and early swing. This may indicate an activation strategy to assist in hip extension for the TKA group. CONCLUSION The analysis approach chosen in this study identified functional differences between healthy subjects and TKA patients. There is evidence for the employment of co-activation strategy by the TKA group in both walking and stair climbing

A biomechanical analysis of common lunge tasks in badminton

The lunge is regularly used in badminton and is recognized for the high physical demands it places on the lower limbs. Despite its common occurrence, little information is available on the biomechanics of lunging in the singles game. A video-based pilot study confirmed the relatively high frequency of lunging, ~15% of all movements, in competitive singles games. The biomechanics and performance characteristics of three badminton-specific lunge tasks (kick, step-in, and hop lunge) were investigated in the laboratory with nine experienced male badminton players. Ground reaction forces and kinematic data were collected and lower limb joint kinetics calculated using an inverse dynamics approach. The step-in lunge was characterized by significantly lower mean horizontal reaction force at drive-off and lower mean peak hip joint power than the kick lunge. The hop lunge resulted in significantly larger mean reaction forces during loading and drive-off phases, as well as significantly larger mean peak ankle joint moments and knee and ankle joint powers than the kick or step-in lunges. These findings indicate that, within the setting of this investigation, the step-in lunge may be beneficial for reducing the muscular demands of lunge recovery and that the hop lunge allows for higher positive power output, thereby presenting an efficient lunging method

Bilateral ground reaction forces and joint moments for lateral sidestepping and crossover stepping tasks

Racquet sports have high levels of joint injuries suggesting the joint loads during play may be excessive. Sports such as badminton employ lateral sidestepping (SS) and crossover stepping (XS) movements which so far have not been described in terms of biomechanics. This study examined bilateral ground reaction forces and three dimensional joint kinetics for both these gaits in order to determine the demands of the movements on the leading and trailing limb and predict the contribution of these movements to the occurrence of overuse injury of the lower limbs. A force platform and motion-analysis system were used to record ground reaction forces and track marker trajectories of 9 experienced male badminton players performing lateral SS, XS and forward running tasks at a controlled speed of 3 m·s-1 using their normal technique. Ground reaction force and kinetic data for the hip, knee and ankle were analyzed, averaged across the group and the biomechanical variables compared. In all cases the ground reaction forces and joint moments were less than those experienced during moderate running suggesting that in normal play SS and XS gaits do not lead to high forces that could contribute to increased injury risk. Ground reaction forces during SS and XS do not appear to contribute to the development of overuse injury. The distinct roles of the leading and trailing limb, acting as a generator of vertical force and shock absorber respectively, during the SS and XS may however contribute to the development of muscular imbalances which may ultimately contribute to the development of overuse injury. However it is still possible that faulty use of these gaits might lead to high loads and this should be the subject of future work

CHANGES IN STEP CHARACTERISTICS BETWEEN THE MAXIMUM VELOCITY AND DECELERATION PHASES OF THE 100 METRE SPRINT RUN

In a 100 m sprint race, athletes are unable to maintain their maximum velocity through the finish line. The aim of this study was to investigate the contributions of step length and step frequency to changes in velocity as athletes decelerate. Nine well-trained sprint athletes each performed between three and five maximal 100 m sprints. Velocity, step length and step frequency were measured for individual steps in the maximum velocity (30-40 m) and deceleration (70-80 m) phases. On a group level, velocity and step frequency reduced between the maximum velocity and deceleration phases (p < 0.05), whereas step length did not. Individual athlete analyses revealed that the fastest sprinters tended to maintain velocity in the deceleration phase by combining a significant reduction in step frequency with a significant increase in step length

Examining the relationship between biomechanics and GMFCS level in children with cerebral palsy

INTRODUCTION Cerebral palsy (CP) is a non-progressive lesion of the developing central nervous system that affects the development of posture and motor control [1]. The Gross Motor Function Classification System (GMFCS) is a clinical tool used to categorize children with CP based on their functional competence. It consists of five levels indicating increasing functional disability. Due to the wide range of motor outcomes in CP, some children may not fit the mould of one of the levels and the classification becomes subjective. Biomechanics provides a quantitative approach that may allow for more specific functional classification [2]. Quantifying biomechanics adaptations may support patient-specific clinical disability classification, and inform longitudinal assessment of the efficacy of therapy intervention. The aim of this study was to determine the relationship between GMFCS levels and subject-specific gait biomechanics in children with CP. It was hypothesized that joint angles and moments differ between participants with GMFCS levels 1 and 2.METHODS Gait biomechanics of 24 children with hemiplegic or diplegic CP were analyzed as part of a secondary data analysis approved by the local ethics committee. Participants were classified according to GMFCS: Level 1 (n=12) - 12.2±1.9 yrs, 1.54±0.07 m, 46.4±12.5 kg; Level 2 (n=12) - 13.6±1.6 yrs, 1.56±0.03 m, 47.8±10.5 kg. All data were collected as part of a clinical consult over the past seven years. The participants had reflective markers placed according to the Helen-Hayes set up while they walked barefoot at their preferred speed on a raised wooden walkway.Data were processed in Visual 3D (C-Motion, USA) using subject-specific lower limb models. These models created local coordinate systems for each of the segments, which were then used to calculate the kinematics (segment motions) and kinetics (forces and moments) for the hip, knee, and ankle joints. Joint angle and moment time curves for the left leg were computed using standard approaches. All data were normalized to stance phase from heel-strike to toe-off (101 data points).  Joint moments were normalized to body mass.Statistical analyses of kinematic and kinetic waveforms were conducted in MATLAB (MathWorks, USA) using statistical parametric mapping (spm1d.org). This analysis method performs statistical tests over a range of values to determine where two sets of waveforms are different from each other. Differences in gait velocity were assessed using Student’s t-test in SPSS (IBM, USA).RESULTS  Figure 1. Left hip adductor/abductor moment. The x-axis represents the stance phase from heel strike (HS) to toe off (TO) and the y-axis is the moment in Nm/kg. The blue lines represent GMFCS Level I participants (12) and red are Level II (12).  The thin lines indicate individual participants and the thick lines denote the mean of the corresponding GMFCS level.In examining the three lower extremity joints biomechanics, two significant differences in hip joint moments were identified with respect to GMFCS levels. GMFCS level 1 participants displayed significantly greater hip abductor (p=0.002, t-test, Figure 1) and hip internal rotation (p=0.047, t-test) moments between 17-26% and 18-21% of stance phase respectively. No significant differences were observed for the knee or ankle kinetics. The kinematics showed no significant differences in any of the three joints. Further, Level 1 participants walked significantly faster (p=0.009, Student’s t-test, level 1 1.1±0.1ms-1, level 2 0.9±0.2 ms-1). DISCUSSION The results of this investigation partially supported the hypothesis, demonstrating few between-group differences in gait biomechanics. The differences found in the hip abductor and internal rotation moments could be due to a number of contributing factors. They could be related to greater abductor muscle weakness in participants with lower functional competence, the differences in walking speeds found, or due to the effects of performing movements with spasticity. Spasticity is commonly seen in children with CP and is increased muscle tone that causes resistance to movement. Its influence on the resulting kinematics and kinetics of the participants in this study has not been determined.ImplicationsInterestingly, most kinematic and kinetic measures in the lower extremities are not significantly different according to GMFCS levels.  The lack of differences may be explained by the substantial variability of biomechanical measures across GMFCS groups. The variability of biomechanics outcomes between participants supports the view that GMFCS classification is likely not sensitive to child-specific function.Future Directions In order to address this shortcoming, further research will be conducted to determine the relationship between biomechanical outcomes and alternative clinical measures of functional capacity (e.g., spasticity and fatigue). Research questions to address in future research include: What is the association of spasticity and gait biomechanics abnormality? Do children with CP display distinct biomechanical clusters? Non-supervised machine-learning may be used to identify associations of biomechanical and clinical data to explore the second question. Such groupings may be beneficial for use as clinical diagnostics and therapy progression monitoring.ACKNOWLEDGEMENTS The NSERC Undergraduate Student Research Award provided funding support for this project.  Funding is acknowledged from the Vi Riddell Pediatric Rehabilitation Research Program, (Alberta Children’s Hospital Foundation) and Alberta Innovates Technology Futures.REFERENCESPalisano et al. DMCN 1997; 39:214-223.Dziuba et al. Acta Bioeng Biomech 2013; Vol. 15, No. 2

Evaluation of optimal control formulations for predicting swing-through axillary crutch-assisted gait

Crutches are widely used to assist gait in individuals with lower limb impairment. Walking with crutches alters both upper and lower body loading, potentially leading to discomfort. As such, it is important to study how crutch walking affects upper and lower extremity movement patterns. Computer modelling and simulation can provide answers that motion analysis cannot. For this reason, the availability of an algorithm that allows the prediction of different crutch walking patterns could be useful in order to study the impact of changing conditions on crutch walking, and could overcome some limitations of experimental studies, such as difficulty in recruiting subjects or limitation in the number of tests that can be performedPeer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version

PROCESS VALIDATION IN CALCULATING MEDIAN PROXIMITY IN TIBIOFEMORAL CARTILAGE DEFORMATION UNDER FULL BODY LOADING

INTRODUCTION Knee osteoarthritis (OA) is characterized by progressive and irreversible degradation of tibiofemoral (TF) cartilages. Anterior cruciate ligament (ACL) rupture is a known risk factor for post-traumatic OA (PTOA) [1]. However, there are currently no in-vivo tests to diagnose pre-radiographic PTOA. Following injury, the cartilage macromolecular matrix weakens, cartilage swells and consequently cartilage softness increases [2]. This research investigates the in-vivo effects of ACL injury on cartilage deformation magnitude and rate under full body loading. The objective of this project was to determine the consequences of cartilage model mesh types and incremental mesh simplifications on the accuracy of resultant TF cartilage proximities. METHODS The affected knee of a 37 year old male PTOA subject (ACL deficient for 6 years) was imaged using Magnetic Resonance Imaging (FIESTA sequence; 3T GE Discovery 750). 3D TF bone and cartilage models were generated in Amira (VSG, Germany). The subject performed a 10 minute standing task in the Dual Fluoroscopic (DF) laboratory. DF images (32LP/mm) were collected at 6Hz. Bone alignments were reconstructed from DF images using AutoScoper (Brown University, USA) and cartilage models were co-registered. TF cartilage surface proximity was determined as the surface normal distance from each triangular mesh face onto the opposing cartilage. (Matlab, v2014b, The MathWorks, USA). The effects on surface proximities of three types of triangular cartilage surface meshes, generated in Amira, were analysed: 1) Basic Simplification - reducing face numbers with variable mesh size; 2) Remeshed Surface – isotropic mesh; 3) Iteratively Smoothed Remeshed Surface. Face numbers were reduced at 10% increments from the original surface for each surface type. RESULTS Median proximity errors for the Remeshed Surface were consistently smaller than the other mesh types across all four cartilage surface compartments. The medial tibial plateau displayed a rapid increase in error (Figure 1) indicating a high sensitivity to model simplification. This may have been due to its more complex surface geometry. The maximum acceptable error was chosen to match the minimum detectable displacement of 0.05mm for this DF system [3]. DISCUSSION AND CONCLUSIONS The findings of this investigation identified differences in the error of cartilage surface proximities under loading due to the use of different mesh types and simplifications. The smoothing technique used by Amira did not consistently converge to a surface and the variable triangle size in Basic Simplification affected the computation of proximity, resulting in unpredictable error spikes in cartilage surface proximity calculations. The results suggest that surface modeling parameters are surface geometry specific. The limiting case of the medial tibial plateau showed the optimal simplification was 0.594mm triangle mesh side length (40% of the original faces). These results inform ongoing work toward an in-vivo pre-radiographic diagnostic of PTOA

Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation

Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To solve the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans is proposed in this work. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. To this aim, an improved version of Mask-RCNN architecture has been adapted to localize the distortion location and recover the original image pixels. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated based on the Osteoarthritis Initiative dataset. Using this self-supervised pretraining method improved the Dice score by 20% compared to training from scratch. The proposed self-supervised learning is simple, effective, and suitable for different ranges of medical image analysis tasks including anomaly detection, segmentation, and classification