Sami Äyrämö

Stiff Landings Are Associated With Increased ACL Injury Risk in Young Female Basketball and Floorball Players

Background: Few prospective studies have investigated the biomechanical risk factors of anterior cruciate ligament (ACL) injury. Purpose: To investigate the relationship between biomechanical characteristics of vertical drop jump (VDJ) performance and the risk of ACL injury in young female basketball and floorball players. Study Design: Cohort study; Level of evidence, 3. Methods: At baseline, a total of 171 female basketball and floorball players (age range, 12-21 years) participated in a VDJ test using 3-dimensional motion analysis. The following biomechanical variables were analyzed: (1) knee valgus angle at initial contact (IC), (2) peak knee abduction moment, (3) knee flexion angle at IC, (4) peak knee flexion angle, (5) peak vertical ground-reaction force (vGRF), and (6) medial knee displacement. All new ACL injuries, as well as match and training exposure, were then recorded for 1 to 3 years. Cox regression models were used to calculate hazard ratios (HRs) and 95% CIs. Results: Fifteen new ACL injuries occurred during the study period (0.2 injuries/1000 player-hours). Of the 6 factors considered, lower peak knee flexion angle (HR for each 10° increase in knee flexion angle, 0.55; 95% CI, 0.34-0.88) and higher peak vGRF (HR for each 100-N increase in vGRF, 1.26; 95% CI, 1.09-1.45) were the only factors associated with increased risk of ACL injury. A receiver operating characteristic (ROC) curve analysis showed an area under the curve of 0.6 for peak knee flexion and 0.7 for vGRF, indicating a failed-to-fair combined sensitivity and specificity of the test. Conclusions: Stiff landings, with less knee flexion and greater vGRF, in a VDJ test were associated with increased risk of ACL injury among young female basketball and floorball players. However, although 2 factors (decreased peak knee flexion and increased vGRF) had significant associations with ACL injury risk, the ROC curve analyses revealed that these variables cannot be used for screening of athletes.

Study on the effects of pseudorandom generation quality on the performance of differential evolution

Experiences in the field of Monte Carlo methods indicate that the quality of a random number generator is exceedingly significant for obtaining good results. This result has not been demonstrated in the field of evolutionary optimization, and many practitioners of the field assume that the choice of the generator is superfluous and fail to document this aspect of their algorithm. In this paper, we demonstrate empirically that the requirement of high quality generator does not hold in the case of Differential Evolution.

Clustering aided approach for decision making in computationally expensive multiobjective optimization

Typically, industrial optimization problems need to be solved in an efficient, multiobjective and global manner, because they are often computationally expensive (as function values are typically based on simulations), they may contain multiple conflicting objectives, and they may have several local optima. Solving such problems may be challenging and time consuming when the aim is to find the most preferred Pareto optimal solution. In this study, we propose a method where we use an advanced clustering technique to reveal essential characteristics of the approximation of the Pareto optimal set, which has been generated beforehand. Thus, the decision maker (DM) is involved only after the most time consuming computation is finished. After the initiation phase, a moderate number of cluster prototypes projected to the Pareto optimal set is presented to the DM to be studied. This allows him/her to rapidly gain an overall understanding of the main characteristics of the problem without placing too much cognitive load on the DM. Furthermore, we also suggest some ways of applying our approach to different types of problems and demonstrate it with an example related to internal combustion engine design.

Robust refinement of initial prototypes for partitioning-based clustering algorithms

Non-uniqueness of solutions and sensitivity to erroneous data are common problems to large-scale data clustering tasks. In order to avoid poor quality of solutions with partitioning-based clustering methods, robust estimates (that are highly insensitive to erroneous data values) are needed and initial cluster prototypes should be determined properly. In this paper, a robust density estimation initialization method that exploits the spatial median estimate to the prototype update is presented. Besides being insensitive to noise and outliers, the new method is also computationally comparable with other traditional methods. The methods are compared by numerical experiments on a set of synthetic and real-world data sets. Conclusions and discussion on the results are given.

Online Mass Flow Prediction in CFB Boilers

Fuel feeding and inhomogeneity of fuel typically cause process fluctuations in the circulating fluidized bed (CFB) process. If control systems fail to compensate for the fluctuations, the whole plant will suffer from fluctuations that are reinforced by the closed-loop controls. This phenomenon causes a reduction of efficiency and lifetime of process components. Therefore, domain experts are interested in developing tools and techniques for getting better understanding of underlying processes and their mutual dependencies in CFB boilers. In this paper we consider an application of data mining technology to the analysis of time series data from a pilot CFB reactor. Namely, we present a rather simple and intuitive approach for online mass flow prediction in CFB boilers. This approach is based on learning and switching regression models. Additionally, noise canceling, and windowing mechanisms are used for improving the robustness of online prediction. We validate our approach with a set of simulation experiments with real data collected from the pilot CFB boiler.

Monitoring Training Adaptation With a Submaximal Running Test Under Field Conditions

UNLABELLED Regular monitoring of adaptation to training is important for optimizing training load and recovery, which is the main factor in successful training. PURPOSE To investigate the usefulness of a novel submaximal running test (SRT) in field conditions in predicting and tracking changes of endurance performance. METHODS Thirty-five endurance-trained men and women (age 20-55 y) completed the 18-wk endurance-training program. A maximal incremental running test was performed at weeks 0, 9, and 18 for determination of maximal oxygen consumption (VO2max) and running speed (RS) at exhaustion (RSpeak) and lactate thresholds (LTs). In addition, the subjects performed weekly a 3-stage SRT including a postexercise heart-rate-recovery (HRR) measurement. The subjects were retrospectively grouped into 4 clusters according to changes in SRT results. RESULTS Large correlations (r = .60-.89) were observed between RS during all stages of SRT and all endurance-performance variables (VO2max, RSpeak, RS at LT2, and RS at LT1). HRR correlated only with VO2max (r = .46). Large relationships were also found between changes in RS during 80% and 90% HRmax stages of SRT and a change of RSpeak (r = .57, r = .79). In addition, the cluster analysis revealed the different trends in RS during 80% and 90% stages during the training between the clusters, which showed different improvements in VO2max and RSpeak. CONCLUSIONS The current SRT showed great potential as a practical tool for regular monitoring of individual adaptation to endurance training without time-consuming and expensive laboratory tests.

Knee Extensor and Flexor Dominant Gait Patterns Increase the Knee Frontal Plane Moment during Walking

High gait‐induced knee frontal plane moment is linked with the development of knee osteoarthritis. Gait patterns across the normal population exhibit large inter‐individual variabilities especially at the knee sagittal plane moment profile during loading response and terminal stance phase. However, the effects of different gait patterns on this moment remain unknown. Therefore, we examined whether different gait patterns are associated with atypically high knee frontal plane moments. Profiles of knee joint moments divided a sample of 24 subjects into three subgroups (11, 7, 6) through cluster analysis. Kinetics, kinematics, and spatio‐temporal parameters were compared among clusters. Subjects who showed a typical sagittal plane moment pattern (n = 11) had 43% lower first peak of knee frontal plane moment compared to the cluster, which showed the dominance of the knee extensor moment during stance phase (n = 7, p < 0.01). In addition, a typical gait pattern cluster had 44% lower second peak knee frontal plane moment than the cluster, which showed the dominance of the knee flexor moment during the terminal stance phase (n = 6, p < 0.05). These findings indicate that different knee strategies driving gait considerably impact knee loading, suggesting that knee extensor and flexor dominant gait patterns demonstrate atypically high knee frontal plane moments. People in these subgroups may, therefore, be at higher risk of developing knee osteoarthritis.

Visualizing time series state changes with prototype based clustering

Modern process and condition monitoring systems produce a huge amount of data which is hard to analyze manually. Previous analyzing techniques disregard time information and concentrate only for the indentification of normal and abnormal operational states. We present a new method for visualizing operational states and overall order of the transitions between them. This method is implemented to a visualization tool which helps the user to see the overall development of operational states allowing to find causes for abnormal behaviour. In the end visualization tool is tested in practice with real time series data collected from gear unit.

Oxygen Uptake, Acid-Base Balance and Anaerobic Energy System Contribution in Maximal 300 â 400 M Running in Child, Adolescent and Adult Athletes

1.1 Objective: The purpose of this study was to investigate oxygen uptake, acid-base balance and energy system contributions during and after short maximal running in adult (n = 8), adolescent (n = 8) and child (n = 8) male athletes. 1.2 Methods: The tests included a maximal time trial of 400 m, 350 m and 300 m for different age groups respectively and a VO2max running test on a 200 m indoor track. Capillary blood samples to analyse pH and lactate were taken before and after the time trial. Energy system contributions were estimated using the accumulated oxygen deficit (AOD) method. 1.3 Results: Peak oxygen uptake (VO2peak) during the time trial was the lowest in children (53.1 ± 4.6 ml/kg/min) compared to adolescents (59.9 ± 3.7 ml/kg/min, P < 0.01) and to adults (60.7 ± 2.4 ml/kg/min, P < 0.01). After the time trial minimal blood pH was the lowest in adults (6.97 ± 0.06) compared to adolescents (7.14 ± 0.07, P < 0.05) and children (7.18 ± 0.03, P < 0.001) and maximal blood lactate was the greatest in adults (17.4 ± 1.8 mmol/l) compared to adolescents (13.3 ± 3.7 mmol/l, P < 0.05) and children (10.2 ± 1.1mmol/l, P < 0.01). The estimated anaerobic energy percentage during the time trial was the greatest in adults (53 ± 5 %) compared to adolescents (44 ± 7 %, P < 0.05) and children (45 ± 5 %, P < 0.05). 1.4 Conclusion: The present data demonstrated that adult and adolescent male athletes achieved greater oxygen uptake than child athletes during maximal 52–54 s running and adult athletes used mainly anaerobic energy and achieved greater acidosis than adolescents and children, who used mainly aerobic energy.