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.

Predicting overweight and obesity in later life from childhood data: A review of predictive modeling approaches

Background: Overweight and obesity are an increasing phenomenon worldwide. Predicting future overweight or obesity early in the childhood reliably could enable a successful intervention by experts. While a lot of research has been done using explanatory modeling methods, capability of machine learning, and predictive modeling, in particular, remain mainly unexplored. In predictive modeling models are validated with previously unseen examples, giving a more accurate estimate of their performance and generalization ability in real-life scenarios. Objective: To find and review existing overweight or obesity research from the perspective of employing childhood data and predictive modeling metho…

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Previous studies have suggested that runners can be subgrouped based on homogeneous gait patterns, however, no previous study has assessed the presence of such subgroups in a population of individuals across a wide variety of injuries. Therefore, the purpose of this study was to assess whether distinct subgroups with homogeneous running patterns can be identified among a large group of injured and healthy runners and whether identified subgroups are associated with specific injury location. Three‐dimensional kinematic data from 291 injured and healthy runners, representing both sexes and a wide range of ages (10‐66 years) was clustered using hierarchical cluster analysis. Cluster analysis r…

Mining road traffic accidents

H&E Multi-Laboratory Staining Variance Exploration with Machine Learning

In diagnostic histopathology, hematoxylin and eosin (H&E) staining is a critical process that highlights salient histological features. Staining results vary between laboratories regardless of the histopathological task, although the method does not change. This variance can impair the accuracy of algorithms and histopathologists’ time-to-insight. Investigating this variance can help calibrate stain normalization tasks to reverse this negative potential. With machine learning, this study evaluated the staining variance between different laboratories on three tissue types. We received H&E-stained slides from 66 different laboratories. Each slide contained kidney, skin, and colon tissue sampl…

Game learning analytics for understanding reading skills in transparent writing system

Serious games are designed to improve learning instead of providing only entertainment. Serious games analytics can be used for understanding and enhancing the quality of learning with serious games. One challenge in developing computerized support for learning is that learning of skills varies between players. Appropriate algorithms are needed for analyzing the performance of individual players. This paper presents a novel clustering-based profiling method for analyzing serious games learners. GraphoLearn, a game for training connections between speech sounds and letters, serves as the game-based learning environment. The proposed clustering method was designed to group the learners into p…

Neuromuscular fatigue after short-term maximal run in child, youth, and adult athletes

Johdanto ja tutkimuksen tavoite. Aikaisempien tutkimusten perusteella on havaittu että esipuberteetti ikäiset lapset väsyvät vähemmän ja palautuvat nopeammin kuin aikuiset kovatehoisten urheilusuoritusten yhteydessä. Nuoren urheilijan kypsymiseen liittyvät muutokset perifeerisissä ja hermostollisissa väsymysmekanismeissa ovat kuitenkin vielä selvittämättä. Tämän tutkimuksen tavoitteena on tuottaa uutta tietämystä iän ja kypsymisen vaikutuksista perifeeristen ja hermostollisten mekanismien rooliin 50 s maksimaalisessa juoksusuorituksessa. Menetelmät: Tutkimukseen osallistui 24 miespuolista koehenkilöä jotka jaettiin kolmeen ikäryhmään: Lapset (N = 8; 11.9 ± 1.4 v), Nuoret (N = 8; 14.9 ± 1.1 …

Use cases for operational decision support system

Cross-cultural adaptation and validation of the Kerlan-Jobe Orthopaedic Clinic shoulder and elbow score in Finnish-speaking overhead athletes

Background The Kerlan-Jobe Orthopaedic Clinic Shoulder and Elbow score (KJOC) is developed to evaluate the shoulder and elbow function in overhead athletes. To date, the score has not been adapted into Finnish language. The aim of this study was to perform a cross-cultural adaptation of the Kerlan-Jobe Orthopaedic Clinic Shoulder and Elbow score (KJOC) into Finnish language and evaluate its validity, reliability, and responsiveness in overhead athletes. Methods Forward–backward translation method was followed in the cross-cultural adaptation process. Subsequently, 114 overhead athletes (52 males, 62 females, mean age 18.1 ± 2.8 years) completed the Finnish version of KJOC score, Disabilitie…

5 Frontal plane femoral adduction during single-leg landing and low back pain in young athletes: a prospective profits cohort study

Introduction Prospective studies investigating risk factors for low back pain (LBP) in young athletes are limited. The aim of this prospective cohort study was to investigate the association between LBP and selected biomechanical factors and postural stability during dynamic movement tasks in young athletes. Materials and methods 396 young floorball and basketball players (mean age 15.8±1.9) were included and followed prospectively for 1–3 years (2011–2014). In the beginning of every study year the players were tested. The physical tests included single-leg squat (SLS), single-leg vertical drop jump (SLVDJ), vertical drop jump (VDJ) and Star Reach Excursion Balance Test (SEBT). Individual e…

Comparison of cluster validation indices with missing data

Clustering is an unsupervised machine learning technique, which aims to divide a given set of data into subsets. The number of hidden groups in cluster analysis is not always obvious and, for this purpose, various cluster validation indices have been suggested. Recently some studies reviewing validation indices have been provided, but any experiments against missing data are not yet available. In this paper, performance of ten well-known indices on ten synthetic data sets with various ratios of missing values is measured using squared euclidean and city block distances based clustering. The original indices are modified for a city block distance in a novel way. Experiments illustrate the di…

Maturation-Related Differences in Neuromuscular Fatigue After a Short-Term Maximal Run

AbstractPurpose. This study investigated maturation-related differences in neuromuscular fatigue after a short-term maximal run. Methods. Eight male children, eight adolescents, and eight adults performed a maximal ca. 50-s run (300/350/400 m, respectively). Mechanisms of neuromuscular fatigue were assessed through isometric plantar flexor tests, electrical stimulation of the posterior tibial nerve, soleus electromyography, and blood tests. Results. All the groups showed a decrease in the running speed (children: -12.2 ± 6.5%; adolescents: -9.8 ± 5.1%; adults: -12.2 ± 3.1%), but only adults revealed a decline in the maximal isometric plantar flexor torque (-16.1 ± 13.0%). On the other hand,…

Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks

New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date. One proposed solution to the problem involves isolation of cancer cells from the patients' cancer tissue, after which they are exposed to potential drugs alone or in combinations to find the most optimal medication. To achieve this goal, methods that can efficiently quantify and analyze changes in tested cell are needed. Our study aimed to detect and segment cells and structures from canc…

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. Subjec…

Data Analytics in Healthcare: A Tertiary Study

AbstractThe field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analy…

An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis

Efficient and scalable early diagnostic methods for knee osteoarthritis are desired due to the disease’s prevalence. The current automatic methods for detecting osteoarthritis using plain radiographs struggle to identify the subjects with early-stage disease. Tibial spiking has been hypothesized as a feature of early knee osteoarthritis. Previous research has demonstrated an association between knee osteoarthritis and tibial spiking, but the connection to the early-stage disease has not been investigated. We study tibial spiking as a feature of early knee osteoarthritis. Additionally, we develop a deep learning based model for detecting tibial spiking from plain radiographs. We collected an…

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.

Tekoälyn perusteita ja sovelluksia

Omadata terveydenhuollon tietointensiivisessä rakenteessa

Reaaliaikajärjestelmän mallintaminen UML-kuvausmenetelmän avulla

Collecting and Using Students’ Digital Well-Being Data in Multidisciplinary Teaching

This article examines how students (N=198; aged 13 to 17) experienced the new methods for sensor-based learning in multidisciplinary teaching in lower and upper secondary education that combine the use of new sensor technology and learning from self-produced well-being data. The aim was to explore how students perceived new methods from the point of view of their learning and did the teaching methods provide new information that could promote their own well-being. We also aimed to find out how to collect digital well-being data from a large number of students and how the collected big data set can be utilized to predict school success from the students’ well-being data by using machine lear…

Monitoring Training Adaptation With a Submaximal Running Test in Field Conditions

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 …

Introduction to partitioning-based clustering methods with a robust example

Prediction of active peak force using a multilayer perceptron

Both kinematic parameters and ground reaction forces (GRFs) are necessary for understanding the biomechanics of running. Kinematic information of a runner is typically measured by a motion capture system whereas GRF during the support phase of running is measured by force platforms. To analyze both kinematics and kinetics of a runner over several subsequent contacts, an instrumented treadmill or alternatively several force platforms installed over a regulated space are available options, but they are highly immovable, expensive, and sometimes even impractical options. Naturally, it would be highly useful to predict GRFs using a motion capture system only and this way reduce costs and comple…

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 …

Tekoäly ja terveydenhuolto Suomessa

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 syntheti…

Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion

In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer …

Fysiikkaa liikkuen : 7-luokkalaisten oppilaiden ja opettajien kokemuksia kehollisesta opetuksesta fysiikassa

Nuorten liikkumattomuus ja poikien motivaation hiipuminen luonnontieteitä kohtaan ovat nousseet esille viimeaikaisissa kansallisissa kyselyissä. Uusi perusopetuksen opetussuunnitelma ja Liikkuva koulu-ohjelma pyrkivätkin jalkauttamaan lisää toiminnallisia työtapoja tavallisille oppitunneille, jotta opetuksen kokemuksellisuus ja oppilaiden koulupäivän aikainen fyysinen aktiivisuus lisääntyisi. Liikunnan vaikutusta aivoihin ja oppimiseen on tutkittu laajasti viimeisen kahden vuosikymmenen aikana, mutta liikunnallistavien opetusmenetelmien vaikutuksesta oppilaiden oppimiskokemukseen on vielä vähän tutkimusta suomalaisen tiedeopetuksen kentällä. Tämän tutkimuksen tarkoituksena on kehittää uusia…

Balance Perturbations as a Measurement Tool for Trunk Impairment in Cross-Country Sit Skiing

In cross-country sit-skiing, the trunk plays a crucial role in propulsion generation and balance maintenance. Trunk stability is evaluated by automatic responses to unpredictable perturbations; however, electromyography is challenging. The aim of this study was to identify a measure to group sit-skiers according to their ability to control the trunk. Seated in their competitive sit-ski, 10 male and 5 female Paralympic sit-skiers received 6 forward and 6 backward unpredictable perturbations in random order. k-means clustered trunk position at rest, delay to invert the trunk motion, and trunk range of motion significantly into 2 groups. In conclusion, unpredictable perturbations might quantif…

Applying Semiautomatic Generation of Conceptual Models to Decision Support Systems Domain

This paper presents a decision support system specification in the form of business use cases and a stereotyped conceptual model based on the specification. The use cases are based on generic user requirements and address cognitive biases. The specification can be used to set fixed and common terms among the project participants. Semiautomatic generation of the conceptual model is demonstrated with mixed results. While there are some shortcomings in the parsing and the result is dependent on the phrasing conventions used in the use cases, the conceptual model highlights the most essential entities in the domain and provides a base for further development phases. peerReviewed

Knowledge mining using robust clustering

FM Sami Äyrämö tutki väitöstyössään suurten digitaalisten tietomassojen tehokasta hyödyntämistä ja siihen sovellettavia laskennallisesti älykkäitä niin kutsuttuja tiedonlouhintamenetelmiä (data mining). Aihe on ajankohtainen, sillä informaatiojärjestelmien nopea kehittyminen ja yleistyminen johtavat entistä useammin ”datatulvaan": digitaalisessa muodossa kerätään tietoa niin paljon, että oleellinen informaatio voi hukkua epäoleellisen ja moninkertaisen tiedon sekaan.Väitöstyönsä päätuloksena Äyrämö esittelee luotettavan, laskennallisesti tehokkaan ja käyttäjälle yksinkertaisen klusterointimenetelmän, joka ei ota kantaa sovelluskohteeseen ja on siten hyvin yleiskäyttöinen. Menetelmän pohjana…

Performance in dynamic movement tasks and occurrence of low back pain in youth floorball and basketball players

Abstract Background Prospective studies investigating risk factors for low back pain (LBP) in youth athletes are limited. The aim of this prospective study was to investigate the association between hip-pelvic kinematics and vertical ground reaction force (vGRF) during landing tasks and LBP in youth floorball and basketball players. Methods Three-hundred-and-eighty-three Finnish youth female and male floorball and basketball players (mean age 15.7 ± 1.8) participated and were followed up on for 3 years. At the beginning of every study year the players were tested with a single-leg vertical drop jump (SLVDJ) and a vertical drop jump (VDJ). Hip-pelvic kinematics, measured as femur-pelvic angl…

The standing knee lift test is not a useful screening tool for time loss from low back pain in youth basketball and floorball players

Abstract Objectives The aim of this study was to investigate the association between pelvic kinematics during the standing knee lift (SKL) test and low back pain (LBP) in youth floorball and basketball players. Design A prospective cohort study. Setting Finnish elite youth floorball and basketball players. Participants Finnish elite youth female and male floorball and basketball players (n = 258, mean age 15.7 ± 1.8). Main outcome measures LBP resulting in time loss from practice and games was recorded over a 12-month period and verified by a study physician. Associations between LBP and sagittal plane pelvic tilt and frontal plane pelvic obliquity during the SKL test as measured at baselin…

sj-pdf-1-ajs-10.1177_03635465221112095 – Supplemental material for Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes

Supplemental material, sj-pdf-1-ajs-10.1177_03635465221112095 for Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes by Susanne Jauhiainen, Jukka-Pekka Kauppi, Tron Krosshaug, Roald Bahr, Julia Bartsch and Sami Äyrämö in The American Journal of Sports Medicine

DeepFake knee osteoarthritis X-rays from generative adversarial neural networks deceive medical experts and offer augmentation potential to automatic classification

Recent developments in deep learning have impacted medical science. However, new privacy issues and regulatory frameworks have hindered medical data sharing and collection. Deep learning is a very data-intensive process for which such regulatory limitations limit the potential for new breakthroughs and collaborations. However, generating medically accurate synthetic data can alleviate privacy issues and potentially augment deep learning pipelines. This study presents generative adversarial neural networks capable of generating realistic images of knee joint X-rays with varying osteoarthritis severity. We offer 320,000 synthetic (DeepFake) X-ray images from training with 5,556 real images. W…

Talent identification in soccer using a one-class support vector machine

Abstract Identifying potential future elite athletes is important in many sporting events. The successful identification of potential future elite athletes at an early age would help to provide high-quality coaching and training environments in which to optimize their development. However, a large variety of different skills and qualities are needed to succeed in elite sports, making talent identification generally a complex and multifaceted problem. Due to the rarity of elite athletes, datasets are inherently imbalanced, making classical statistical inference difficult. Therefore, we approach talent identification as an anomaly detection problem. We trained a nonlinear one-class support ve…

Predicting hospital associated disability from imbalanced data using supervised learning.

Hospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. This is accomplished by studying the outcome of hospitalization of elderly patients as a supervised learning task. A rich set of features characterizing the medical and social situation of elderly patients is leveraged and using confusion matrices, association rule mining, and two different classes of supervised learning algo…

Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning

Objectives: To assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier). Methods: Data from a 2-year observational study (2013‒2015, 12.4±1.3 years, n=633, 50% girls), with 48 baseline characteristics (questionnaires (demographics, physical, psychological, social and lifestyle factors), objective measurements (anthropometrics, fitness characteristics, physical activity, body composition and academic scores)) were used to predict: (Task 1) unfavourable future 20MSRT status (identification of individuals in the lowest 20MSRT tertile after 2 years), and (Task…

On Combining Explainable Artificial Intelligence and Interactive Multiobjective Optimization in Data-Driven Decision Support

Validation of Knee KL-classifying Deep Neural Network with Finnish Patient Data

Osteoarthritis (OA) is the most common form of joint disease in the world. The diagnosis of OA is currently made by human experts and suffers from subjectivity, but recently new promising detection algorithms have been developed. We validated the current state-of-the-art KL-classifying neural network model for knee OA using knee X-rays taken from postmenopausal women suffering from knee pain attributable to OA. The performance of the model on the clinical data was considerably lower compared to the previous results on population-based test data. This suggests that the performance of the current grading methods is not yet adequate to be applied in clinical settings. The present results also …

Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes

Background: Injury risk prediction is an emerging field in which more research is needed to recognize the best practices for accurate injury risk assessment. Important issues related to predictive machine learning need to be considered, for example, to avoid overinterpreting the observed prediction performance. Purpose: To carefully investigate the predictive potential of multiple predictive machine learning methods on a large set of risk factor data for anterior cruciate ligament (ACL) injury; the proposed approach takes into account the effect of chance and random variations in prediction performance. Study Design: Case-control study; Level of evidence, 3. Methods: The authors used 3-dime…

CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS

Abstract. Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-for…

Change of Direction Biomechanics in a 180-Degree Pivot Turn and the Risk for Noncontact Knee Injuries in Youth Basketball and Floorball Players.

Background: Studies investigating biomechanical risk factors for knee injuries in sport-specific tasks are needed. Purpose: To investigate the association between change of direction (COD) biomechanics in a 180-degree pivot turn and knee injury risk among youth team sport players. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 258 female and male basketball and floorball players (age range, 12-21 years) participated in the baseline COD test and follow-up. Complete data were obtained from 489 player-legs. Injuries, practice, and game exposure were registered for 12 months. The COD test consisted of a quick ball pass before and after a high-speed 180-degree pivot turn o…

Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifie…

Information Extraction from Binary Skill Assessment Data with Machine Learning

Strength training exercises are essential for rehabilitation, improving our health as well as in sports. For optimal and safe training, educators and trainers in the industry should comprehend exercise form or technique. Currently, there is a lack of tools measuring in-depth skills of strength training experts. In this study, we investigate how data mining methods can be used to identify novel and useful skill patterns from a binary multiple choice questionnaire test designed to measure the knowledge level of strength training experts. A skill test assessing exercise technique expertise and comprehension was answered by 507 fitness professionals with varying backgrounds. A triangulated appr…

Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity

Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…

Altered hip control during a standing knee-lift test is associated with increased risk of knee injuries

Few prospective studies have investigated hip and pelvic control as a risk factor for lower extremity (LE) injuries. The purpose of this study was to investigate whether deficits in hip and lumbopelvic control during standing knee lift test are associated with increased risk of acute knee and LE injuries in youth team sports. At baseline, 258 basketball and floorball players (aged 12‒21 y.) participated in a standing knee lift test using 3‐dimensional motion analysis. Two trials per leg were recorded from each participant. Peak sagittal plane pelvic tilt and frontal plane pelvic drop/hike were measured. Both continuous and categorical variables were analysed. New non‐contact LE injuries, an…