Correlations between Achilles tendon material and structural properties and quantitative magnetic resonance imagining in different athletic populations

Achilles tendon stiffness (kAT) and Young's modulus (yAT) are important determinants of tendon function. However, their evaluation requires sophisticated equipment and time-consuming procedures. The goal of this study was twofold: to compare kAT and yAT between populations using the classical approach proposed in the literature (a combination of ultrasound and force data) and the MRI technique to understand the MRI's capability in determining differences in kAT and yAT. Furthermore, we investigated potential correlations between short and long T2* relaxation time, kAT and yAT to determine whether T2* relaxation time may be associated with material or structural properties. Twelve endurance and power athlete, and twelve healthy controls were recruited. AT T2* short and long components were measured using standard gradient echo MRI at rest, while kAT and yAT were evaluated using the classical method (combination of ultrasound and dynamometric measurements). Power athletes had the highest kAT (3064 ± 260, 2714 ± 260 and 2238 ± 189 N/mm for power athletes, endurance athletes and healthy control, respectively) and yAT (2.39 ± 0.28, 1.64 ± 0.22 and 1.97 ± 0.32 GPa for power athletes, endurance athletes and healthy control, respectively) and the lowest T2* short component (0.58 ± 0.07, 0.77 ± 0.06 and 0.74 ± 0.08 ms for power athletes, endurance athletes and healthy control, respectively). Endurance athletes had the highest T2* long component value. No correlations were reported between T2* long component, kAT or yAT in the investigated populations, whereas the T2* short component was negatively correlated with yAT. These results suggest that T2* short component could be used to investigate the differences in AT material properties in different populations

FOOTFALL PATTERN IS NOT ASSOCIATED WITH ALTERED ACHILLES TENDON T2* RELAXATION TIME OF RECREATIONAL DISTANCE RUNNERS

Achilles tendon (AT) tendinopathy was reported to be the pathology with one of the highest incidences of all running-related injuries. Non-rearfoot FP has been proposed to be the risk factor for AT overload. T2* relaxation time of Achilles tendon is considered to be a marker of AT tendinopathy. Therefore, the purpose of this study was to compare T2* relaxation time of Achilles tendon (AT) between recreational runner population with rearfoot (RF) and non-rearfoot (NR) footfall patterns (FP). Twenty-two middle age recreational runners (11 rearfoot and 11 non-rearfoot), matched according to running distance, participated in this study. Resting T2* relaxation time was determined using a 1.5 Tesla magnetic resonance imaging technique. Lower extremity kinematics and kinetics were recorded during over-ground running at a self-selected speed. No significant differences were found between T2* relaxation time of the insertion portion of the AT between RF and NR runners. Structural properties of the most injured part of the AT is not affected by running FP in healthy middle-aged runners

Prediction model of alcohol intoxication from facial temperature dynamics based on K-means clustering driven by evolutionary computing

Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster's distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.Web of Science118art. no. 99

Running and Physical Activity in an Air-Polluted Environment: The Biomechanical and Musculoskeletal Protocol for a Prospective Cohort Study 4HAIE (Healthy Aging in Industrial Environment—Program 4)

Far too little attention has been paid to health effects of air pollution and physical (in)activity on musculoskeletal health. The purpose of the Healthy aging in industrial environment study (4HAIE) is to investigate the potential impact of physical activity in highly polluted air on musculoskeletal health. A total of 1500 active runners and inactive controls aged 18–65 will be recruited. The sample will be recruited using quota sampling based on location (the most air-polluted region in EU and a control region), age, sex, and activity status. Participants will complete online questionnaires and undergo a two-day baseline laboratory assessment, including biomechanical, physiological, psychological testing, and magnetic resonance imaging. Throughout one-year, physical activity data will be collected through Fitbit monitors, along with data regarding the incidence of injuries, air pollution, psychological factors, and behavior collected through a custom developed mobile application. Herein, we introduce a biomechanical and musculoskeletal protocol to investigate musculoskeletal and neuro-mechanical health in this 4HAIE cohort, including a design for controlling for physiological and psychological injury factors. In the current ongoing project, we hypothesize that there will be interactions of environmental, biomechanical, physiological, and psychosocial variables and that these interactions will cause musculoskeletal diseases/protection

BIOMECHANICS IN THE 4HAIE STUDY: AIR POLLUTION AND MUSCULOSKELETAL HEALTH - AN UPDATE

The overall purpose of the 4HAIE study was to assess the influence of the interaction between air pollution and biomechanical, physiological and psychosocial factors on the incidence of injuries, health and well-being. A total of 1,500 active runners and inactive controls aged 18-65 will be recruited. Herein, we describe the biomechanical study design with data examples to investigate musculoskeletal and neuro-mechanics health in different air quality regions

A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring

The study deals with detection of the occupation of Intelligent Building (IB) using data obtained from indirect methods with Big Data Analysis within IoT. In the area of daily living activity monitoring, one of the most challenging tasks is occupancy prediction, giving us information about people's mobility in the building. This task can be done via monitoring of CO2 as a reliable method, which has the ambition to predict the presence of the people in specific areas. In this paper, we propose a novel hybrid system, which is based on the Support Vector Machine (SVM) prediction of the CO2 waveform with the use of sensors that measure indoor/outdoor temperature and relative humidity. For each such prediction, we also record the gold standard CO2 signal to objectively compare and evaluate the quality of the proposed system. Unfortunately, this prediction is often linked with a presence of predicted signal activities in the form of glitches, often having an oscillating character, which inaccurately approximates the real CO2 signals. Thus, the difference between the gold standard and the prediction results from SVM is increasing. Therefore, we employed as the second part of the proposed system a smoothing procedure based on Wavelet transformation, which has ambitions to reduce inaccuracies in predicted signal via smoothing and increase the accuracy of the whole prediction system. The whole system is completed with an optimization procedure based on the Artificial Bee Colony (ABC) algorithm, which finally classifies the wavelet's response to recommend the most suitable wavelet settings to be used for data smoothing

Running and Physical Activity in an Air-Polluted Environment: The Biomechanical and Musculoskeletal Protocol for a Prospective Cohort Study 4HAIE (Healthy Aging in Industrial Environment—Program 4)

Far too little attention has been paid to health effects of air pollution and physical (in)activity on musculoskeletal health. The purpose of the Healthy aging in industrial environment study (4HAIE) is to investigate the potential impact of physical activity in highly polluted air on musculoskeletal health. A total of 1500 active runners and inactive controls aged 18–65 will be recruited. The sample will be recruited using quota sampling based on location (the most air-polluted region in EU and a control region), age, sex, and activity status. Participants will complete online questionnaires and undergo a two-day baseline laboratory assessment, including biomechanical, physiological, psychological testing, and magnetic resonance imaging. Throughout one-year, physical activity data will be collected through Fitbit monitors, along with data regarding the incidence of injuries, air pollution, psychological factors, and behavior collected through a custom developed mobile application. Herein, we introduce a biomechanical and musculoskeletal protocol to investigate musculoskeletal and neuro-mechanical health in this 4HAIE cohort, including a design for controlling for physiological and psychological injury factors. In the current ongoing project, we hypothesize that there will be interactions of environmental, biomechanical, physiological, and psychosocial variables and that these interactions will cause musculoskeletal diseases/protection