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Abstract The American Barn Owl (Tyto furcata) lives in urban, periurban and wild environments and feeds mainly on small rodents, meaning it has great importance in the biological control of pests. The aim of this work was to describe the reproductive, parental and eating habits of a pair of American barn owls naturally living outside a residence in the urban area of the municipality of Campos dos Goytacazes, Rio de Janeiro state, Brazil. A wood box was installed on an outside wall of the home, monitored by a video camera. A spreadsheet was created to keep track of the observations recorded. The female laid four eggs, and after an incubation period of 30-32 days all the eggs hatched, but only two chicks survived after cannibalism among the chicks. Initially, the male provided the food to the chicks and the female remained in the nest caring for the brood. After approximately a month, the female also began to leave the nest and return with prey, which was offered to the chicks, with the male also continuing this behavior. The chicks left the nest in September, 2017. The data obtained show the existence of cooperation and division of tasks between male and female owls during the reproductive period.

AbstractProlonged non-contact camera-based monitoring in critically ill patients presents unique challenges, but may facilitate safe recovery. A study was designed to evaluate the feasibility of introducing a non-contact video camera monitoring system into an acute clinical setting. We assessed the accuracy and robustness of the video camera-derived estimates of the vital signs against the electronically-recorded reference values in both day and night environments. We demonstrated non-contact monitoring of heart rate and respiratory rate for extended periods of time in 15 post-operative patients. Across day and night, heart rate was estimated for up to 53.2% (103.0 h) of the total valid camera data with a mean absolute error (MAE) of 2.5 beats/min in comparison to two reference sensors. We obtained respiratory rate estimates for 63.1% (119.8 h) of the total valid camera data with a MAE of 2.4 breaths/min against the reference value computed from the chest impedance pneumogram. Non-contact estimates detected relevant changes in the vital-sign values between routine clinical observations. Pivotal respiratory events in a post-operative patient could be identified from the analysis of video-derived respiratory information. Continuous vital-sign monitoring supported by non-contact video camera estimates could be used to track early signs of physiological deterioration during post-operative care.

Abstract The purpose of this study was to compare swing time and golf club angle parameters during golf swings using three, two dimensional (2D) low cost, Augmented-Video-based-Portable-Systems (AVPS) (Kinovea, SiliconCoach Pro, SiliconCoach Live). Twelve right-handed golfers performed three golf swings whilst being recorded by a high-speed 2D video camera. Footage was then analysed using AVPS-software and the results compared using both descriptive and inferential statistics. There were no significant differences for swing time and the golf phase measurements between the 2D and 3D software comparisons. In general, the results showed a high Intra class Correlation Coefficient (ICC > 0.929) and Cronbach’s Coefficient Alpha (CCA > 0.924) reliability for both the kinematic and temporal parameters. The inter-rater reliability test for the swing time and kinematic golf phase measurements on average were strong. Irrespective of the AVPS software investigated, the cost effective AVPS can produce reliable output measures that benefit golf analyses.

AbstractCorticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.

Introduction. External manifestations of aging, and especially skin aging are the most important for modern women.Aim. The aim of our study was to identify a set of adverse factors that effect on the skin of women in menopausal transition (MP) and in postmenopause (PM), and to identify markers of skin aging in this category of women.Materials and methods. The study included 36 women in MP and PM suffering from MS. At the first stage, anamnesis was collected, anthropometric data and severity of MS were evaluated, and the hormonal profile of patients was determined. At the second stage, computer mapping of the skin was performed using the digital video camera Aramo SG with the skin XPpro program. Statistic analysis was performed using the SPSS v13.0 program. Spearman’s analysis was used to determine the relationship between anthropometric, clinical and anamnestic data and the measurements of skin condition of women in MP and PM. Results. According to the obtained data, the main triggers of skin aging of women in MP and PM are: the presence of metabolic disorders (obesity, metabolic syndrome), decrease of estradiol and progesterone levels in blood serum as well as increase of prolactin level and rhythm disturbance of melatonin secretion. Markers of skin aging in women with menopausal syndrome are decreased moisture, increased oiliness in T-zone, increased of skin pigmentation and enlarged pore size.Conclusions. The obtained data are useful for management involuting skin changes of women in PM and MP suffering from MS. The data justifies the relevancy to normalization not only the estradiol level, but other sex steroids, melatonin, as well as correction of metabolic endocrine processes, and treatment of MS.

Gait analysis is used in many fields such as Medical Diagnostics, Osteopathic medicine, Comparative and Sports-related biomechanics, etc. The most commonly used system for capturing gait is the advanced video camera-based passive marker system such as VICON. However, such systems are expensive, and reflective markers on subjects can be intrusive and time-consuming. Moreover, the setup of markers for certain rehabilitation patients, such as people with stroke or spinal cord injuries, could be difficult. Recently, some markerless systems were introduced to overcome the challenges of marker-based systems. However, current markerless systems have low accuracy and pose other challenges in gait analysis with people in long clothing, hiding the gait kinematics. The present work attempts to make an affordable, easy-to-use, accurate gait analysis system while addressing all the mentioned issues. The system in this study uses images from a video taken with a smartphone camera (800 × 600 pixels at an average rate of 30 frames per second). The system uses OpenPose, a 2D real-time multi-person keypoint detection technique. The system learns to associate body parts with individuals in the image using Convolutional Neural Networks (CNNs). This bottom-up system achieves high accuracy and real-time performance, regardless of the number of people in the image. The proposed system is called the “OpenPose based Markerless Gait Analysis System” (OMGait). Ankle, knee, and hip flexion/extension angle values were measured using OMGait in 16 healthy volunteers under different lighting and clothing conditions. The measured kinematic values were compared with a standard video camera based normative dataset and data from a markerless MS Kinect system. The mean absolute error value of the joint angles from the proposed system was less than 90 for different lighting conditions and less than 110 for different clothing conditions compared to the normative dataset. The proposed system is adequate in measuring the kinematic values of the ankle, knee, and hip. It also performs better than the markerless systems like MS Kinect that fail to measure the kinematics of ankle, knee, and hip joints under dark and bright light conditions and in subjects with long robe clothing.

PurposeThis study aims to show what interventions in human factors can effectively reduce construction workers' unsafe behavior.Design/methodology/approachA diagnostic intervention model targeted the construction workers' weakest internal factors. The workers' behavior and cognition data were collected via a questionnaire and a video camera system from two medium-sized construction sites. A safety supervisor accompanied each site supervisor to improve construction workers' internal factors by implementing the designed intervention measures.FindingsThe statistical analysis results confirmed a persistent positive effect on construction workers' safe behavior by improving internal factors. Among the intervention programs applied, those aimed to improve the subjective norms, safety knowledge and attitudes had the most significant effect sizes.Practical implicationsThe findings of this case study advise project managers to design a specific behavioral intervention that aims at improving construction workers' significant internal factors, including subjective norms, safety attitudes, habits and knowledge together with demographic characteristics to reduce construction workers' unsafe behavior.Originality/valueWhile the declining rate of construction accidents approaches an asymptote which is still high, this study suggests that targeting the individual internal factors through diagnostic interventions is the key to further reduce the rate by improving construction workers' behavior.

There are numerous articles that study the ground reaction forces during the golf swing, among which only a few analyze the pressure pattern distributed on the entire surface of the foot. The current study compares the pressure patterns on the foot insoles of fifty-five golfers, from three different performance levels, playing swings with driver and 5-iron clubs in the driving range. Five swings were selected for each club. During each swing, ultra-thin insole sensors (4 sensors/cm^2) measure foot pressure at the frequency of 100 Hz. To perform statistical analysis, insole sensors are clustered to form seven areas, with the normalized pressure of each area being our dependent variable. A video camera was used to label the five key instants of the swing. Statistical analysis demonstrates a significant difference between the pressure distribution pattern of the left and right feet for both driver and 5-iron. However, the pressure distribution pattern remains almost the same when switching the club type from 5-iron to driver. We have also observed that there are significant differences between the pattern of professionals and players with medium and high handicap. The obtained pattern agrees with the principle of weight transfer with a different behavior between the medial and lateral areas of the foot.

A simultaneous localization and mapping (SLAM) algorithm allows a mobile robot or a driverless car to determine its location in an unknown and dynamic environment where it is placed, and simultaneously allows it to build a consistent map of that environment. Driverless cars are becoming an emerging reality from science fiction, but there is still too much required for the development of technological breakthroughs for their control, guidance, safety, and health related issues. One existing problem which is required to be addressed is SLAM of driverless car in GPS denied-areas, i.e., congested urban areas with large buildings where GPS signals are weak as a result of congested infrastructure. Due to poor reception of GPS signals in these areas, there is an immense need to localize and route driverless car using onboard sensory modalities, e.g., LIDAR, RADAR, etc., without being dependent on GPS information for its navigation and control. The driverless car SLAM using LIDAR and RADAR involves costly sensors, which appears to be a limitation of this approach. To overcome these limitations, in this article we propose a visual information-based SLAM (vSLAM) algorithm for GPS-denied areas using a cheap video camera. As a front-end process, features-based monocular visual odometry (VO) on grayscale input image frames is performed. Random Sample Consensus (RANSAC) refinement and global pose estimation is performed as a back-end process. The results obtained from the proposed approach demonstrate 95% accuracy with a maximum mean error of 4.98.

The method and structural scheme of an information-measuring system for determining the parameters of objects' movements (technological equipment in the quarry for extracting block natural stone) have been proposed. A distinctive feature of time video sequences containing images of measured objects is their adaptation and adjustment in accordance with the intensity of movement and accuracy requirements for measurement results. Structural and software-algorithmic methods were also applied for improving the accuracy of measurements of motion parameters, namely: complexation of two measuring channels and exponential smoothing of digital references. One of the measuring channels is based on a digital video camera, the second is based on an accelerometer mounted on an object and two integrators. Exponential smoothing makes it possible to take into consideration the previous countdowns of movement parameters with weight coefficients. That ensures accounting for the existing patterns of movement of the object and reducing the errors when measuring the parameters of movement by (1.4...1.6) times. The resulting solutions have been implemented in the form of an information and measurement system. The technological process of extracting blocks of natural stone in the quarry was experimentally investigated using a diamond-rope installation. Based on the contactless measurement of motion parameters, it is possible to ensure control over this process and improve the quality of blocks made of natural stone. Based on the experimental study of measurement errors, recommendations were given for the selection of adaptive parameters of a video sequence, namely the size of images and the value of the inter-frame interval. In addition, methods for the software-algorithmic processing of measuring information were selected, specifically exponential smoothing and averaging the coordinates of the contour of an object, measured in 30 adjacent lines of the image