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Freestall comfort is reflected in various indicators, including the ability for dairy cattle to display unhindered posture transition movements in the cubicles. To ensure farm animal welfare, it is instrumental for the farm management to be able to continuously monitor occurrences of abnormal motions. Advances in computer vision have enabled accurate kinematic measurements in several fields such as human, equine and bovine biomechanics. An important step upstream to measuring displacement during posture transitions is to determine that the behavior is accurately detected. In this study, we propose a framework for detecting lying to standing posture transitions from 3D pose estimation data. A multi-view computer vision system recorded posture transitions between Dec. 2021 and Apr. 2022 in a Swedish stall housing 183 individual cows. The output data consisted of the 3D coordinates of specific anatomical landmarks. Sensitivity of posture transition detection was 88.2% while precision reached 99.5%. Analyzing those transition movements, breakpoints detected the timestamp of onset of the rising motion, which was compared with that annotated by observers. Agreement between observers, measured by intra-class correlation, was 0.85 between 3 human observers and 0.81 when adding the automated detection. The intra-observer mean absolute difference in annotated timestamps ranged from 0.4s to 0.7s. The mean absolute difference between each observer and the automated detection ranged from 1.0s to 1.3s. There was a significant difference in annotated timestamp between all observer pairs but not between the observers and the automated detection, leading to the conclusion that the automated detection does not introduce a distinct bias. We conclude that the model is able to accurately detect the phenomenon of interest and that it is equatable to an observer.

This study investigates the combination of audio and image data to classify road conditions, particularly focusingon loose gravel scenarios. The dataset underwent binary categorisation, comprising audio segments capturinggravel sounds and corresponding images. Early feature fusion, utilising a pre-trained Very Deep ConvolutionalNetworks 19 (VGG19) and Principal component analysis (PCA), improved the accuracy of the Random Forestclassifier, surpassing other models in accuracy, precision, recall, and F1-score. Late fusion, involving decisionlevelprocessing with logical disjunction and conjunction gates (AND and OR) in combination with individualclassifiers for images and audio based on Densely Connected Convolutional Networks 121 (DenseNet121),demonstrated notable performance, especially with the OR gate, achieving 97 % accuracy. The late fusionmethod enhances adaptability by compensating for limitations in one modality with information from the other.Adapting maintenance based on identified road conditions minimises unnecessary environmental impact. Thismethod can help to identify loose gravel on gravel roads, substantially improving road safety and implementing aprecise maintenance strategy through a data-driven approach.

Maintaining gravel roads is crucial, as loose gravel poses safety risks and increases vehicle costs. Current methods used by the Swedish road administration, Trafikverket, are subjective and time-consuming. Road agencies need a cost-effective, efficient, and unbiased approach to assess gravel road conditions. Studies show human ratings are error-prone and inconsistent. This study aims to develop an automatic method for estimating loose gravel using audio recordings from inside a vehicle, capturing the sound of gravel hitting the car's bottom. These recordings were classified into four classes based on Trafikverket regulations. Sound features were extracted and analysed using supervised machine-learning methods. The Multilayer Perceptron (MLP) achieved the highest classification accuracy of 0.96, with an F1 score, recall, and precision of 0.97. Results indicate that audio data can effectively classify loose gravel conditions.

The rigid structure of free stall partitions interferes with the natural movements of cows as they transition between postures, often resulting in abnormal motions like sideways head lunge. Although most stalls allow for forward lunge room, cows are frequently hindered in their posture transition movements. Methods are needed to evaluate effective lunge room and detect sideways lunge. The Sony multi-camera system generated 3D pose estimation to measure lunge distance and angle during lying-to-standing posture transitions (n = 493 bouts). After validating lunge timestamp detection against 3 observers (n = 165 annotated bouts), we explored features associated with abnormal rising. Agreement between observers on lunge timestamp was very high, as per an intra-class correlation of 0.97, and 0.95 when adding the automated detection. Mean absolute difference (MAD) in annotated lunge timestamp between observers and machine was 0.33s while mean difference was 0.03s ± 0.03 indicating a minor difference and no substantial bias. In comparison, average intra-observer MAD was 0.2s. Lunge angle had a mean of 166.1° ± 0.5 and was skewed to the left by -1.31, indicating that most motions occurred with the body in a relatively straight line and that sideways lunging occurred at a lower frequency. Using a linear regression, a significant effect of height at the withers (p = 0.003) and of lunge distance (P < 0.001) were found on lunge angle. These results show that the standardized cubicle does not accommodate all individuals' proper lunge. They further suggest that 3D pose estimation is a promising technology for measuring the kinematics of lunging motions. © 2024 11th European Conference on Precision Livestock Farming. All rights reserved.

AIM: Older adults are increasingly encouraged to continue living in their own homes with support from home care services. However, few studies have focused on older adults' safety in home care. This study explored associations between the sense of security and factors related to demographic characteristics and home care services.

METHODS: The mixed longitudinal design was based on a retrospective national survey. The study population consisted of individuals in Sweden (aged 65+ years) granted home care services at any time between 2016 and 2020 (n=82,834-94,714). Multiple ordinal logistic regression models were fitted using the generalised estimation equation method to assess the strength of relationship between the dependent (sense of security) and independent (demographics, health and care-related factors) variables.

RESULTS: The sense of security tended to increase between 2016 and 2020, and was significantly associated with being a woman, living outside big cities, being granted more home care services hours or being diagnosed/treated for depression (cumulative odds ratio 2-9% higher). Anxiety, poor health and living alone were most strongly associated with insecurity (cumulative odds ratio 17-64% lower). Aside from overall satisfaction with home care services, accessibility and confidence in staff influenced the sense of security most.

CONCLUSIONS: We stress the need to promote older adults' sense of security for safe ageing in place, as mandated by Swedish law. Home care services profoundly influence older adults' sense of security. Therefore, it is vital to prioritise continuity in care, establish trust and build relationships with older adults. Given the increasing shortage of staff, integrating complementary measures, such as welfare technologies, is crucial to promoting this sense of security.

The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs. The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden. The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates. The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively. The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97%, respectively. The RF and SVM models also performed well, with accuracy values of 74% and 79% and R2 ranging from 59% to 92%. The findings demonstrate the potential of machine learning to effectively predict the status and daylight chromaticity of road signs and their impact on road safety in the Swedish context. © 2023 International Association of Traffic and Safety Sciences

BACKGROUND: In Sweden, older people in residential care had the highest mortality rates, followed by those who received home care, during the coronavirus disease 2019 (COVID-19) pandemic. Staff working in the care of older people assumed responsibility for preventing the spread of the virus despite lacking the prerequisites and training. This study aimed to investigate the psychosocial work environment during the COVID-19 pandemic among staff in the care of older people and examine the factors associated with staff's perceptions of the clarity of instructions and the ability to follow them.

METHODS: A cross-sectional study design was employed using a web survey. The staff's perceptions of their psychosocial environment were analysed using descriptive statistics. The association between organisational and individual factors, as well as the degree of clarity of the instructions and the staff's ability to follow them, were assessed using multivariate (ordinal) regression analysis.

RESULTS: The main findings show that perceptions of the clarity and adaptability of the instructions were primarily correlated with organisational factors, as higher responses (positive) for the subscales focusing on role clarity, support and encouragement in leadership at work were associated with the belief that the instructions were clear. Similarly, those indicating high job demands and high individual learning demands were less likely to report that the instructions were clear. Regarding adaptability, high scores for demands on learning and psychological demands were correlated with lower adaptability, while high scores for role clarity, encouraging leadership and social support, were associated with higher adaptability.

CONCLUSIONS: High job demands and individual learning demands were demonstrated to decrease the staff's understanding and adoption of instructions. These findings are significant on an organisational level since the work environment must be prepared for potential future pandemics to promote quality improvement and generally increase patient safety and staff health.

The concept of cumulative impacts is widespread in policy documents, regulations and ecological studies, but quantification methods are still evolving. Infrastructure development usually takes place in landscapes with preexisting anthropogenic features. Typically, their impact is determined by computing the distance to the nearest feature only, thus ignoring the potential cumulative impacts of multiple features. We propose the cumulative ZOI approach to assess whether and to what extent anthropogenic features lead to cumulative impacts. The approach estimates both effect size and zone of influence (ZOI) of anthropogenic features and allows for estimation of cumulative effects of multiple features distributed in the landscape. First, we use simulations and an empirical study to understand under which circumstances cumulative impacts arise. Second, we demonstrate the approach by estimating the cumulative impacts of tourist infrastructure in Norway on the habitat of wild reindeer (Rangifer t. tarandus), a near-threatened species highly sensitive to anthropogenic disturbance. In the simulations, we showed that analyses based on the nearest feature and our cumulative approach are indistinguishable in two extreme cases: when features are few and scattered and their ZOI is small, and when features are clustered and their ZOI is large. The empirical analyses revealed cumulative impacts of private cabins and tourist resorts on reindeer, extending up to 10 and 20 km, with different decaying functions. Although the impact of an isolated private cabin was negligible, the cumulative impact of ‘cabin villages’ could be much larger than that of a single large tourist resort. Focusing on the nearest feature only underestimates the impact of ‘cabin villages’ on reindeer. The suggested approach allows us to quantify the magnitude and spatial extent of cumulative impacts of point, linear, and polygon features in a computationally efficient and flexible way and is implemented in the oneimpact R package. The formal framework offers the possibility to avoid widespread underestimations of anthropogenic impacts in ecological and impact assessment studies and can be applied to a wide range of spatial response variables, including habitat selection, population abundance, species richness and diversity, community dynamics and other ecological processes. © 2023 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Road maintenance agencies subjectively assess loose gravel as one of the parameters for determininggravel road conditions. This study aims to evaluate the performance of deep learning-based pretrainednetworks in rating gravel road images according to classical methods as done by humanexperts. The dataset consists of images of gravel roads extracted from self-recorded videos andimages extracted from Google Street View. The images were labelled manually, referring to thestandard images as ground truth defined by the Road Maintenance Agency in Sweden (Trafikverket).The dataset was then partitioned in a ratio of 60:40 for training and testing. Various pre-trainedmodels for computer vision tasks, namely Resnet18, Resnet50, Alexnet, DenseNet121, DenseNet201,and VGG-16, were used in the present study. The last few layers of these models were replaced toaccommodate new image categories for our application. All the models performed well, with anaccuracy of over 92%. The results reveal that the pre-trained VGG-16 with transfer learning exhibitedthe best performance in terms of accuracy and F1-score compared to other proposed models.

The road traffic signs in Sweden have no inventory system and it is unknown when a sign has reached the end of its service life and needs to be replaced. As a result, the road authorities do not have a systematic maintenance program for road traffic signs, and many signs which are not in compliance with the minimum retroreflectivity performance requirements are still found on the roads. Therefore, it is very important to find an inexpensive, safe, easy, and highly accurate method to judge the retroreflectivity performance of road signs. This will enable maintenance staff to determine the retroreflectivity of road signs without requiring measuring instruments for retroreflectivity or colors performance. As a first step toward the above goal, this paper aims to identify factors affecting the retroreflectivity of road signs. Two different datasets were used, namely, the VTI dataset from Sweden and NMF dataset from Denmark. After testing different models, two logarithmic regression models were found to be the best-fitting models, with R2 values of 0.50 and 0.95 for the VTI and NMF datasets, respectively. The first model identified the age, direction, GPS positions, color, and class of road signs as significant predictors, while the second model used age, color, and the class of road signs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.