SmarterRoutes : data-driven road complexity estimation for level-of-detail adaptation of navigation services

SmarterRoutes aims to improve navigational services and make them more dynamic and personalised by data-driven and environmentally-aware road scene complexity estimation. SmarterRoutes divides complexity into two subtypes: perceived and descriptive complexity. In the SmarterRoutes architecture, the overall road scene complexity is indicated by combining and merging parameters from both types of complexity. Descriptive complexity is derived from geospatial data sources, traffic data and sensor analysis. The architecture is currently using OpenStreetMap (OSM) tag analysis, Meten-In-Vlaanderen (MIV) derived traffic info and the Alaro weather model of the Royal Meteorological Institute of Belgium (RMI) as descriptive complexity indicators. For the perceived complexity an image based complexity estimation mechanism is presented. This image based Densenet Convolutional Neural Network (CNN) uses Street View images as input and was pretrained on buildings with Bag-of-Words and Structure-from-motion features. The model calculates an image descriptor allowing comparison of images by calculation of the Euclidean distances between descriptors. SmarterRoutes extends this model by additional hand-labelled rankings of road scene images to predict visual road complexity. The reuse of an existing pretrained model with an additional ranking mechanism produces results corresponding with subjective assessments of end-users. Finally, the global complexity mechanism combines the aforementioned sub-mechanisms and produces a service which should facilitate user-centred context-aware navigation by intelligent data selection and/or omission based on SmarterRoutes’ complexity input

GPS driven camera selection in cyclocross races for automatic rider story generation

Cyclocross races are a very popular winter sport in Belgium and the Netherlands. In this paper we present a methodology to calculate the proximity of riders to a number of cameras that are located on a cyclocross course in order to automatically select the correct camera for each rider. The methodology is based on two main input sources. The first input is the course with cameras positioned along it. As the course and camera information is usually available as pdf and isn’t directly processable by computer programs, we propose the conversion GeoJSON. The second requirement for our methodology is accurate location tracking of the athletes on the course with the help of wearable GPS trackers. We present an experimental camera proximity algorithm that uses both input sources and finds for every rider at any given moment in the race the closest camera or vice versa. The output of this methodology results in automatic identification of the filmed riders by a given camera at a given moment in the race and might benefit post-processing of the camera video streams for further computer vision-based analysis of the streams, for example, to pre-filter the camera streams or to generate rider and team stories

Data-driven summarization of broadcasted cycling races by automatic team and rider recognition

The number of spectators for cycling races broadcasted on television is decreasing each year. More dynamic and personalized reporting formats are needed to keep the viewer interested. In this paper, we propose a methodology for data-driven summarization, which allows end-users to query for personalized stories of a race, tailored to their needs (such as the length of the clip and the riders and/or teams that they are interested in). The automatic summarization uses a combination of skeleton-based rider pose detection and pose-based recognition algorithms of the team jerseys and rider faces/numbers. Evaluation on both cyclocross and road cycling races show that there is certainly potential in this novel methodology

The need for data-driven bike fitting : data study of subjective expert fitting

The number of cyclists is growing rapidly, for commuting but also as a sport. With this growth, there has been an increasing interest in cycling position. Trainers, athletes and bike vendors acknowledged this and started to perform bike fits. As these experts have different backgrounds and varying levels of expertise, it was hypothesised that this could have an influence on the outcome in terms of the advised position. In this research three cyclists were bike fitted by nine different bike fitting studios. It was hypothesised that, as different bike fitters use varying techniques and have different experience levels, the cyclist would be advised a different optimal position by these different bike fitters. The preconceived hypothesis was confirmed as the range of advised positions in both saddle height and setback was up to 3 cm. Data-driven bike fitting can help bring down these considerable differences amongst fitters and will be discussed in the last chapter

Early evaluation of a screen-and-treat strategy using high-risk HPV testing for Uganda:Implications for screening coverage and treatment

BACKGROUND: Uganda has a high burden of cervical cancer and its current coverage of screening based on visual inspection with acetic acid (VIA) is low. High-risk HPV (hrHPV) testing is recommended by the World Health Organization as part of the global elimination strategy for cervical cancer. In this context, country-specific health economic evaluations can inform national-level decisions regarding implementation. We evaluated the recommended hrHPV screen-and-treat strategy to determine the minimum required levels of coverage and treatment adherence, as well as the maximum price level per test, for the strategy to be cost-effective in Uganda.METHODS: We conducted a headroom analysis to estimate potential room for spending on implementing the hrHPV screen-and-treat strategy at different levels of coverage and treatment adherence (from 10% to 100%) at each screening round, and at different price levels of the hrHPV test. We compared the strategy with the existing VIA-based screen-and-treat policy in Uganda. We calculated headroom as the product of number of life years gained by the strategy and the willingness-to-pay threshold, minus the incremental costs incurred by the strategy. Positive headroom was interpreted as an indication of cost-effectiveness.RESULTS: Compared with VIA-based screening with low 5% coverage, the hrHPV screen-and-treat strategy required at least 30% coverage and adherence for positive mean headroom, and compared with 30% VIA-based screening coverage, the minimum levels were 60%. At 60% coverage and adherence, the maximum acceptable price per hrHPV test was found to be between 15 and 30 international dollars.CONCLUSIONS: The hrHPV-based screen-and-treat strategy could be cost-effective in Uganda if the screening coverage and treatment adherence are at least 30% in each screening round, and if the price per test is set below 30 international dollars. The minimum required levels of screening coverage and adherence to treatment provide potential starting points for decision-makers in planning the rollout of hrHPV testing. The headroom estimates can guide the planning costs of screening infrastructure and campaigns to achieve the required coverage and treatment adherence in Uganda.</p

Investigating feasibility of 2021 WHO protocol for cervical cancer screening in underscreened populations:PREvention and SCReening Innovation Project Toward Elimination of Cervical Cancer (PRESCRIP-TEC)

Abstract Background High-risk human papillomavirus (hrHPV) testing has been recommended by the World Health Organization as the primary screening test in cervical screening programs. The option of self-sampling for this screening method can potentially increase women’s participation. Designing screening programs to implement this method among underscreened populations will require contextualized evidence. Methods PREvention and SCReening Innovation Project Toward Elimination of Cervical Cancer (PRESCRIP-TEC) will use a multi-method approach to investigate the feasibility of implementing a cervical cancer screening strategy with hrHPV self-testing as the primary screening test in Bangladesh, India, Slovak Republic and Uganda. The primary outcomes of study include uptake and coverage of the screening program and adherence to follow-up. These outcomes will be evaluated through a pre-post quasi-experimental study design. Secondary objectives of the study include the analysis of client-related factors and health system factors related to cervical cancer screening, a validation study of an artificial intelligence decision support system and an economic evaluation of the screening strategy. Discussion PRESCRIP-TEC aims to provide evidence regarding hrHPV self-testing and the World Health Organization’s recommendations for cervical cancer screening in a variety of settings, targeting vulnerable groups. The main quantitative findings of the project related to the impact on uptake and coverage of screening will be complemented by qualitative analyses of various determinants of successful implementation of screening. The study will also provide decision-makers with insights into economic aspects of implementing hrHPV self-testing, as well as evaluate the feasibility of using artificial intelligence for task-shifting in visual inspection with acetic acid. Trial registration ClinicalTrials.gov, NCT05234112 . Registered 10 February 202