Classification of the Physical Surface in Sound-based Uroflowmetry.

This work constitutes a first approach to automatically classify the urination medium for non-invasive sound based uroflowmetry tests. Often the voiding flow impacts the toilet wall (often made of ceramic) instead of the water. This causes a reduction in the amplitude of the recorded audio signal, and thus a reduction in the amplitude of the extracted envelope.

Privacy-Preserving Automatic Collection of Acoustic Voiding Events.

Uroflowmetry is a non-invasive diagnostic test used to evaluate the function of the urinary tract. Despite its benefits, it has two main limitations: high intra-subject variability of flow parameters and the requirement for patients to urinate on demand. To overcome these limitations, we have developed a low-cost ultrasonic platform that utilizes machine learning (ML) models to automatically detect and record natural in-home voiding events, without any need for user intervention.

Cultural adaptation of the iSupport online training and support programme for caregivers of people with dementia in Castilla y León, Spain.

Background: E-learning has shown to be an effective intervention in helping informal caregivers of people living with dementia. It has the potential to reach people living in remote areas, increasing service coverage. As a response to the demographic context in Spain associated with a higher percentage of ageing, depopulation, and the complexities of health service delivery in rural areas, this paper describes the cultural adaptation and co-design of the iSupport online training and support programme for Castilla y León, Spain, as a potential e-health intervention to mitigate these constraints.

Automatic Classification of Audio Uroflowmetry with a Smartwatch.

Prior work has shown the classification of voiding dysfunctions from uroflowmeter data using machine learning. We present the use of smartwatch audio, collected through the UroSound platform, in order to automatically classify voiding signals as normal or abnormal, using classical machine learning techniques. We train several classification models using classical machine learning and report a maximal test accuracy of 86.

UroSound: A Smartwatch-Based Platform to Perform Non-Intrusive Sound-Based Uroflowmetry.

Leveraging consumer technology such as smartwatches to objectively and remotely assess people with voiding dysfunction could capture unique features for prompt diagnosis of a disease. This paper presents the UroSound, the first platform that performs non-intrusive sound-based uroflowmetry with a smartwatch. We study the feasibility of using a smartwatch to assess how well the urinary tract functions by processing the sound generated when the urine stream hits the water level in the toilet bowl, which can be modelled through the sound envelope.

Enhancing improved heuristic drift elimination for step-and-heading based pedestrian dead-reckoning systems.

Location based services can improve the quality of patient care and increase the efficiency of the healthcare systems. Among the different technologies that provide indoor positioning, inertial sensors based pedestrian dead-reckoning (PDR) is one of the more cost-effective solutions, but its performance is limited by drift problems. Regarding the heading drift, some heuristics make use of the building's dominant directions in order to reduce this problem.

Conditional Random Field-Based Offline Map Matching for Indoor Environments.

In this paper, we present an offline map matching technique designed for indoor localization systems based on conditional random fields (CRF). The proposed algorithm can refine the results of existing indoor localization systems and match them with the map, using loose coupling between the existing localization system and the proposed map matching technique. The purpose of this research is to investigate the efficiency of using the CRF technique in offline map matching problems for different scenarios and parameters.

Ubiquitous connected train based on train-to-ground and intra-wagon communications capable of providing on trip customized digital services for passengers.

During the last years, the application of different wireless technologies has been explored in order to enable Internet connectivity from vehicles. In addition, the widespread adoption of smartphones by citizens represents a great opportunity to integrate such nomadic devices inside vehicles in order to provide new and personalized on trip services for passengers. In this paper, a proposal of communication architecture to provide the ubiquitous connectivity needed to enhance the smart train concept is presented and preliminarily tested.

E-field errors associated with RF dosimeters for RF human exposure assessment in urban environments.

In this paper, the potential errors which could be associated with body-worn RF dosimeters have been analyzed in a multipath environment. A statistical analysis of the azimuth power distribution on average in an urban environment has been done by using the two-dimensional ray-tracing technique. The surface electric field on a human body when it impinges with different angles of arrival has been characterized based on the finite-difference time-domain method.