ESS- BBB - Biomatemática, Bioestatística e Bioinformática
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- Adapted control methods for cerebral palsy users of an intelligent wheelchairPublication . Faria, Brigida Monica; Reis, Luis Paulo; Lau, NunoThe development of an intelligent wheelchair (IW) platform that may be easily adapted to any commercial electric powered wheelchair and aid any person with special mobility needs is the main objective of this project. To be able to achieve this main objective, three distinct control methods were implemented in the IW: manual, shared and automatic. Several algorithms were developed for each of these control methods. This paper presents three of the most significant of those algorithms with emphasis on the shared control method. Experiments were performed by users suffering from cerebral palsy, using a realistic simulator, in order to validate the approach. The experiments revealed the importance of using shared (aided) controls for users with severe disabilities. The patients still felt having complete control over the wheelchair movement when using a shared control at a 50% level and thus this control type was very well accepted. Thus it may be used in intelligent wheelchairs since it is able to correct the direction in case of involuntary movements of the user but still gives him a sense of complete control over the IW movement.
- Adaptive model for biofeedback data flows management in the design of interactive immersive environmentsPublication . Gomes, Paulo Veloso; Marques, António; Donga, João; Sá, Catarina; Correia, António; Pereira, JavierThe interactivity of an immersive environment comes up from the relationship that is established between the user and the system. This relationship results in a set of data exchanges between human and technological actors. The real-time biofeedback devices allow to collect in real time the biodata generated by the user during the exhibition. The analysis, processing and conversion of these biodata into multimodal data allows to relate the stimuli with the emotions they trigger. This work describes an adaptive model for biofeedback data flows management used in the design of interactive immersive systems. The use of an affective algorithm allows to identify the types of emotions felt by the user and the respective intensities. The mapping between stimuli and emotions creates a set of biodata that can be used as elements of interaction that will readjust the stimuli generated by the system. The real-time interaction generated by the evolution of the user’s emotional state and the stimuli generated by the system allows him to adapt attitudes and behaviors to the situations he faces.
- Advancing the understanding of pupil size variation in occupational safety and health: A systematic review and evaluation of open-source methodologiesPublication . Ferreira, Filipa; Ferreira, Simão; Mateus, Catarina; Rocha, Nuno; Coelho, Luís; Rodrigues, MatildePupil size can be used as an important biomarker for occupational risks. In recent years, there has been an increase in the development of open-source tools dedicated to obtaining and measuring pupil diameter. However, it remains undetermined determined whether these tools are suitable for use in occupational settings. This study explores the significance of pupil size variation as a biomarker for occupational risks and evaluates existing open-source methods for potential use in both research and occupational settings, with the goal of to prevent occupational accidents and improve the health and performance of workers. To this end, a two-phase systematic literature review was conducted in the Web of Science™, ScienceDirect®, and Scopus® databases. For the relevance of monitoring pupil size variation in occupational settings, 15 articles were included. The articles were divided into three groups: mental workload, occupational stress, and mental fatigue. In most cases, pupil dilation increased with workload enhancement and with higher levels of stress. Regarding fatigue, it was noted that an increase in this condition corresponded with a decrease in pupil size. With respect to the open-source methodologies, 16 articles were identified, which were categorized into two groups: algorithms and software. Convolutional neural networks (CNN)1 have exhibited superior performance among the various algorithmic approaches studied. Building on this insight, and considering the evaluations of software options, MEYE emerges as the premier open-source system for deployment in occupational settings due to its compatibility with a standard computer webcam. This feature positions MEYE as a particularly practical tool for workers in stable environments, like those of developers and administrators.
- Affinity coefficient for clustering autoregressive moving average modelsPublication . Nascimento, Ana Paula; Oliveira, Alexandra; Faria, Brígida Mónica; Pimenta, Rui; Vieira, Mónica; Prudêncio, Cristina; Nicolau, Helena BacelarIn various fields, such as economics, finance, bioinformatics, geology, and medicine, namely, in the cases of electroencephalogram, electrocardiogram, and biotechnology, cluster analysis of time series is necessary. The first step in cluster applications is to establish a similarity/dissimilarity coefficient between time series. This article introduces an extension of the affinity coefficient for the autoregressive expansions of the invertible autoregressive moving average models to measure their similarity between them. An application of the affinity coefficient between time series was developed and implemented in R. Cluster analysis is performed with the corresponding distance for the estimated simulated autoregressive moving average of order one. The primary findings indicate that processes with similar forecast functions are grouped (in the same cluster) as expected concerning the affinity coefficient. It was also possible to conclude that this affinity coefficient is very sensitive to the behavior changes of the forecast functions: processes with small different forecast functions appear to be well separated in different clusters. Moreover, if the two processes have at least an infinite number of π- weights with a symmetric signal, the affinity value is also symmetric.
- Age-period-cohort effects in the incidence of hip fractures: political and economic events are coincident with changes in riskPublication . Alves, Sandra Maria; Castiglione, Débora; Oliveira, Carla Maria; Sousa, Bruno; Pina, Maria De FátimaIntroduction: Healthcare improvements have allowed prevention but have also increased life expectancy, resulting in more people being at risk. Our aim was to analyse the separate effects of age, period and cohort on incidence rates by sex in Portugal, 2000–2008. Methods: From the National Hospital Discharge Register, we selected admissions (aged ≥49 years) with hip fractures (ICD9-CM, codes 820.x) caused by low/moderate trauma (falls from standing height or less), readmissions and bone cancer cases. We calculated person-years at risk using population data from Statistics Portugal. To identify period and cohort effects for all ages, we used an age–period–cohort model (1-year intervals) followed by generalised additive models with a negative binomial distribution of the observed incidence rates of hip fractures. Results: There were 77,083 hospital admissions (77.4 % women). Incidence rates increased exponentially with age for both sexes (age effect). Incidence rates fell after 2004 for women and were random for men (period effect). There was a general cohort effect similar in both sexes; risk of hip fracture altered from an increasing trend for those born before 1930 to a decreasing trend following that year. Risk alterations (not statistically significant) coincident with major political and economic change in the history of Portugal were observed around birth cohorts 1920 (stable–increasing), 1940 (decreasing–increasing) and 1950 (increasing–decreasing only among women). Conclusions: Hip fracture risk was higher for those born during major economically/politically unstable periods. Although bone quality reflects lifetime exposure, conditions at birth may determine future risk for hip fractures.
- An approach for assessing the distribution of reporting delay in portuguese AIDS dataPublication . Oliveira, Alexandra; Gaio, Ana Rita; da Costa, Joaquim Pinto; Reis, Luís PauloHIV/AIDS epidemic is an important public health problem. The burden of the epidemic is estimated from surveillance systems data. The collected information is incomplete, making the estimation a challenging task and the reported trends often biased. The most common incomplete-data problems, in this kind of data, are due to under-diagnosis and reporting delays, mainly in the most recent years. This is a classical problem for imputation methodologies. In this paper we study the distribution of AIDS reporting delays through a mix approach, combining longitudinal K-means with the generalized least squares method. While the former identifies homogeneous delay patterns, the latter estimated longitudinal regression curves. We found that a 2-cluster structure is appropriated to accommodate the heterogeneity in reporting delay on HIV/AIDS data and that the corresponding estimated delay curves are almost stationary over time.
- An approach to assess quality of life through biometric monitoring in cancer patientsPublication . Silva, Eliana; Aguiar, Joyce; Oliveira, Alexandra; Faria, Brígida Mónica; Reis, Luís Paulo; Carvalho, Victor; Gonçalves, Joaquim; Oliveira e Sá, JorgeCancer is a serious disease that causes significant disability and suffering, so naturally Health Related Quality of Life (HRQoL) is a major concern of patients, families and clinicians. This paper intends to relate biometric indices, in terms of HRV metrics, with self-perceived HRQoL from patients with lymphoma. Patients (N = 12) answered FACT questionnaire and used a smartband that collected biometrical data in real-time along the chemotherapy treatment. Our results revealed that Physical Well-Being, Total, Lymphoma subscale and FACT-Lym Trial Outcome domains seem to have a similar pattern that HRV metrics across the treatment cycles. In specific, the FACT domains and the HRV metrics have the lowest average levels on the first cycle and seem to increase along the following cycles (3rd and 6th cycles). This approach of continuous assessment of HRQoL will enable a better accuracy and more supported clinical decision.
- An empirical study about the variables that influence the social acceptability on the biogasPublication . Dias, Telma; Rodrigues, Matilde; Pereira, Ilídio; Leão, Celina P.Due to the excessive use of conventional energy sources, renewable energy technologies have becoming a potential alternative to provide a sustainable development of society. Biogas has been considered as one of the most environmental beneficial source of energy that contributes for reducing greenhouse gas emissions and global warming (Jiang et al., 2011). Furthermore, the production of biogas can be highly important in rural areas, where there are many treatment stations of organic wastes, being a good way to solve problems related to release gases into the atmosphere, as well as related to the bad smells. However, the society opposition, disinterest and lack of knowledge about the advantages of biogas production and its use, can be a barrier to the availability of the implementation of these renewable technologies. In this context, more studies are necessary to clarify the public acceptability of these technologies (Zyadin et al ., 2012), in order to get further information to help into design an intervention strategy.
- An investigation of digital skills of therapeutic radiographers/radiation therapists: A european survey of proficiency level and future educational needsPublication . Barbosa, B.; Oliveira, C.; Bravo, I.; Couto, G.; Antunes, L.; McFadden, S.; Hughes, C.; McClure, P.; Rodrigues, J.; Dias, A.G.This study aims to assess the proficiency level of digital skills, the factors influencing that level and the training needs of Therapeutic Radiographers/Radiation Therapists (TR/RTTs), due to the differences in technology availability and accessibility, variations in the regulation and education of TR/RTTs in European countries, and the lack of a digital skills framework. An online survey was distributed to TR/RTTs working in Europe to capture their self assessment of proficiency levels of digital skills when performing their clinical role. Information was also gathered regarding training, work experience and level of information and communication tech nology (ICT) skills. Quantitative measures were analysed using descriptive statistics and correlation between variables, and qualitative responses using thematic analysis. 101 respondents from 13 European countries completed the survey. Digital skills in treatment planning followed by management and research were the least developed skills, while the most developed were transversal digital skills followed by digital skills in treatment delivery. The Radio therapy areas of practice where TR/RTT has experience (e.g. Planning Image, Treatment Planning, Treatment), as well as the level of generic ICT skills (communication, content creation and problem solving), was related to the level of proficiency of TR/RTT digital skills. Greater scope of practice and level of generic ICT were associated with a higher level of TR/RTT digital skills. Thematic analysis allowed the identification of new sub-themes to be included in the training of TR/RTTs. Education and training of TR/RTTs should be improved and adapted to the current needs of digitalisation to avoid differences in digital proficiency levels. Implications for practice: Aligning TR/RTTs’ digital skill sets with emerging digitalisation will improve current practice and ensure the best care to all RT patients.
- An unobtrusive multimodal stress detection model & recommender systemPublication . Ferreira, Simão; Correia, Hugo; Rodrigues, Fátima; Rodrigues, Matilde; Rocha, NunoStudies estimate that about 50% of all lost workdays are related to occupational stress. In recent years, several solutions for mental health management, including biofeedback applications, have emerged as a way to enhance employee mental health. Solutions to mitigate risk factors related to the working settings present an enormous potential and a clear contribution. However, most of the work that has been developed is limited to laboratory environments and does not suit real-life needs. Our study proposes an unobtrusive multimodal approach for detecting work-related stress combining videoplethysmography and self-reported measures for stablishing the ground truth in real-life settings. The study involved 28 volunteers over a two-month period. Various physiological signals were collected through a videopletismography solution, while users were performing daily working, for approximately eight hours a day. In parallel, selfreported measures were collected via a pop-up application (developed by the research team) that periodically retrieved the user's perceived stress (amongst other variables) in order to label the physiological data. In order to develop the stress detection model, we pre-processed the data and performed Heart Rate Variability (HRV) feature extraction. Then, we experimented with several machine learning models, utilizing both individual and combined physiological signals to explore all available alternatives. After rigorous evaluation, the best-trained model achieved an accuracy of over 80% and an F1 Score of over 85%. With the stress detection model in place, we are developing a structured intervention model to help reduce stress. This intervention model integrates two interconnected dimensions through digital coaching, which prioritizes personalized recommendations based on user preferences. Our top priority is to ensure user engagement, and we believe that adherence to and adoption of recommended interventions are more likely when users receive recommendations that align with their preferences. Thus, we prioritize personalized recommendations that are tailored to each individual's unique model. After detecting immediate stress peaks and providing real-time feedback on stress levels, our alarm system goes a step further by offering customized recommendations for brief stress relief. The digital coach (intervention model) offers various recommendations and active lifestyle changes such as exercise, task management, weight management, better sleep habits, structured pauses, and other critical interventions. These critical interventions are also based on user preferences, allowing our system to prevent future stress-related incidents and, most importantly, mitigate long-term stress. This project and its methodology demonstrate that truly unobtrusive stress detection is possible and can be performed within the scope of ethical demands. In future work, we will evaluate the responses and beneficial outcomes of implementing a recommender system.