Analysis of the innovation outputs in mHealth for patient monitoring

Abstract—In the last decade, mobile health (mHealth) has developed as a natural consequence of the advances in mobile technologies, the growing spread of mobile devices, and their application in the provision of novel health services. mHealth has demonstrated the potential to make the health care sector more efficient and sustainable and to increase the healthcare quality. Considering the boost to the healthcare area which will be provided by mHealth, many organizations and governments have engaged in innovating in this area. In this context, this work investigated the role of innovation in the area of mHealth for patient monitoring in order to determine the trends and the performance of the innovation activities in this domain. Proxy indicators, like intellectual property statistics and scientific publication statistics, were utilized to measure the outputs of innovation during the period of time from 2006 to 2015 in Europe. Two studies were performed to provide quantitative measures for the indicators measuring innovation outputs in the domain of mHealth for patient monitoring and three main conclusions were observed. First, even if there was a lot of research in Europe in mHealth for patient monitoring, the vast majority of the enterprises did not protect their inventions. Second, a strong research collaboration in the area of mHealth for patient monitoring took place between researchers affiliated to institu- tions of different European countries and even with researchers working in Asian or American institutions. Finally, an increasing trend on the number of published articles about mHealth for patient monitoring was identified. Therefore, the findings of the studies demonstrated the great interest that has arisen the field of mHealth and the huge involvement in innovation activities in the area of mHealth for patient monitoring

HIGEA: An Intelligent Conversational Agent to Detect Caregiver Burden

Mental health disorders increasingly affect people worldwide. As a consequence, more families and relatives find themselves acting as caregivers. Most often, these are untrained people who experience loneliness, abandonment, and often develop signs of depression (i.e., caregiver burden syndrome). In this work, we present HIGEA, a digital system based on a conversational agent to help to detect caregiver burden. The conversational agent naturally embeds psychological test questions into informal conversations, which aim at increasing the adherence of use and avoiding user bias. A proof-of-concept is developed based on the popular Zarit Test, which is widely used to assess caregiver burden. Preliminary results show the system is useful and effective

Enabling remote assessment of cognitive behaviour through mobile experience sampling

Cognitive decline is among the normal processes of ageing, involving problems with memory, language, thinking and judgment, happening at different times and affecting people's live to a significant extent. Traditional clinical methods for cognitive assessment are conducted by experts once first symptoms appear. Mobile technologies can help supporting more immediate, continuous and ubiquitous measurements, thus potentially allowing for much earlier diagnosis of cognitive disorders. We present in this paper a digital mobile tool to administer cognitive tests in the form of multimedia experience sampling methods (ESM), which can run on a smartphone and can be scheduled and assessed remotely. The tool integrates digital cognitive ESM with passive sensor data that can be used to study the interplay of cognition and physical, social and emotional behaviours. We implement the Mini-Mental State Examination (MMSE) test, a clinical questionnaire extensively used to assess cognitive disorders, in order to showcase the possibilities offered by the proposed tool. Initial usability results show the tool to be perceived simple, easy and accessible for cognitively unimpaired persons

Health related quality of life of immigrant children: towards a new pattern in Germany?

Background: To study Health related quality of life (HRQoL) of a sample of kindergarten children with migration background. Methods: Five kindergartens in Frankfurt/Main and Darmstadt (Germany) participated. HRQoL was measured with the Kiddy-KINDL (KK) in 3 to 5 year old children. We examined the associations of HRQoL with socio-demographic variables, positive development and resilience, socio-emotional and motor development. Linear regression models were applied to examine differences in HRQoL between migrant and native-born German children. Results: The response rate was 90.5% (N = 283). The children had predominantly migrant background (81.35%). Perceived health was slightly higher in migrants (69.85, SD 17.00) compared to native-born German children (68.33, SD 17.31, p > 0.05), even though migrant children were characterized by a lower socio-economic status (p < 0.01). Conclusions: Results suggest that HRQoL at early ages in our study exhibits a different pattern than reported previously in studies among older individuals. We attribute the discrepancy partly to a possible changing pattern of migration in Europe with more migrants capable to migrate with healthy profiles, and to the age of our population. Our findings underscore the need to study the life course trajectory of HRQoL among young immigrants and replication in representative samples

Pediatric Health-Related Quality of Life:A Structural Equation Modeling Approach

Objectives: One of the most referenced theoretical frameworks to measure Health Related Quality of Life (HRQoL) is the Wilson and Cleary framework. With some adaptions this framework has been validated in the adult population, but has not been tested in pediatric populations. Our goal was to empirically investigate it in children.Methods: The contributory factors to Health Related Quality of Life that we included were symptom status (presence of chronic disease or hospitalizations), functional status (developmental status), developmental aspects of the individual (social-emotional) behavior, and characteristics of the social environment (socioeconomic status and area of education). Structural equation modeling was used to assess the measurement structure of the model in 214 German children (3-5 years old) participating in a follow-up study that investigates pediatric health outcomes.Results: Model fit was chi(2) = 5.5; df = 6; p = 0.48; SRMR = 0.01. The variance explained of Health Related Quality of Life was 15%. Health Related Quality of Life was affected by the area education (i.e. where kindergartens were located) and development status. Developmental status was affected by the area of education, socioeconomic status and individual behavior. Symptoms did not affect the model.Conclusions: The goodness of fit and the overall variance explained were good. However, the results between children' and adults' tests differed and denote a conceptual gap between adult and children measures. Indeed, there is a lot of variety in pediatric Health Related Quality of Life measures, which represents a lack of a common definition of pediatric Health Related Quality of Life. We recommend that researchers invest time in the development of pediatric Health Related Quality of Life theory and theory based evaluations.</p

Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review

Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather insufficient level of detail.This research has been partially funded by the Spanish project “Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology, and Biomedicine (RTI2018-101674-B-I00)” and the Andalusian project “Integration of heterogeneous biomedical information sources by means of high performance computing. Application to personalized and precision medicine (P20_00163)”. Funding for this research is provided by the EU Horizon 2020 Pharaon project ‘Pilots for Healthy and Active Ageing’ (no. 857188). Moreover, this research has received funding under the REMIND project Marie Sklodowska-Curie EU Framework for Research and Innovation Horizon 2020 (no. 734355). This research has been partially funded by the BALLADEER project (PROMETEO/2021/088) from the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana. Furthermore, it has been partially funded by the AETHER-UA (PID2020-112540RB-C43) project from the Spanish Ministry of Science and Innovation. This work has been also partially funded by “La Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital”, under the project “Development of an architecture based on machine learning and data mining techniques for the prediction of indicators in the diagnosis and intervention of autism spectrum disorder. AICO/2020/117”. This study was also funded by the Colombian Government through Minciencias grant number 860 “international studies for doctorate”. This research has been partially funded by the Spanish Government by the project PID2021-127275OB-I00, FEDER “Una manera de hacer Europa”. Moreover, this contribution has been supported by the Spanish Institute of Health ISCIII through the DTS21-00047 project. Furthermore, this work was funded by COST Actions “HARMONISATION” (CA20122) and “A Comprehensive Network Against Brain Cancer” (Net4Brain - CA22103). Sandra Amador is granted by the Generalitat Valenciana and the European Social Fund (CIACIF/ 2022/233)

PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring

Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users’ conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.This work was partially supported by the Spanish CICYT Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476, and the FPU Spanish Grant AP2009-2244. This work was also supported in part by the INTERREG IV European Project WHM-Wireless Health Monitoring (I-1-02=091) and the European Commission Seventh Framework Programme FP7 Project OPENi-Open-Source, Web-Based, Framework for Integrating Applications with Social Media Services, and Personal Cloudlets under Grant no. 317883

Temporada de pesca

Treballs de l'alumnat del Grau de Comunicació Audiovisual, Facultat d'Informació i Mitjans Audiovisuals, Universitat de Barcelona, Projectes II. Curs: 2019-2020, Tutor: Francesc Llinares. // Director: Guillem Villalonga i Colomé; Aj. Direcció: Ivette Herrero Serrano i Claudia Turmo Margalef; Direcció dárt: Ivette Herrero Serrano; Productor: Bernat Morros González; Aj. Producció: Marta Millán Jiménez, Lorena Sanchiz Rodríguez i Aina Cruz Arcas; Script i claqueta: Marta Millán Jiménez, Lorena Sanchiz Rodríguez i Claudia Turmo Margalef; Guionista: Guillem Villalonga i Colomé; Dir. Fotografia: Gabriel Alonso Díez; Càmera: Walter Luis Altamirano Castillo; Aj. càmera: Guillem Villalonga i Colomé; Il·luminador: Gabriel Alonso Díez; Storyboard: Claudia Turmo Margalef; Direcció de so: Bernat Morros González; Muntatge: Walter Luis Altamirano Castillo; Música: Roger Albet; Postproducció: Gabriel Alonso Díez. Equip artístic: Irieix Freixas, Aina Cruz Arcas, Elies Villalonga, Pau Rumbo, Claudia Turmo Margalef, Lorena Sanchiz Rodríguez, Marta Millán Jiménez, Gabriel Alonso Díez, Ivette Herrero Serrano, Mariona Fortuny i Guillem Villalonga i Colomé.Sato, un jove turmentat psicològicament per l’educació del seu pare, segresta turistes que no es comporten com ell creu correcte. Una noia que acaba d’arribar al poble on viu es fixa amb ell i s’enamoren. El canvi que la relació estava generant en tots dos es veu aturat quan ella descobreix els segrestos

60 Iconos Turísticos de Imbabura

Provincia de lagos y volcanes, biodiversa, artística, intercultural, multiétnica y poseedora de valores que la exponen como uno de los atractivos turístico más importantes del Ecuador. La Universidad Técnica del Norte mediante la publicación de este libro realiza un recorrido ilustrado de los 60 Iconos Turísticos de lmbabura, donde se investigan los ámbitos geográfico, histórico, cultural y social, lo que demuestra el potencial turístico de esta provincia y en donde los turistas podrán experimentar experiencias únicas. Imbabura cuenta con bondades turísticas de diferentes índoles. Este libro evidencia y difunde información técnica e ilustra con 181 magníficas fotografías la naturaleza, paisajes, flora, fauna, costumbres, cultura, historia, gastronomía, deportes, arte, arquitectura, artesanías y el patrimonio industrial, lo que representa una oportunidad para fortalecer el turismo y las implicaciones económicas y sociales que esto significa para la sociedad, lo que convierte a Imbabura en un patrimonio tangible e intangible del Ecuador y del mundo. La Universidad Técnica del Norte aporta en visibilizar y difundir los íconos turísticos más importantes de Imbabura, con base a una investigación exhaustiva en el marco de aportar al crecimiento y sostenibilidad del turismo y en procura del cumplimiento de la misión de la UTN. En este sentido, felicito a los profesores investigadores y fotógrafos autores de este libro, dirigidos por nuestro profesor de la FECYT, Albert Arnavat. Turismo diverso, cultural, rural, social y sostenible ponen a disposición los 60 íconos a los turistas nacionales y extranjeros, cuyos resultados servirán para convertir a la provincia de Imbabura en un icono turístico nacional e internacional

mDurance: A Novel Mobile Health System to Support Trunk Endurance Assessment

Low back pain is the most prevalent musculoskeletal condition. This disorder constitutes one of the most common causes of disability worldwide, and as a result, it has a severe socioeconomic impact. Endurance tests are normally considered in low back pain rehabilitation practice to assess the muscle status. However, traditional procedures to evaluate these tests suffer from practical limitations, which potentially lead to inaccurate diagnoses. The use of digital technologies is considered here to facilitate the task of the expert and to increase the reliability and interpretability of the endurance tests. This work presents mDurance, a novel mobile health system aimed at supporting specialists in the functional assessment of trunk endurance by using wearable and mobile devices. The system employs a wearable inertial sensor to track the patient trunk posture, while portable electromyography sensors are used to seamlessly measure the electrical activity produced by the trunk muscles. The information registered by the sensors is processed and managed by a mobile application that facilitates the expert’s normal routine, while reducing the impact of human errors and expediting the analysis of the test results. In order to show the potential of the mDurance system, a case study has been conducted. The results of this study prove the reliability of mDurance and further demonstrate that practitioners are certainly interested in the regular use of a system of this nature