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

COVID-BEHAVE dataset:measuring human behaviour during the COVID-19 pandemic

Aiming to illuminate the effects of enforced confinements on people’s lives, this paper presents a novel dataset that measures human behaviour holistically and longitudinally during the COVID-19 outbreak. In particular, we conducted a study during the first wave of the lockdown, where 21 healthy subjects from the Netherlands and Greece participated, collecting multimodal raw and processed data from smartphone sensors, activity trackers, and users’ responses to digital questionnaires. The study lasted more than two months, although the duration of the data collection varies per participant. The data are publicly available and can be used to model human behaviour in a broad sense as the dataset explores physical, social, emotional, and cognitive domains. The dataset offers an exemplary perspective on a given group of people that could be considered to build new models for investigating behaviour changes as a consequence of the lockdown. Importantly, to our knowledge, this is the first dataset combining passive sensing, experience sampling, and virtual assistants to study human behaviour dynamics in a prolonged lockdown situation

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

First approach to automatic performance status evaluation and physical activity recognition in cancer patients

The evaluation of cancer patients’ recovery is still under the big subjectivity of physicians. Many different systems have been successfully implemented for physical activity evaluation, nonetheless there is still a big leap into Performance Status evaluation with ECOG and Karnofsky’s Performance Status scores. An automatic system for data recovering based on Android smartphone and wearables has been developed. A gamification implementation has been designed for increasing patients’ motivation in their recovery. Furthermore, novel and without-precedent algorithms for Performance Status (PS) and Physical Activity (PA) assessment have been developed to help oncologists in their diagnoses

Sistema automático de captura de movimiento en 2D para evaluación del riesgo de lesión de rodilla

La medida de los ángulos articulares del ser humano es frecuentemente utilizada como indicador de riesgo de lesión, especialmente en los miembros inferiores. Comúnmente se hace uso de la proyección bidimensional de estos ángulos como estimador de estas medidas. Sin embargo, los sistemas tradicionales de medida requieren un largo tiempo de análisis offline. En este artículo se presenta un sistema de captura y análisis en tiempo real de los ángulos articulares en 2D haciendo uso del sensor infrarrojo incluido en la cámara Kinect V2 y marcadores retro-reflectantes. El sensor captura la posición de los marcadores reflectantes y la información registrada es procesada en tiempo real por un software que proporciona la medida del ángulo articular deseado. La fiabilidad del sistema ha sido validada frente a los procedimientos tradicionales de análisis offline, obteniendo excelentes resultados

Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.Consejo Nacional de Ciencia y Tecnologia (CONACyT)Sonora Institute of Technology (ITSON) via the PROFAPI program PROFAPI_2020_0055Spanish Ministry of Science, Innovation and Universities (MICINN) project "Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology and Biomedicine" RTI2018-101674-B-I0

Dealing with the effects of sensor displacement in wearable activity recognition

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.This work was supported by the High Performance Computing (HPC)-Europa2 project funded by the European Commission-DG Research in the Seventh Framework Programme under grant agreement No. 228398 and by the EU Marie Curie Network iCareNet under grant No. 264738. This work was also supported by the Spanish Comision Interministerial de Ciencia y Tecnologia (CICYT) Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476 and the FPU Spanish grant, AP2009-2244

An initiative for the creation of open datasets within pervasive healthcare

In this paper issues surrounding the collection, annotation, management and sharing of data gathered from pervasive health systems are presented. The overarching motivation for this work has been to provide an approach whereby annotated data sets can be made readily accessible to the research community in an effort to assist the advancement of the state-of-the-art in activity recognition and behavioural analysis using pervasive health systems. Recommendations of how this can be made a reality are presented in addition to the initial steps which have been taken to facilitate such an initiative involving the definition of common formats for data storage and a common set of tools for data processing and visualization

Agents united:An open platform for multi-agent conversational systems

The development of applications with intelligent virtual agents (IVA) often comes with integration of multiple complex components. In this article we present the Agents United Platform: an open source platform that researchers and developers can use as a starting point to setup their own multi-IVA applications. The new platform provides developers with a set of integrated components in a sense-remember-think-act architecture. Integrated components are a sensor framework, memory component, Topic Selection Engine, interaction manager (Flipper), two dialogue execution engines, and two behaviour realisers (ASAP and GRETA) of which the agents can seamlessly interact with each other. This article discusses the platform and its individual components. It also highlights some of the novelties that arise from the integration of components and elaborates on directions for future work