State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

State of the Art of Audio- and Video-Based Solutions for AAL

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters. Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals. Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely lifelogging and self-monitoring, remote monitoring of vital signs, emotional state recognition, food intake monitoring, activity and behaviour recognition, activity and personal assistance, gesture recognition, fall detection and prevention, mobility assessment and frailty recognition, and cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed

Mobility classification of cattle with micro-Doppler radar

Lameness in dairy cattle is a welfare concern that negatively impacts animal productivity and farmer profitability. Micro-Doppler radar sensing has been previously suggested as a potential system for automating lameness detection in ruminants. This thesis investigates the refinement of the proposed automated system by analysing and enhancing the repeatability and accuracy of the existing scoring method in cattle mobility scoring, used to provide labels in machine learning. The main aims of the thesis were (1) to quantify the performance of the micro-Doppler radar sensing method for the assessment of mobility, (2) to characterise and validate micro-Doppler radar signatures of dairy cattle with varying degrees of gait impairment, and (3) to develop machine learning algorithms that can infer the mobility status of the animals under test from their radar signatures and support automatic contactless classification. The first study investigated inter-assessor agreement using a 4-level system and modifications to it, as well as the impact of factors such as mobility scoring experience, confidence in scoring decisions, and video characteristics. The results revealed low levels of agreement between assessors' scores, with kappa values ranging from 0.16 to 0.53. However, after transforming and reducing the mobility scoring system levels, an improvement was observed, with kappa values ranging from 0.2 to 0.67. Subsequently, a longitudinal study was conducted using good-agreement scores as ground truth labels in supervised machine-learning models. However, the accuracy of the algorithmic models was found to be insufficient, ranging from 0.57 to 0.63. To address this issue, different labelling systems and data pre-processing techniques were explored in a cross-sectional study. Nonetheless, the inter-assessor agreement remained challenging, with an average kappa value of 0.37 (SD = 0.16), and high-accuracy algorithmic predictions remained elusive, with an average accuracy of 56.1 (SD =16.58). Finally, the algorithms' performance was tested with high-confidence labels, which consisted of only scores 0 and 3 of the AHDB system. This testing resulted in good classification accuracy (0.82), specificity (0.79), and sensitivity (0.85). This led to the proposal of a new approach to producing labels, testing vantage point changes, and improving the performance of machine learning models (average accuracy = 0.7 & SD = 0.17, average sensitivity = 0.68 & SD = 0.27, average specificity = 0.75 & SD = 0.17). The research identified a challenge in creating high-confidence diagnostic labels for supervised machine learning-based algorithms to automate the detection and classification of lameness in dairy cows. As a result, the original goals were partially overridden, with the focus shifted to creating reliable labels that would perform well with radar data and machine learning. This point was considered necessary for smooth system development and process automation. Nevertheless, we managed to quantify the performance of the micro-Doppler radar system, partially develop the supervised machine learning algorithms, compare levels of agreement among multiple assessors, evaluate the assessment tools, assess the mobility evaluation process and gather a valuable data set which can be used as a foundation for subsequent studies. Finally, the thesis suggests changes in the assessment process to improve the prediction accuracy of algorithms based on supervised machine learning with radar data

Assessment of ambient assisted living systems for patients with mild cognitive impairment

According to the World Health Organization, about 50 million people worldwide suffer from dementia. Ten million new cases added every year. Mild Cognitive Impairment (MCI) affects more than 15% of the population aged 65. Technological solutions, such as smart home technology with ubiquitous computing devices, 24/7 telemedical observation and support can alleviate the growing problem and lower pressure on the healthcare system. This approach is also preferable for homecare patients in distant and rural areas. MCI patients are mostly home-based. Ambient Assisted Living (AAL) systems provide tools for automatic registration of vital signs and other medically and socially important information. AAL system for MCI patients is a logical answer to the problem. At the same time, many of the proposed AAL systems are proprietary, technically complicated and have a high price tag for implementation and service. Also, some proposed technical solutions not entirely reflect the opinion of healthcare stakeholders. The current study was proposed as a way to bridge the possible differences in the positions. An online anonymous questionnaire for healthcare professionals was created to prove or disprove the number of interconnected hypotheses about the necessity and feasibility of AAL system for MCI patients. The main focus was made on the hypotheses: "There is necessity of AAL systems for the healthcare" and "AAL systems are capable of providing assistance for patients with Mild Cognitive Impairment". The questionnaire was presented to more than three hundred potential respondents. Around a hundred and twenty agreed to fill it, and sixty completed the whole questionnaire. Results were analyzed to produce some directions guideline for future technical applications of AAL systems for MCI patients and future research. Descriptive statistics show support for the implementation of general AAL and variants for MCI patients. Comparative analysis of ordinal data for specific groups of respondents is done with help of non-parametric tests. Mann–Whitney–Wilcoxon test and Kruskal-Wallis test are applied. Table questions results are analyzed with chisquare for frequency tables. Group analysis demonstrated relative positive uniformity in of responses in the support of AAL of MCI patients.Segundo a Organização Mundial da Saúde, cerca de 50 milhões de pessoas em todo o mundo sofrem de demência. Dez milhões de novos casos adicionados a cada ano. O comprometimento cognitivo leve (MCI) afeta mais de 15% da população com 65 anos. Soluções tecnológicas, como tecnologia de casa inteligente com dispositivos de computação onipresentes, observação e suporte telemédico 24 horas por dia, 7 dias por semana, podem aliviar o problema crescente e diminuir a pressão sobre o sistema de saúde. Essa abordagem também é preferível para pacientes de cuidados domiciliares em áreas distantes e rurais. Os pacientes com CCL são, em sua maioria, domiciliares. Os sistemas Ambient Assisted Living (AAL) fornecem ferramentas para registro automático de sinais vitais e outras informações médicas e socialmente importantes. O sistema AAL para pacientes com MCI é uma resposta lógica para o problema. Ao mesmo tempo, muitos dos sistemas AAL propostos são proprietários, tecnicamente complicados e têm um alto preço para implementação e serviço. Além disso, algumas soluções técnicas propostas não refletem inteiramente a opinião das partes interessadas na área da saúde. O presente estudo foi proposto como forma de colmatar as possíveis diferenças nas posições. Um questionário anônimo online para profissionais de saúde foi criado para comprovar ou refutar o número de hipóteses interligadas sobre a necessidade e viabilidade do sistema AAL para pacientes com CCL. O foco principal foi feito nas hipóteses: "Há necessidade de sistemas de AAL para a saúde" e "Os sistemas de AAL são capazes de prestar assistência a pacientes com Comprometimento Cognitivo Leve". O questionário foi apresentado a mais de trezentos respondentes potenciais. Cerca de cento e vinte concordaram em preenchê-lo e sessenta preencheram todo o questionário. Os resultados foram analisados para produzir algumas diretrizes para futuras aplicações técnicas de sistemas AAL para pacientes com MCI e pesquisas futuras. Estatísticas descritivas mostram suporte para a implementação de AAL geral e variantes para pacientes com CCL. A análise comparativa de dados ordinais para grupos específicos de respondentes é feita com a ajuda de testes não paramétricos. Aplicam-se os testes de Mann-Whitney-Wilcoxon e Kruskal-Wallis. Os resultados das questões da tabela são analisados com qui-quadrado para tabelas de frequência. A análise do grupo demonstrou relativa uniformidade positiva nas respostas no suporte de AAL de pacientes com CCL.Selon l'Organisation mondiale de la santé, environ 50 millions de personnes dans le monde souffrent de démence. Dix millions de nouveaux cas ajoutés chaque année. Les troubles cognitifs légers (MCI) touchent plus de 15 % de la population âgée de 65 ans. Les solutions technologiques, telles que la technologie de la maison intelligente avec des appareils informatiques omniprésents, l'observation et le soutien télémédicaux 24 heures sur 24, 7 jours sur 7, peuvent atténuer le problème croissant et réduire la pression sur le système de santé. Cette approche est également préférable pour les patients en soins à domicile dans les régions éloignées et rurales. Les patients MCI sont pour la plupart à domicile. Les systèmes Ambient Assisted Living (AAL) fournissent des outils pour l'enregistrement automatique des signes vitaux et d'autres informations importantes sur le plan médical et social. Le système AAL pour les patients MCI est une réponse logique au problème. Dans le même temps, bon nombre des systèmes AAL proposés sont propriétaires, techniquement compliqués et ont un prix élevé pour la mise en oeuvre et le service. De plus, certaines solutions techniques proposées ne reflètent pas entièrement l'opinion des acteurs de santé. L'étude actuelle a été proposée comme un moyen de combler les différences possible dans les positions. Un questionnaire anonyme en ligne destiné aux professionnels de la santé a été créé pour prouver ou réfuter le nombre d'hypothèses interconnectées sur la nécessité et la faisabilité du système AAL pour les patients MCI. L'accent a été mis principalement sur les hypothèses: "Il existe une nécessité de systèmes AAL pour les soins de santé" et "Les systèmes AAL sont capables de fournir une assistance aux patients atteints de troubles cognitifs légers". Le questionnaire a été présenté à plus de trois cents répondants potentiels. Environ cent vingt ont accepté de le remplir, et soixante ont rempli tout le questionnaire. Les résultats ont été analysés pour produire des lignes directrices pour les futures applications techniques des systèmes AAL pour les patients MCI et l'avenir de la recherche. Les statistiques descriptives montrent un soutien à la mise en oeuvre de l'AAL général et des variantes pour les patients MCI. L'analyse comparative des données ordinales pour des groupes spécifiques de répondants est effectuée à l'aide de tests non paramétriques. Le test de Mann-Whitney-Wilcoxon et le test de Kruskal-Wallis sont appliqués. Les résultats des questions de tableau sont analysés avec le chi carré pour les tableaux de fréquence. L'analyse de groupe a démontré une uniformité positive relative dans les réponses à l'appui de l'AAL des patients MCI

An automatic wearable multi-sensor based gait analysis system for older adults.

Gait abnormalities in older adults are very common in clinical practice. They lead to serious adverse consequences such as falls and injury resulting in increased care cost. There is therefore a national imperative to address this challenge. Currently gait assessment is done using standardized clinical tools dependent on subjective evaluation. More objective gold standard methods (motion capture systems such as Qualisys and Vicon) to analyse gait rely on access to expensive complex equipment based in gait laboratories. These are not widely available for several reasons including a scarcity of equipment, need for technical staff, need for patients to attend in person, complicated time consuming procedures and overall expense. To broaden the use of accurate quantitative gait monitoring and assessment, the major goal of this thesis is to develop an affordable automatic gait analysis system that will provide comprehensive gait information and allow use in clinic or at home. It will also be able to quantify and visualize gait parameters, identify gait variables and changes, monitor abnormal gait patterns of older people in order to reduce the potential for falling and support falls risk management. A research program based on conducting experiments on volunteers is developed in collaboration with other researchers in Bournemouth University, The Royal Bournemouth Hospital and care homes. This thesis consists of five different studies toward addressing our major goal. Firstly, a study on the effects on sensor output from an Inertial Measurement Unit (IMU) attached to different anatomical foot locations. Placing an IMU over the bony prominence of the first cuboid bone is the best place as it delivers the most accurate data. Secondly, an automatic gait feature extraction method for analysing spatiotemporal gait features which shows that it is possible to extract gait features automatically outside of a gait laboratory. Thirdly, user friendly and easy to interpret visualization approaches are proposed to demonstrate real time spatiotemporal gait information. Four proposed approaches have the potential of helping professionals detect and interpret gait asymmetry. Fourthly, a validation study of spatiotemporal IMU extracted features compared with gold standard Motion Capture System and Treadmill measurements in young and older adults is conducted. The results obtained from three experimental conditions demonstrate that our IMU gait extracted features are highly valid for spatiotemporal gait variables in young and older adults. In the last study, an evaluation system using Procrustes and Euclidean distance matrix analysis is proposed to provide a comprehensive interpretation of shape and form differences between individual gaits. The results show that older gaits are distinguishable from young gaits. A pictorial and numerical system is proposed which indicates whether the assessed gait is normal or abnormal depending on their total feature values. This offers several advantages: 1) it is user friendly and is easy to set up and implement; 2) it does not require complex equipment with segmentation of body parts; 3) it is relatively inexpensive and therefore increases its affordability decreasing health inequality; and 4) its versatility increases its usability at home supporting inclusivity of patients who are home bound. A digital transformation strategy framework is proposed where stakeholders such as patients, health care professionals and industry partners can collaborate through development of new technologies, value creation, structural change, affordability and sustainability to improve the diagnosis and treatment of gait abnormalities

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

2020 Student Symposium Research and Creative Activity Book of Abstracts

The UMaine Student Symposium (UMSS) is an annual event that celebrates undergraduate and graduate student research and creative work. Students from a variety of disciplines present their achievements with video presentations. It’s the ideal occasion for the community to see how UMaine students’ work impacts locally – and beyond. The 2020 Student Symposium Research and Creative Activity Book of Abstracts includes a complete list of student presenters as well as abstracts related to their works

Sensing the care:Advancing unobtrusive sensing solutions to support informal caregivers of older adults with cognitive impairment

Older adults (65 years and above) make up a growing proportion of the world's population which is anticipated to increase further in the coming decades. As individuals age, they often become more vulnerable to cognitive impairments, necessitating a diverse array of care and support services from their caregivers to uphold their quality of life. However, the scarcity of professional caregivers and care facilities, compounded by the preference of many older adults to remain in their own homes, places a significant burden on informal caregivers, adversely affecting their physical, mental, and social well-being. To assist informal caregivers, numerous sensing solutions have been developed. However, many of these solutions are not optimally suited for older adult care, particularly in cases of cognitive impairments. In that regard, the overarching aim of this thesis was to develop and evaluate the Unobtrusive Sensing Solution (USS) for in-home monitoring of older adults with cognitive impairment (OwCI) who live alone in their own houses to ease the support of their informal caregivers. In the 'Explore and Scope' part, a scoping review was conducted to identify available unobtrusive sensing technology that can be implemented in older adult care. Subsequently, in the 'Develop and Test' part, Wi-Fi CSI technology was utilized to collect a dataset illustrating physical agitation activities (Wi-Gitation). However, upon evaluation of the Wi-Gitation dataset, a challenge of generalization across different domains (or environments) was identified. To address this, the Inter-data Selected Sequential Transfer Learning framework was proposed and implemented. Lastly, in the 'Design to Communicate' part, the thesis focused on identifying the needs and requirements of informal caregivers of OwCI towards USSs. These needs and requirements were gathered through interviews and surveys, informing the development of a Lo-Fi prototype for an interaction platform. Overall, the results obtained in this thesis not only enhance the development of Wi-Fi CSI (specifically for OwCI care) but also provide valuable insights into the informational and design requirements of informal caregivers, thereby promoting the context-aware development of USSs

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months