A context-aware adaptive feedback agent for activity monitoring and coaching

A focus in treatment of chronic diseases is optimizing levels of physical activity. At Roessingh Research and Development, a system was developed, consisting of a Smartphone and an activity sensor, that can measure a patient’s daily activity behavior and provide motivational feedback messages. We are currently looking into ways of increasing the effectiveness of motivational messages that aim to stimulate sustainable behavioral change, by adapting its timing and content to individual patients in their current context of use

Learning from medical data streams: an introduction

Clinical practice and research are facing a new challenge created by the rapid growth of health information science and technology, and the complexity and volume of biomedical data. Machine learning from medical data streams is a recent area of research that aims to provide better knowledge extraction and evidence-based clinical decision support in scenarios where data are produced as a continuous flow. This year's edition of AIME, the Conference on Artificial Intelligence in Medicine, enabled the sound discussion of this area of research, mainly by the inclusion of a dedicated workshop. This paper is an introduction to LEMEDS, the Learning from Medical Data Streams workshop, which highlights the contributed papers, the invited talk and expert panel discussion, as well as related papers accepted to the main conference

Interpreting streaming biosignals:in search of best approaches to augmenting mobile health monitoring with machine learning for adaptive clinical decision support

We investigate Body Area Networks for ambulant patient monitoring. As well as sensing physiological parameters, BAN applications may provide feedback to patients. Automating formulation of feedback requires realtime analysis and interpretation of streaming biosignals and other context and knowledge sources. We illustrate with two prototype applications: the first is designed to detect epileptic seizures and support appropriate intervention. The second is a decision support application aiding weight management; the goal is to promote health and prevent chronic illnesses associated with overweight/obesity. We begin to explore extending these and other m-health applications with generic AI-based decision support and machine learning. Monitoring success of different behavioural change strategies could provide a basis for machine learning, enabling adaptive clinical decision support by personalising and adapting strategies to individuals and their changing needs. Data mining applied to BAN data aggregated from large numbers of patients opens up possibilities for discovery of new clinical knowledge

First steps of asthma management with a personalized ontology model

Asthma is a chronic respiratory disease characterized by severe inflammation of the bronchial mucosa. Allergic asthma is the most common form of this health issue. Asthma is classified into allergic and non-allergic asthma, and it can be triggered by several factors such as indoor and outdoor allergens, air pollution, weather conditions, tobacco smoke, and food allergens, as well as other factors. Asthma symptoms differ in their frequency and severity since each patient reacts differently to these triggers. Formal knowledge is selected as one of the most promising solutions to deal with these challenges. This paper presents a new personalized approach to manage asthma. An ontology-driven model supported by Semantic Web Rule Language (SWRL) medical rules is proposed to provide personalized care for an asthma patient by identifying the risk factors and the development of possible exacerbations

Key body pose detection and movement assessment of fitness performances

Motion segmentation plays an important role in human motion analysis. Understanding the intrinsic features of human activities represents a challenge for modern science. Current solutions usually involve computationally demanding processing and achieve the best results using expensive, intrusive motion capture devices. In this thesis, research has been carried out to develop a series of methods for affordable and effective human motion assessment in the context of stand-up physical exercises. The objective of the research was to tackle the needs for an autonomous system that could be deployed in nursing homes or elderly people's houses, as well as rehabilitation of high profile sport performers. Firstly, it has to be designed so that instructions on physical exercises, especially in the case of elderly people, can be delivered in an understandable way. Secondly, it has to deal with the problem that some individuals may find it difficult to keep up with the programme due to physical impediments. They may also be discouraged because the activities are not stimulating or the instructions are hard to follow. In this thesis, a series of methods for automatic assessment production, as a combination of worded feedback and motion visualisation, is presented. The methods comprise two major steps. First, a series of key body poses are identified upon a model built by a multi-class classifier from a set of frame-wise features extracted from the motion data. Second, motion alignment (or synchronisation) with a reference performance (the tutor) is established in order to produce a second assessment model. Numerical assessment, first, and textual feedback, after, are delivered to the user along with a 3D skeletal animation to enrich the assessment experience. This animation is produced after the demonstration of the expert is transformed to the current level of performance of the user, in order to help encourage them to engage with the programme. The key body pose identification stage follows a two-step approach: first, the principal components of the input motion data are calculated in order to reduce the dimensionality of the input. Then, candidates of key body poses are inferred using multi-class, supervised machine learning techniques from a set of training samples. Finally, cluster analysis is used to refine the result. Key body pose identification is guaranteed to be invariant to the repetitiveness and symmetry of the performance. Results show the effectiveness of the proposed approach by comparing it against Dynamic Time Warping and Hierarchical Aligned Cluster Analysis. The synchronisation sub-system takes advantage of the cyclic nature of the stretches that are part of the stand-up exercises subject to study in order to remove out-of-sequence identified key body poses (i.e., false positives). Two approaches are considered for performing cycle analysis: a sequential, trivial algorithm and a proposed Genetic Algorithm, with and without prior knowledge on cyclic sequence patterns. These two approaches are compared and the Genetic Algorithm with prior knowledge shows a lower rate of false positives, but also a higher false negative rate. The GAs are also evaluated with randomly generated periodic string sequences. The automatic assessment follows a similar approach to that of key body pose identification. A multi-class, multi-target machine learning classifier is trained with features extracted from previous motion alignment. The inferred numerical assessment levels (one per identified key body pose and involved body joint) are translated into human-understandable language via a highly-customisable, context-free grammar. Finally, visual feedback is produced in the form of a synchronised skeletal animation of both the user's performance and the tutor's. If the user's performance is well below a standard then an affine offset transformation of the skeletal motion data series to an in-between performance is performed, in order to prevent dis-encouragement from the user and still provide a reference for improvement. At the end of this thesis, a study of the limitations of the methods in real circumstances is explored. Issues like the gimbal lock in the angular motion data, lack of accuracy of the motion capture system and the escalation of the training set are discussed. Finally, some conclusions are drawn and future work is discussed

Modèle ontologique contextuel pour les patients atteints de la maladie pulmonaire obstructive chronique

L'informatique ubiquitaire est considérée comme l'une des réalisations scientifiques les plus marquantes de la dernière décennie. Cette vision a créé une révolution dans les interactions des utilisateurs finaux à partir le concept de sensibilité au contexte. L'informatique ubiquitaire offre une nouvelle opportunité pour remodeler la forme des solutions conventionnelles en fournissant des services personnalisés en fonction des situations contextuelles de chaque environnement. Des centaines d'architectures théoriques ont été développées dans le but de mettre en oeuvre l'idée de systèmes sensible au contexte. Cependant, l'informatique ubiquitaire est encore pratiquement non applicable en raison de nombreux défis, surtout que les architectures proposées se présentent toujours comme une solution générale qui permet de satisfaire n'importe quel type d'application et toutes sortes d'utilisation. OBJECTIFS: Cette thèse vise à concevoir et valider un modèle contextuel pour les systèmes de soins de santé ubiquitaires et spécifiquement destinés à aider les patients souffrant de la maladie pulmonaire obstructive chronique (MPOC). LA MÉTHODE: Les informations contextuelles sont très importantes pour les applications de soins de santé sensibles au contexte, en particulier celles utilisées pour surveiller les patients atteints de maladies chroniques qui sont affectées par des conditions concevables. Dans cette thèse, nous proposons une nouvelle classification de contexte pour le domaine médical qui couvre tous les aspects influençant la santé des patients. La grande échelle de cette classification le rend apte pour être une référence générale pour de divers projets de recherche s'intéressant au contexte médical. Ensuite, nous proposons un modèle contextuel à base d’ontologies capable de gérer la structure complexe du domaine de la MPOC de manière cohérente, en proportion de la nature dynamique de cet environnement. Ce nouveau modèle ontologique constitue le noyau de notre perception pour la mise en oeuvre de la solution de soins de santé ubiquitaire. Le modèle présenté examine son efficacité dans la gestion de l’une des maladies les plus vulnérables au contexte, où il prouve ainsi sa capacité à adapter les services de soins de santé à titre personnel et en fonction des conditions actuelles et prévues. Le modèle proposé a montré des résultats prometteurs dépassant 85% approuvé par un groupe de spécialistes expérimentés dans le domaine des maladies pulmonaires. Ubiquitous computing is considered one of the most impactful scientific achievements in the last decade. This conception created tremendous revolution in the end-user interactions through the concept of context-awareness. Ubiquitous computing offers a new opportunity to redesign the pattern of conventional solutions where it can easily tailor its processes upon existing contextual situations. Hundreds of theoretical architectures have been developed to enable context-awareness computing in pervasive settings. However, ubiquitous computing is still practically not feasible due to many challenges, but most importantly, that the proposed models always present themselves as a general solution to all kinds of real-life applications. OBJECTIVES: This thesis aims to design and validate a contextual model for health-care context-aware systems to support patients suffer from Chronic Obstructive Pulmonary Disease (COPD). METHODS: The contextual information is important for developing Context-Aware Healthcare Applications, especially those used to monitor patients with chronic diseases which are affected by perceived conditions. In this thesis, we propose a novel context categorization within the medical domain which covers all the context aspects. Then, we propose an ontology-based model able to handle the complex contextual structure of the COPD domain coherently, and in proportion to the dynamic nature of that environment. This new ontological context is the core of our perception for implementing the ubiquitous healthcare solution. The presented model examines its effectiveness in managing one of the most context-sensitive diseases, thereby demonstrating its ability to adapt health care services on a personal basis and in accordance with current and projected events. The proposed model has shown promising results exceeding 85% approved by a group of experienced specialists in respiratory and lung diseases

Ontology-Based Generation of Dynamic Feedback on Physical Activity

This article describes a new, AI-inspired telemedicine system for providing feedback on daily activity. Optimising daily levels of physical activity is an important focus in the treatment of chronic illnesses. An ambulatory monitoring and feedback system has been developed to monitor activity and provide feedback to help patients reach a healthy daily pattern. The system has shown positive effects in trials on different patient groups including COPD and obese patients. We describe the design and implementation of an intelligent feedback generation module that improves interaction with the patient by providing personalised dynamic context-aware feedback. An ontology of messages was designed, which the system uses to find appropriate feedback using context information to prune irrelevant paths. The system adapts based on derived probabilities concerning user preferences to certain message types. We aim to improve patient compliance to individual feedback messages and improve the user experience, leading to better overall treatment compliance