Automatic detection of falls and fainting

Healthcare environments have always been considered an important scenario in which to apply new technologies to improve residents and employees conditions, solve problems and facilitate the performance of tasks. In this way, the use of sensors based on user movement interaction allows solving complicated situations that should be immediately addressed, such as controlling falls and fainting spells in residential care homes. However, ensuring that all the residents are always visually controlled by at least one employee is quite complicated. In this paper, we present a ubiquitous and context-aware system focused on geriatrics and residential care homes, but it could be applied to any other healthcare centre. This system has been designed to automatically detect falls and fainting spells, alerting the most appropriate employees to address the emergency. To that end, the system is based on movement interaction through a set of Kinect devices that allows the identification of the position of a person. These devices imply some development problems that authors have had to deal with, including camera location, the detection of head movements and people in horizontal position. The proposed system allows controlling each resident posture through a notification and warning procedure. When an anomalous situation is detected, the system analyses the resident posture and, if necessary, the most adequate employee will be warned to react urgently. Ubiquity and context-awareness are essential features since the proposed system has to be able to know where any employee is and what they are doing at any time. Finally, we present the outcomes of an evaluation based on the ISO 9126-4 about the usability of the system.We would like to acknowledge the project CICYT TIN2011-27767-C02-01 from the Spanish Ministerio de Ciencia e Innovación and the Regional Goverment: Junta de Comunidades de Castilla-La Mancha PPII10-0300-4174 and PII2C09-0185-1030 projects for partially funding this work

Context-aware support for cardiac health monitoring using federated machine learning

Context-awareness provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters such as heart rate, electrocardiogram (ECG) signals and activity data. It involves the use of digital technologies to monitor the health condition of a patient in an intelligent environment. Feedback gathered from relevant professionals at earlier stages of the project indicates that physical activity recognition is an essential part of cardiac condition monitoring. However, the traditional machine learning method f developing a model for activity recognition suffers two significant challenges; model overfitting and privacy infringements. This research proposes an intelligent and privacy-oriented context-aware decision support system for cardiac health monitoring using the physiological and the activity data of the patient. The system makes use of a federated machine learning approach to develop a model for physical activity recognition. Experimental analysis shows that the federated approach has advantages over the centralized approach in terms of model generalization whilst maintaining the privacy of the user

Context-aware system for cardiac condition monitoring and management: a survey

Health monitoring assists physicians in the decision-making process, which in turn, improves quality of life. As technology advances, the usage and applications of context-aware systems continue to spread across different areas in patient monitoring and disease management. It provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters. In this survey, we consider context-aware systems proposed by researchers for health monitoring and management. More specifically, we investigate different technologies and techniques used for cardiac condition monitoring and management. This paper also propose "mCardiac", an enhanced context-aware decision support system for cardiac condition monitoring and management during rehabilitation

Mobile Clinical Decision Support Systems – A Systematic Review

In this review article, we provide a descriptive analysis of the current state of mobile decision support systems in the healthcare domain based on studies published in the following databases: Business Source Complete, CINAHL, Cochrane library, MEDLINE, PsycINFO, PubMed, ScienceDirect and Web of Science databases. A total of 29 studies were identified and analyzed to understand the current state of development, evaluation efforts, usability and challenges to adoption by patients and care providers. Our aim is to evaluate these systems and identify the key challenges which hinders their widespread adoption. Although, mobile based decision support systems in healthcare context have the potential to improve clinical decision making, the current state with low adoption rate and early stage of development need to be addressed for successful health outcomes

ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

The creation of sensors allowing the collection of a high amount of data has been possible thanks to the evolution of information and communication technology. These data must be properly interpreted to deliver meaningful information and services. Context-aware reasoning plays an important role in this task, and it is considered as a hot topic to study in the development of solutions that can be categorised under the scope of Intelligent Environments. This research work studies the use of context-aware reasoning as a tool to provide support in the asthma management process. The contribution of this study is presented as the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), which can be used as a guideline to create solutions supporting asthma management in a personalised way. ADAPT proposes context-aware reasoning as an appropriate tool to achieve the personalisation that is required to address the heterogeneity of asthma. This heterogeneity makes people with asthma have different triggers provoking their exacerbations and to experience different symptoms when their exacerbations occur, which is considered as the most challenging characteristic of the condition when it comes to implementing asthma treatments. ADAPT context dimensions are the main contribution of the research work as they directly address the heterogeneity of asthma management by allowing the development of preventive and reactive features that can be customised depending on the characteristics of a person with asthma. The approach also provides support to people not knowing their triggers properly through case-based reasoning, and includes virtual assistant as a complementing technology supporting asthma management. The comprehensive nature of ADAPT motivates the study of the interaction between context-aware reasoning and case-based reasoning in Intelligent Environments, which is also reported as a key contribution of the research work. The inclusion of people with asthma, carers and experts in respiratory conditions in the experiments of the research project was possible to achieve thanks to the collaboration formed with Asthma UK

Exploring the need for a suitable privacy framework for mHealth when managing chronic diseases

The widespread rises in chronic illnesses (e.g., diabetes and high blood pressure) have resulted in the need to find more efficient ways of managing patients with these conditions. One such way is by the use of mobile health (mHealth) technologies that can gather real-time data from patients and monitor them from a distance, removing the need to be at a medical facility. These technologies can be an integral part of intelligent healthcare environments (e.g., smart homes to monitor and assist elderly patients) which are essential to reducing healthcare costs and improving efficiency. The use of mHealth, however, brings various privacy concerns and challenges. This paper reviews and examines the challenges of preserving user privacy in the context of using mHealth to manage chronic diseases. The paper first discusses mHealth, its importance in managing chronic diseases, and the associated privacy concerns. Second, the paper compares the existing privacy frameworks applicable to mHealth. Third, the key principles gathered from the frameworks are analysed in the context of their suitability for enabling adequate privacy when using mHealth for managing chronic diseases. Finally, the paper argues that a new privacy framework is needed for mHealth in the context of managing chronic diseases

Designing a mHealth clinical decision support system for Parkinson's disease: a theoretically grounded user needs approach.

BACKGROUND: Despite the established evidence and theoretical advances explaining human judgments under uncertainty, developments of mobile health (mHealth) Clinical Decision Support Systems (CDSS) have not explicitly applied the psychology of decision making to the study of user needs. We report on a user needs approach to develop a prototype of a mHealth CDSS for Parkinson's disease (PD), which is theoretically grounded in the psychological literature about expert decision making and judgement under uncertainty. METHODS: A suite of user needs studies was conducted in 4 European countries (Greece, Italy, Slovenia, the UK) prior to the development of PD_Manager, a mHealth-based CDSS designed for Parkinson's disease, using wireless technology. Study 1 undertook Hierarchical Task Analysis (HTA) including elicitation of user needs, cognitive demands and perceived risks/benefits (ethical considerations) associated with the proposed CDSS, through structured interviews of prescribing clinicians (N = 47). Study 2 carried out computational modelling of prescribing clinicians' (N = 12) decision strategies based on social judgment theory. Study 3 was a vignette study of prescribing clinicians' (N = 18) willingness to change treatment based on either self-reported symptoms data, devices-generated symptoms data or combinations of both. RESULTS: Study 1 indicated that system development should move away from the traditional silos of 'motor' and 'non-motor' symptom evaluations and suggest that presenting data on symptoms according to goal-based domains would be the most beneficial approach, the most important being patients' overall Quality of Life (QoL). The computational modelling in Study 2 extrapolated different factor combinations when making judgements about different questions. Study 3 indicated that the clinicians were equally likely to change the care plan based on information about the change in the patient's condition from the patient's self-report and the wearable devices. CONCLUSIONS: Based on our approach, we could formulate the following principles of mHealth design: 1) enabling shared decision making between the clinician, patient and the carer; 2) flexibility that accounts for diagnostic and treatment variation among clinicians; 3) monitoring of information integration from multiple sources. Our approach highlighted the central importance of the patient-clinician relationship in clinical decision making and the relevance of theoretical as opposed to algorithm (technology)-based modelling of human judgment

ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

People with asthma have heterogeneous triggers and symptoms, which they need to be aware of in order to implement the strategies to manage their condition. Context-aware reasoning has the potential to provide the personalisation that is required to address the heterogeneity of asthma by helping people to define the information that is relevant considering the characteristics of their condition and delivering services based on this information. This research work proposes the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), whose aim is to facilitate the creation of solutions allowing the required customisation to address the heterogeneity of asthma. ADAPT is the result of the constant interaction with people affected by asthma throughout the research project, which was possible to achieve thanks to the collaboration formed with the Centre for Applied Research of Asthma UK. ADAPT context dimensions facilitate the development of preventive and reactive features that can be configured depending on the characteristics of the person with asthma. The approach also provides support to people not knowing their triggers through case-based reasoning and includes virtual assistant as a complementing technology supporting asthma management. ADAPT is validated by people with asthma, carers and experts in respiratory conditions, who evaluated a mobile application that was built based on the approach