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

Wearable feedback systems for rehabilitation

In this paper we describe LiveNet, a flexible wearable platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification. Based on the MIT Wearable Computing Group's distributed mobile system architecture, LiveNet is a stable, accessible system that combines inexpensive, commodity hardware; a flexible sensor/peripheral interconnection bus; and a powerful, light-weight distributed sensing, classification, and inter-process communications software architecture to facilitate the development of distributed real-time multi-modal and context-aware applications. LiveNet is able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. We demonstrate the power and functionality of this platform by describing a number of health monitoring applications using the LiveNet system in a variety of clinical studies that are underway. Initial evaluations of these pilot experiments demonstrate the potential of using the LiveNet system for real-world applications in rehabilitation medicine

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

Patient monitoring under an ambient intelligence setting

Springer - Series Advances in Intelligent and Soft Computing, vol. 72In recent years there has been a growing interest in developing Ambient Intelligence based systems in order to create smart environments for user and environmental monitoring. In fact, higher-level monitoring systems with vital information about the user and the environment around him/her represents an improvement of the quality of care provided. In this paper, we propose an architecture that implements a multi-agent user-profile based system for patient monitoring aimed to improve the assistance and health care provided. This system mixes logical based reasoning mechanisms with context-aware technologies. It is also presented a case based on a scenario developed at a major Portuguese healthcare institution

FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning

Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via mobile devices. We propose FedTherapist, a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning. We explore multiple model designs by comparing their performance and overhead for FedTherapist to overcome the complex nature of on-device language model training on smartphones. We further propose a Context-Aware Language Learning (CALL) methodology to effectively utilize smartphones' large and noisy text for mental health signal sensing. Our IRB-approved evaluation of the prediction of self-reported depression, stress, anxiety, and mood from 46 participants shows higher accuracy of FedTherapist compared with the performance with non-language features, achieving 0.15 AUROC improvement and 8.21% MAE reduction.Comment: Accepted to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023

Fuzzy Logic-Based Health Monitoring System for COVID’19 Patients

In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID’19 patients and quarantined people. It identifies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model. Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identification. Linguistics rules are framed based on the fuzzy set attributes belong to different context types. The fuzzy semantic rules are used to identify the relationship among the attributes, and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation. Outcomes are measured using a fuzzy logic-based context reasoning system under simulation. The results indicate the usefulness of monitoring the COVID’19 patients based on the current context

Context-aware QoS provisioning for an M-health service platform

Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e., m-health) services allow healthcare professionals to monitor mobile patient's vital signs and provide feedback to this patient anywhere at any time. Due to the nature of current supporting mobile service platforms, m-health services are delivered with a best-effort, i.e., there are no guarantees on the delivered Quality of Service (QoS). In this paper, we argue that the use of context information in an m-health service platform improves the delivered QoS. We give a first attempt to merge context information with a QoS-aware mobile service platform in the m-health services domain. We illustrate this with an epilepsy tele-monitoring scenario

Use of location data for the surveillance, analysis, and optimization of clinical processes

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Includes bibliographical references (leaves 33-35).Location tracking systems in healthcare produce a wealth of data applicable across many aspects of care and management. However, since dedicated location tracking systems, such as the oft mentioned RFID tracking system, are still sparsely deployed, a number of other data sources may be utilized to serve as a proxy for physical location, such as barcodes and manual timestamp entry, and may be better suited to indicate progress through clinical workflows. INCOMING!, a web-based platform that monitors and tracks patient progress from the operating room to the post-anesthesia care unit (PACU), is one such system that utilizes manual timestamps routinely entered as standard process of care in the operating room in order to track a patient's progress through the post-operative period. This integrated real time system facilitates patient flow between the PACU and the surgical ward and eases PACU workload by reducing the effort of discharging patients.(cont.) We have also developed a larger-scale integrated system for perioperative processes that integrates perioperative data from anesthesia and surgical devices and operating room (OR) / hospital information systems, and projects the real-time integrated data as a single, unified, easy to visualize display. The need to optimize perioperative throughput creates a demand for integration of the datastreams and for timely data presentation. The system provides improved context-sensitive information display, improved real-time monitoring of physiological data, real-time access to readiness information, and improved workflow management. These systems provide improved data access and utilization, providing context-aware applications in healthcare that are aware of a user's location, environment, needs, and goals.by Mark A. Meyer.S.M