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

Context-aware approach for cardiac rehabilitation monitoring

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. Existing technologies for cardiac patient monitoring are generally based on the physiological information, mostly the heart rate or Electrocardiogram(ECG) Signals. Other important factors such as physical activities and time of the day are usually ignored. We propose a context-aware solution for cardiac rehabilitation monitoring using multiple vital signs from the physiological and activity data of the patient. This research considers the activity of the patient alongside the time of the activity to facilitate physicians decision-making process. We provide a personalised physical activity recognition processing by generating a personalised model for each user. A prototype is presented to illustrate our proposed approach

Would Exchange Rate Converge in Nigeria?A Stochastic-Markov Transition Process Analysis

This paper examined if the Nigerian exchange rate would converge in the long run thereby looking at the exchange rate switches or transition from a particular state to another. This was done via the iterations of the Chapman-Kolmogorov equations of the Markov model, It was discovered that convergence occurred in the long run as shown by our markov model. It suggests that appreciation and depreciation of the naira via dollar rate would be stable as indicated by the probability values. Keywords: Markov, Transition probabilities, Exchange rate, Chapman-Kolmogoro

Privacy preserving context-aware framework for cardiac health monitoring

The impact of digital technology on healthcare delivery services is increasing as new technologies evolve and current technologies expand. These technologies have the potential to provide a platform to reason about the health condition of a patient using relevant contextual information. Context-aware reasoning is particularly important in cardiac health monitoring because of the increasing number of deaths resulting from cardiac diseases. As a result, several efforts have been made to develop intelligent systems for cardiac condition monitor-ing. Nevertheless, most of the existing systems for cardiac health monitoring are generally based on physiological information, mainly the heart rate or electrocardiogram(ECG) signals, while the few research that does integrate contextual information has not considered the privacy of the patients in the development process. This research proposes a privacy-preserving context-aware framework for cardiac health monitoring using contextual information from the patient’s behavior data to facilitate physicians’ decision-making. The framework considers patient’s privacy by allowing the user to take control of the data generated from the sensors as information is stored in the user’s device and not transferred to any server. Furthermore, the user’s pri-vacy is also considered at the algorithm training and model generation stage by adopting a federated machine learning technique. Using federated learning for model development which is a key contribution of this research aims to maintain user privacy by allowing clients from different locations to collabo-ratively learn a machine learning model without sending datasets to a central server. In addition, the framework addresses the issue of context acquisition by engaging healthcare professionals in the development process. A prototype tagged ”mCardiac” is presented as a proof of concept. The design, implemen-tation, and evaluation of mCardiac was made possible by constant interaction with healthcare professionals. mCardiac was also evaluated with cardiac pa-tients who were asked to use the system to validate the effectiveness of the approach