Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Data-Driven Audiogram Classification for Mobile Audiometry

Recent mobile and automated audiometry technologies have allowed for the democratization of hearing healthcare and enables non-experts to deliver hearing tests. The problem remains that a large number of such users are not trained to interpret audiograms. In this work, we outline the development of a data-driven audiogram classification system designed specifically for the purpose of concisely describing audiograms. More specifically, we present how a training dataset was assembled and the development of the classification system leveraging supervised learning techniques. We show that three practicing audiologists had high intra- and inter-rater agreement over audiogram classification tasks pertaining to audiogram configuration, symmetry and severity. The system proposed here achieves a performance comparable to the state of the art, but is signific

The reality of home remote patient monitoring: A thesis on the nature, dynamics and effects of telehealth

Chronic diseases are very complex conditions in which two or m ore disease processes may coexist. Managing patients with chronic conditions represents a major challenge to public health, since eighty per cent of GP consultations are related to these groups. Further more, five per cent of them represent roughly forty two per cent of the total acute day bed occupancy. Nowadays, the tendency is to bring patients from high levels of care to primary care and home settings. In this scenario, telehealth is one of the methods that can be used to improve and provide access to remote patient monitoring at home. This is important for patients with chronic diseases as it can help to recognise any signs of deterioration, provide health education and support management of their condition. Potentially, telehealth can reduce deaths, emergency visits, A&E visits, elective admissions, bed stays and costs. This can be done by designing and implementing telehealth interventions that help reduce unnecessary referrals to specialised services and monitor patients remotely at home. However, there is not a unique framework for designing telehealth interventions. In order to design a robust, accurate and reliable telehealth intervention, a number of important factors have to be taken into account. A review of three systematic reviews, and an in-depth investigation of a case study based on an EU funded project, were used to gather the evidence for telehealth interventions. These were used to identify the main components of the framework proposed. The investigation focused primarily on home monitoring for patients with diabetes, asthma, hypertension and COPD. We concluded that factors such as usability, safe intervention, patients’ individual requirements, training, safe interventions, ease of use, data integration, development of procedures and allocation of appropriate staff are important components that need to be considered in order to satisfy a robust framework

Bio-signal data gathering, management and analysis within a patient-centred health care context

The healthcare service is under pressure to do more with less, and changing the way the service is modelled could be the key to saving resources and increasing efficacy. This change could be possible using patient-centric care models. This model would include straightforward and easy-to-use telemonitoring devices and a flexible data management structure. The structure would maintain its state by ingesting many sources of data, then tracking this data through cleaning and processing into models and estimates to obtaining values from data which could be used by the patient. The system can become less disease-focused and more health-focused by being preventative in nature and allowing patients to be more proactive and involved in their care by automating the data management. This work presents the development of a new device and a data management and analysis system to utilise the data from this device and support data processing along with two examples of its use. These are signal quality and blood pressure estimation. This system could aid in the creation of patient-centric telecare systems

Eldo-care: EEG with Kinect sensor based telehealthcare for the disabled and the elderly

Telehealthcare systems are nowadays becoming a massive daily helping kit for elderly and disabled people. By using the Kinect sensors, remote monitoring has become easy. Also, the sensors' data are useful for the further improvement of the device. In this paper, we have discussed our newly developed “Eldo-care” system. This system is designed for the assessment and management of diverse neurological illnesses. The telemedical system is developed to monitor the psycho-neurological condition. People with disabilities and the elderly frequently experience access issues to essential services. Researchers today are concentrating on rehabilitative technologies based on human-computer interfaces that are closer to social-emotional intelligence. The goal of the study is to help old and disabled persons with cognitive rehabilitation using machine learning techniques. Human brain activity is observed using electroencephalograms, while user movement is tracked using Kinect sensors. Chebyshev filter is used for feature extraction and noise reduction. Utilizing the autoencoder technique, categorization is carried out by a Convolutional neural network with an accuracy of 95% and higher based on transfer learning. A better quality of life for older and disabled persons will be attained through the application of the suggested system in real time. The proposed device is attached to the subject under monitoring

Comparison and evaluation of the Telehealth systems using a discrete event simulation

The telehealth is the delivery of health-related services at a distance using communication technologies. The telehealth provides important benefits: allows to provide the access to the healthcare service reducing need for a physical stay and decreasing the healthcare cost. This industry’s popularity and the importance constantly increases because of the number of rapid increase of the population share a ected by the chronic diseases. However, the telehealth service development requires substantial investment of finances, time and substantial expertise. A method of comparison and evaluation of the current telehealth systems helps to create the telehealth service while minimising an amount of the resources wasted. The method to simulate, evaluate, and compare the telehealth systems have been created through the review of the state-of-the-art techniques of evaluation and comparison of the existing telehealth systems. Then, it was applied to several the real life telehealth systems. The outcome of this work is (i) the method to construct Discrete Event Simulation (DES) models of the telehealth systems, (ii) twelve the DES models of the current telehealth systems, (iii) the list of suggestions for the future research to increase the quality of the DES models, (iv) the method to choose parameters and to develop metrics to evaluate the telehealth systems, (v) the method to define an approach to compare the telehealth systems, (vi) the evaluation and comparison results of twelve the current telehealth systems. The bottom line of the current research is that the simulation is an e ective way to evaluate and compare the telehealth systems. The DES approach is a viable way of gaining an insight into the telehealth systems properties, although it requires a substantial amount of the future research to mature the method to evaluate and compare the telehealth systems