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In mobile health care monitoring, compression is an essential tool for solving storage and transmission problems. The important issue is able to recover the original signal from the compressed signal. The main purpose of this paper is compressing the ECG signal with no loss of essential data and also encrypting the signal to keep it confidential from everyone, except for physicians. In this paper, mobile processors are used and there is no need for any computers to serve this purpose. After initial preprocessing such as removal of the baseline noise, Gaussian noise, peak detection and determination of heart rate, the ECG signal is compressed. In compression stage, after 3 steps of wavelet transform (db04), thresholding techniques are used. Then, Huffman coding with chaos for compression and encryption of the ECG signal are used. The compression rates of proposed algorithm is 97.72 %. Then, the ECG signals are sent to a telemedicine center to acquire specialist diagnosis by TCP/IP protocol. [ABSTRACT FROM AUTHOR]

Decision support systems (DSS) are increasingly demanded due that diagnosis is one of the main activities that physicians accomplish every day. This fact seems critical when primary care physicians deal with uncommon problems belonging to specialized areas. The main objective of this paper is the development and user evaluation of a mobile DSS for iOS named OphthalDSS. This app has as purpose helping in anterior segment ocular diseases' diagnosis, besides offering educative content about ophthalmic diseases to users. For the deployment of this work, firstly it has been used the Apple IDE, Xcode, to develop the OphthalDSS mobile application using Objective-C as programming language. The core of the decision support system implemented by OphthalDSS is a decision tree developed by expert ophthalmologists. In order to evaluate the Quality of Experience (QoE) of primary care physicians after having tried the OphthalDSS app, a written inquiry based on the Likert scale was used. A total of 50 physicians answered to it, after trying the app during 1 month in their medical consultation. OphthalDSS is capable of helping to make diagnoses of diseases related to the anterior segment of the eye. Other features of OphthalDSS are a guide of each disease and an educational section. A 70% of the physicians answered in the survey that OphthalDSS performs in the way that they expected, and a 95% assures their trust in the reliability of the clinical information. Moreover, a 75% of them think that the decision system has a proper performance. Most of the primary care physicians agree with that OphthalDSS does the function that they expected, it is a user-friendly and the contents and structure are adequate. We can conclude that OphthalDSS is a practical tool but physicians require extra content that makes it a really useful one. [ABSTRACT FROM AUTHOR]

In this paper, two mHealth applications are introduced, which can be employed as the terminals of bigdata based health service to collect information for electronic medical records (EMRs). The first one is a hybrid system for improving the user experience in the hyperbaric oxygen chamber by 3D stereoscopic virtual reality glasses and immersive perception. Several HMDs have been tested and compared. The second application is a voice interactive serious game as a likely solution for providing assistive rehabilitation tool for therapists. The recorder of the voice of patients could be analysed to evaluate the long-time rehabilitation results and further to predict the rehabilitation process. [ABSTRACT FROM AUTHOR]