This paper presents an intelligent system designed to increase the treatment adherence of hypertensive patients. The architecture was developed to allow communication among patients, physicians, and families to determine each patient’s medication intake and self-monitoring of blood pressure rates. Concerning the medication schedule, the system is designed to follow a predefined prescription, adapting itself to undesired events, such as mistakenly taking medication or forgetting to take medication on time. When covering the blood pressure measurement, it incorporates best medical practices, registering the actual values in recommended frequency and form, trying to avoid the known “white-coat effect.” We assume that taking medicine precisely and measuring blood pressure correctly may lead to good adherence to the treatment. The system uses commercial consumer electronic devices and can be replicated in any home equipped with a standard personal computer and Internet access. The resulting architecture has four layers. The first is responsible for adding electronic devices that typically exist in today’s homes to the system. The second is a preprocessing layer that filters the data generated from the patient’s behavior. The third is a reasoning layer that decides how to act based on the patient’s activities observed. Finally, the fourth layer creates messages that should drive the reactions of all involved actors. The reasoning layer takes into consideration the patient’s schedule and medication-taking activity data and uses implicit algorithms based on the J48, RepTree, and RandomTree decision tree models to infer the adherence. The algorithms were first adjusted using one academic machine learning and data mining tool. The system communicates with users through smartphones (anytime and anywhere) and smart TVs (in the patient’s home) by using the 3G/4G and WiFi infrastructure. It interacts automatically through social networks with doctors and relatives when changes or mistakes in medication intake and blood pressure mean values are detected. By associating the blood pressure data with the history of medication intake, our system can indicate the treatment adherence and help patients to achieve better treatment results. Comparisons with similar research were made, highlighting our findings.

This paper presents an indoor navigation system built in a wearable device. The system allows visually impaired users to perform guided audio navigation safe, fast and has a low computational complexity. The methodological approach chosen divides the process into two phases: offline and online. In offline phase, the indoor mapping is done by data fusing of radio frequency and visual markers, constructing a unique and consistent representation. In the online phase navigation and recognition of each of the internal positions are performed through the fusion representation or only by the wi-fi or visual signals when one of the sensors is strongly affected by noises or other interferences. The results showed that the recognition levels of wi-fi, visual and fusion markers were 87.59%, 90.92%, and 92.03%, respectively. The error margin after the data fusion application was 0.8 m, with an average time of 0.62 ms.

One of the biggest problems with chronical patients in treatment is medication adherence. Studies indicate that taking drugs out of time influence the patient's treatment decreasing the drugs effect. To minimize this problem, we developed a ubiquitous and intelligent system able to monitor the taking of medicines and to identify whether the patient is meeting the requirements prescribed by the doctor. An architecture provided with a decision system based on rules and trees to evaluate data collected from an intelligent medicine cabinet, sensors and electronic devices available in the patient's home was designed. The system classified the drugs taken pattern and released messages on social networks, SMS, and consumer electronic devices such as TV, smartphone and tablets, without human interference. Its goal is to help keeping the medication on time and helping to decide what to do in case of missing the right time. The algorithms J48, Rep and Random tree, were tested to classify the taking medicine patterns and to chose the right services available. The obtained results are very promising and reached an acceptable accuracy rate.

As Brazil's interactive Digital TV System (iDTV) is getting impulse, with a strong belief of government and industry that this new system will bring together new economic opportunities, several proposals for extending the potential of TV interactivity to provide innovative services have being proposed nowadays. In this context, the Healthcare is one of the concepts being studied. Healthcare relates to caring about health, preventing diseases, improving the quality o life, and its applications may support hospitals, elderly care centers and similar institutions. Integrating Healthcare technology with home devices is a new trend. Indeed this work aims to investigate new ways for communicating medical devices with the Brazilian iDTV. The solution here proposed is a software platform based on the so called home gateway, which is in charge of interconnecting all medical devices with the iDTV set. As proof of concept two implementation scenarios for measuring the heart beat frequency and the blood pressure were built. All the procedures related to the integration are detailed. We believe this work will be useful in this new field, bringing alternative ways for using common devices and the iDTV to improve the quality of life to the home user.

This article presents a system for improving adherence to medication in patients with hypertension. An architecture for collecting and processing data from an intelligent drug cabinet was developed using a layer of machine learning algorithms. The system implements a decision tree model through a set of algorithms that use the voting strategy to choose the adherence classifier. The result was a system built for mobile devices and Web that has a good evaluation in detecting adherence or non-adherence of the patient to prescribed medication.

Ihe Brazilian Digital IV System, with its middleware specification, has great potential to be used to create modern applications beyond the entertainment. In this paper, we present a new approach to integrate the iDTV technology with medical devices by means of software systems running into one residential gateway and mobile devices. Conventional medical devices are used to collect physiological information. We investigate and propose new integration forms of those different devices, e.g., temperature sensor, blood pressure meter, and pulse oximeter with conventional consumer electronic devices. This proposed system is tested using two usage scenarios. A conventional blood pressure meter and a heartbeat meter are used to collect and send the data being processed. The digital TV plays an important role in the final healthcare system, acting as a ubiquitous interface between the system and the costumer. The final platform provides alternatives to use medical devices together with consumer electronics to improve the life quality of its users.