El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In this paper, we proposed a wearable technology model to control and monitor hypertension during pregnancy. We enhanced prior models by adding a series of health parameters that could potentially prevent and correct hypertension disorders in pregnancy. Our proposed model also emphasizes the application of real-time data analysis for the healthcare organization. In this process, we also assessed the current technologies and systems applications offered in the market. The model consists of four phases: 1. The health parameters of the patient are collected through a wearable device; 2. The data is received by a mobile application; 3. The data is stored in a cloud database; 4. The data is analyzed on real-time using a data analytics application. The model was validated and piloted in a public hospital in Lima, Peru. The preliminary results showed an increased-on number of controlled patients by 11% and a reduction of maternal deaths by 7%, among other relevant health factors that allowed healthcare providers to take corrective and preventive actions. / Revisión por pares

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In this article, we propose a wireless monitoring solution for gait parameters using low-cost sensors in the physical rehabilitation of patients with gait disorders. This solution consists of infrared speed sensors (IRSS), force-sensing Resistor (FSR) and microcontrollers placed in a walker. These sensors collect the pressure distribution on the walker's handle and the speed of the steps during therapy session. The proposal allows to improve the traditional physiotherapy session times through a mobile application to perform the monitoring controlled by a health specialist in real time. The proposed solution consists of 4 stages: 1. Obtaining gear parameters, 2. Data transmission, 3. Information Storage and 4. Data collection and processing. Solution was tested with 10 patients from a physical rehabilitation center in Lima, Peru. Preliminary results revealed a significant reduction in the rehabilitation session from 25 to 5.2 minutes. / Revisión por pares

BACKGROUND: Polycystic Ovary Syndrome (PCOS) is an endocrine disrupting disorder affecting at least 10 percent of reproductive-aged women. In many cases, women develop comorbidities such as diabetes, cardiovascular disease, and other metabolic disorders. In North America and Europe, it takes several years and multiple doctors for women to receive a diagnosis of PCOS. This results in lost time for risk-reducing interventions. Menstrual tracking applications are one potential tool to alert women of their risk for PCOS and prompt them to seek further evaluation from a medical professional. OBJECTIVE: The objective was to develop the Irregular Cycles Feature (ICF) on the mobile phone application Clue®, which generates a probability of a user’s risk for PCOS. The secondary aim was to assess the accuracy of the tool by testing the feature on virtual test subjects. METHODS: A literature review was conducted to generate a list of signs and symptoms of PCOS. Probabilities were assigned to each variable and built into a Bayesian Network. The Irregular Cycles Feature, an adaptive questionnaire, was then developed in order to detect high-risk PCOS patients. The ICF detected at risk Clue® users through self-reported menstrual cycles and answers to medical history questions. Upon completion of the questionnaire, a Result Screen is displayed to the user. The Screen is a summary of the individual’s probability of having PCOS. For each eligible user, a Doctor’s Report is also generated. This is a screen containing information regarding menstrual irregularities and a brief medical history to be used by a medical professional in order to make a final diagnosis. Both the Result Screen and Doctor’s Report disclose information about PCOS and detailed explanations for consulting a medical provider. A brief statistical validation was then performed to compare the output of the network to predictions made by a physician-scientist using a correlation coefficient, a p-value, and a Pearson’s coefficient. RESULTS: The Irregular Cycles Feature successfully predicts probability of PCOS based on eight test cases. The correlation between the network’s calculation and the assessment made by a board-certified reproductive endocrinology/infertility physician-scientist was 0.82, with a p-value of less than 0.05. The Pearson’s coefficient was calculated to be 0.69. These values indicate that the ICF made statistically significant predictions when compared to the physician-scientist. CONCLUSIONS: The ICF provides consumer-friendly ways to improve interactions between medical providers and patients. The tool can be adapted to capture other causes of menstrual irregularities and can serve as an important mechanism for drawing attention to potentially hazardous health problems. Further validation studies will be conducted to confirm the utility of the ICF with Clue® users, particularly amongst those who receive an official diagnosis from a medical professional. / 2020-06-17T00:00:00Z

Distributed cloud computing, when integrated with smartphone capabilities, contribute to building an efficient multimedia e-health application for mobile devices. Unfortunately, mobile devices alone do not possess the ability to run complex machine learning algorithms, which require large amounts of graphic processing and computational power. Therefore, offloading the computationally intensive part to the cloud, reduces the overhead on the mobile device. In this thesis, we introduce two such distributed cloud computing models, which implement machine learning algorithms in the cloud in parallel, thereby achieving higher accuracy. The first model is based on MapReduce SVM, wherein, through the use of Hadoop, the system distributes the data and processes it across resizable Amazon EC2 instances. Hadoop uses a distributed processing architecture called MapReduce, in which a task is mapped to a set of servers for processing and the results are then reduced back to a single set. In the second method, we implement cloud virtualization, wherein we are able to run our mobile application in the cloud using an Android x86 image. We describe a cloud-based virtualization mechanism for multimedia-assisted mobile food recognition, which allow users to control their virtual smartphone operations through a dedicated client application installed on their smartphone. The application continues to be processed on the virtual mobile image even if the user is disconnected for some reason. Using these two distributed cloud computing models, we were able to achieve higher accuracy and reduced timings for the overall execution of machine learning algorithms and calorie measurement methodologies, when implemented on the mobile device.

High calorie intake has proved harmful worldwide, as it has led to many diseases. However, dieticians have deemed that a standard intake of number of calories is essential to maintain the right balance of calorie content in human body. In this thesis, we consider the category of tools that use image processing to recognize single and multiple mixed-food objects, namely Deep Learning and the Support Vector Machine (SVM). We propose a method for the fully automatic and user-friendly calibration of the sizes of food portions. This calibration is required to estimate the total number of calories in food portions. In this work, to compute the number of calories in the food object, we go beyond the finger-based calorie calibration method that has been used in the past, by automatically measuring the distance between the user and the food object. We implement a block resize method that uses the measured distance values along with the recognized food object name to further estimate calories. While measuring distance, the system also assists the user in real time to capture an image that enables the quick and accurate calculation of the number of calories in the food object. The experimental results showed that our method, which uses deep learning to analyze food objects, led to an improvement of 16.58% in terms of recognition, over the SVM-based method. Moreover, the block resize method showed that percentage error for calorie estimation was reduced to 3.64% as compared to 5% achieved in previous methods.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In this article, a technological solution is proposed to identify and reduce the level of mental stress of a person through a wearable device. The proposal identifies a physiological variable: Heart rate, through the integration between a wearable and a mobile application through text recognition using the back camera of a smartphone. As part of the process, the technological solution shows a list of guidelines depending on the level of stress obtained in a given time. Once completed, it can be measured again in order to confirm the evolution of your stress level. This proposal allows the patient to keep his stress level under control in an effective and accessible way in real time. The proposal consists of four phases: 1. Collection of parameters through the wearable; 2. Data reception by the mobile application; 3. Data storage in a cloud environment and 4. Data collection and processing; this last phase is divided into 4 sub-phases: 4.1. Stress level analysis, 4.2. Recommendations to decrease the level obtained, 4.3. Comparison between measurements and 4.4. Measurement history per day. The proposal was validated in a workplace with people from 20 to 35 years old located in Lima, Peru. Preliminary results showed that 80% of patients managed to reduce their stress level with the proposed solution. / Revisión por pares

Background: High-fidelity simulation recreates real-life situations in a safe learning environment and encourages critical thinking in students. Published research in simulation in psychiatric/mental health nursing is sparse. Methods: Four scenarios exemplifying drug or alcohol abuse utilizing the computerized, mannequin SimMan® were implemented. Students evaluated their learning experience following completion of the simulation via a 20-item, Likert-scale survey which included open-ended questions. Results: Results were positive. Students rated all items on the survey as “agree” or “strongly agree” (Mean 4.77, SD=0.55). Conclusions: High fidelity clinical education simulations are an effective means of facilitating student learning of psychiatric and mental health clinical experiences. Students found simulation to be a useful and engaging means by which to learn to care for clients with drug or alcohol abuse disorders.

No description available.

The importance that physical movement holds for people’s health is not up for debate. Staying active can alleviate a lot of symptoms, both physical and mental, that occur in both young and old, due to a sedentary lifestyle or to aging. Because of the way the society is developing, more and more people suffer from a sedentary lifestyle. Among all age groups, the older people have an especially high risk for negative side effects of too much sedentary time. The World Health Organization encourages countries to help combat the sedentary lifestyles and motivate people to exercise more. In their efforts to make sure people across countries have access to health information they also encourage the development of digital health tools and the use of eHealth, which falls under the umbrella term digital health. To create the lifestyle changes needed to become and stay more physically active there can be a need for motivational factors such as triggers, reminders and other motivators. Persuasive design in general and nudges in particular have the function where they will nudge people towards making a behavior change without removing the autonomy of the person. As the older people in Sweden are generally both digital and eHealth literate and are able to handle health related matters such as booking appointments and reading their health journal online, we see a possibility to examine the area of nudging the older towards a behavior change that leads to increased physical activity. Thus, in this study we examine to what extent digital nudges impact physical activities and related conditions, we chose a within subject design experiment which had two conditions – with and without a mobile application that offers digital nudges during two weeks of time period. In our study the test subjects are people above 65 years old as this group of people would greatly benefit from more physical activity and the study of how digital nudges would affect the older population in Sweden is an understudied area. The participants in one of the groups were nudged, using nudges such as the position effects, social norm and framing effects. A dependent t test was conducted and depending on the alpha level used, there was a statistical significance shown. Overall the amount of physical activity in older adults having a prototype with nudges was significantly higher. The results for this study are relevant to some degree since this is not something that has been studied before.

A study investigating how gamification features can be designed within a gym application, how they affect the user’s situated motivation and user engagement, and how these features could increase user loyalty to the product.