Una contribución a la evaluación de la adherencia a hábitos de vida saludables basado en aplicaciones móviles

The adherence to a healthy lifestyle plays a key role for increasing life expectancy and living better. The main habits of healthy lifestyle are: physical activity, diet and sleep quality. Nowadays, many people use a smartphone and carry it all day. The objective of this thesis is to demonstrate the feasibility of the evaluation of the adherence to a healthy lifestyle by means of smartphone applications and sensors, whether internal or externally connected. On the one hand, the accelerometer sensor is used to evaluate the physical activity and the associated energy expenditure. In previous research, we can found classifiers of physical activity from data of this sensor but the measurements were performed in a laboratory environment or with smartphone fixed to a specific position. From the collected data during a week of 26 subjects, a 75.6% of F1-score of the classification of activities has been achieved and a 3.18% of error in the energy expenditure estimation. On the other hand, the heart rate variability (HRV) can serve as indicator of behaviours related to health and physical condition. A system has been designed to evaluate the HRV using the rear camera of the smartphone as a sensor. For this purpose, the photoplethysmography technique has been used. In previous research, this technique has been used in smartphones in order to obtain the heart rate but it has not been assessed the beat-to-beat HRV. The proposed system uses the GPU for image processing in real time. The obtained results have been compared with the electrocardiogram and with a reference photoplethysmography device. For that, the standard deviation of error made for the beat detection and the level of agreement of HRV indices have been assessed. This assessment has been performed with 23 subjects and the results obtained for two different smartphone models have been compared. The standard deviation of error of heart rate detection between smartphone and electrocardiogram obtained was 5.4 ms, while between electrocardiogram and reference photoplethysmography device was 4.9 ms. On the other hand, an application for the ensemble analysis of physical activity and heart rate has been developed. Using this application, the data of 11 people was collected, they have divided in two groups of 5 and 6 people during 3 and 6 weeks respectively. From the analysis of the collected data, it has been found that the level of physical activity decreases over the time and there is some association between the constancy of the practice of physical activity and changes in mood. However, these association should be taken with caution due to the reduced number of subjects which were involved in this study. Therefore, the developed system is a starting point in order to evaluate the adherence to a healthy lifestyle in a unified way with an single application. Finally, one of the consequences of leading an unhealthy lifestyle is the decreasing of quality of sleep that can cause daytime sleepiness. This can be a serious health risk, for example if it occurs while driving. To prevent this, an early drowsiness detection system based on the analysis of respiratory signal and respiratory rate variability has been proposed and validated. The designed algorithm has been assessed with 15 subjects and a specificity of 96.6% and a sensitivity of 90.3% has been obtained.La adherencia a un estilo de vida saludable es un factor muy importante para alargar años de vida y aumentar su calidad. Los principales hábitos de vida saludable son: la actividad física, la dieta y la calidad del sueño. Hoy en día muchas personas utilizan un smartphone y lo llevan encima todo el día. El objetivo de esta tesis es demostrar la viabilidad de la evaluación de la adherencia a hábitos de vida saludables mediante aplicaciones móviles y sensores ya sean del propio smartphone o conectados externamente. Para ello, se utiliza el sensor de acelerometría para evaluar la actividad física y el gasto calórico asociado. En trabajos previos podemos encontrar clasificadores de actividad física a partir de los datos de estos sensores pero las medidas las realizan en un entorno de laboratorio o con el smartphone ubicado en una posición determinada. A partir de los datos de 26 sujetos recogidos durante una semana se ha alcanzado un 75.6% de F1-score de la clasificación de actividades y un 3.18% de error de estimación de gasto calórico. Por otro lado, la variabilidad de la frecuencia cardíaca (VFC) puede servir de indicador de conductas relacionadas con la salud y la condición física. Se ha diseñado un sistema para evaluar la VFC utilizando como sensor la cámara trasera del smartphone. Para ello se ha utilizado la técnica de fotopletismografía. En trabajos previos se ha utilizado esta técnica en smartphones para obtener el ritmo cardíaco pero no se ha comparado la variabilidad del ritmo cardíaco latido a latido. El sistema propuesto utiliza la GPU para procesar la imágenes en tiempo real. Los resultados obtenidos se han comparado con el electrocardiograma y con un dispositivo de fotopletismografía de referencia. Para ello, se ha evaluado la desviación estándar del error cometido en la detección del latido cardíaco y el grado de acuerdo de los índices de VFC. Esta evaluación se ha realizado en 23 sujetos y se han comparado los resultados obtenidos con dos modelos de smartphone. La desviación estándar del error en la detección del latido cardíaco obtenida entre el smartphone y el electrocardiograma es de 5.4 ms, mientras que entre el dispositivo de referencia de fotopletismografía y el electrocardiograma es de 4.9 ms. Por otro lado, se ha desarrollado una aplicación para el análisis conjunto de la actividad física y el ritmo cardíaco. Se recogieron los datos de 11 personas utilizando esta aplicación, divididas en dos grupos de 5 y 6 personas durante 3 y 6 semanas respectivamente. A partir del análisis de los datos recogidos se ha encontrado que el nivel de la actividad física desciende a lo largo del tiempo y que existe alguna asociación entre la constancia en la práctica de la actividad física y los cambios en el estado de ánimo. Sin embargo, estas asociaciones se han de tomar con precaución debido al reducido número de sujetos que han participado en este estudio. Por lo tanto, el sistema desarrollado supone un punto de partida para evaluar la adherencia a un estilo de vida saludable de forma unificada en una única aplicación. Finalmente, una de las consecuencias de llevar un estilo de vida poco saludable es el empobrecimiento de la calidad del sueño que puede provocar la somnolencia diurna. Esto puede resultar un grave peligro para la salud, por ejemplo si se produce mientras se está al volante. Para prevenir esto, se ha propuesto y validado un sistema de detección de somnolencia temprana a partir del análisis de la señal respiratoria basado en la variabilidad del ritmo respiratorio. El algoritmo diseñado ha sido validado con 15 sujetos y se ha obtenido una especificidad del 96.6% y una sensibilidad del 90.3%.Postprint (published version

Una contribución a la evaluación de la adherencia a hábitos de vida saludables basado en aplicaciones móviles

The adherence to a healthy lifestyle plays a key role for increasing life expectancy and living better. The main habits of healthy lifestyle are: physical activity, diet and sleep quality. Nowadays, many people use a smartphone and carry it all day. The objective of this thesis is to demonstrate the feasibility of the evaluation of the adherence to a healthy lifestyle by means of smartphone applications and sensors, whether internal or externally connected. On the one hand, the accelerometer sensor is used to evaluate the physical activity and the associated energy expenditure. In previous research, we can found classifiers of physical activity from data of this sensor but the measurements were performed in a laboratory environment or with smartphone fixed to a specific position. From the collected data during a week of 26 subjects, a 75.6% of F1-score of the classification of activities has been achieved and a 3.18% of error in the energy expenditure estimation. On the other hand, the heart rate variability (HRV) can serve as indicator of behaviours related to health and physical condition. A system has been designed to evaluate the HRV using the rear camera of the smartphone as a sensor. For this purpose, the photoplethysmography technique has been used. In previous research, this technique has been used in smartphones in order to obtain the heart rate but it has not been assessed the beat-to-beat HRV. The proposed system uses the GPU for image processing in real time. The obtained results have been compared with the electrocardiogram and with a reference photoplethysmography device. For that, the standard deviation of error made for the beat detection and the level of agreement of HRV indices have been assessed. This assessment has been performed with 23 subjects and the results obtained for two different smartphone models have been compared. The standard deviation of error of heart rate detection between smartphone and electrocardiogram obtained was 5.4 ms, while between electrocardiogram and reference photoplethysmography device was 4.9 ms. On the other hand, an application for the ensemble analysis of physical activity and heart rate has been developed. Using this application, the data of 11 people was collected, they have divided in two groups of 5 and 6 people during 3 and 6 weeks respectively. From the analysis of the collected data, it has been found that the level of physical activity decreases over the time and there is some association between the constancy of the practice of physical activity and changes in mood. However, these association should be taken with caution due to the reduced number of subjects which were involved in this study. Therefore, the developed system is a starting point in order to evaluate the adherence to a healthy lifestyle in a unified way with an single application. Finally, one of the consequences of leading an unhealthy lifestyle is the decreasing of quality of sleep that can cause daytime sleepiness. This can be a serious health risk, for example if it occurs while driving. To prevent this, an early drowsiness detection system based on the analysis of respiratory signal and respiratory rate variability has been proposed and validated. The designed algorithm has been assessed with 15 subjects and a specificity of 96.6% and a sensitivity of 90.3% has been obtained.La adherencia a un estilo de vida saludable es un factor muy importante para alargar años de vida y aumentar su calidad. Los principales hábitos de vida saludable son: la actividad física, la dieta y la calidad del sueño. Hoy en día muchas personas utilizan un smartphone y lo llevan encima todo el día. El objetivo de esta tesis es demostrar la viabilidad de la evaluación de la adherencia a hábitos de vida saludables mediante aplicaciones móviles y sensores ya sean del propio smartphone o conectados externamente. Para ello, se utiliza el sensor de acelerometría para evaluar la actividad física y el gasto calórico asociado. En trabajos previos podemos encontrar clasificadores de actividad física a partir de los datos de estos sensores pero las medidas las realizan en un entorno de laboratorio o con el smartphone ubicado en una posición determinada. A partir de los datos de 26 sujetos recogidos durante una semana se ha alcanzado un 75.6% de F1-score de la clasificación de actividades y un 3.18% de error de estimación de gasto calórico. Por otro lado, la variabilidad de la frecuencia cardíaca (VFC) puede servir de indicador de conductas relacionadas con la salud y la condición física. Se ha diseñado un sistema para evaluar la VFC utilizando como sensor la cámara trasera del smartphone. Para ello se ha utilizado la técnica de fotopletismografía. En trabajos previos se ha utilizado esta técnica en smartphones para obtener el ritmo cardíaco pero no se ha comparado la variabilidad del ritmo cardíaco latido a latido. El sistema propuesto utiliza la GPU para procesar la imágenes en tiempo real. Los resultados obtenidos se han comparado con el electrocardiograma y con un dispositivo de fotopletismografía de referencia. Para ello, se ha evaluado la desviación estándar del error cometido en la detección del latido cardíaco y el grado de acuerdo de los índices de VFC. Esta evaluación se ha realizado en 23 sujetos y se han comparado los resultados obtenidos con dos modelos de smartphone. La desviación estándar del error en la detección del latido cardíaco obtenida entre el smartphone y el electrocardiograma es de 5.4 ms, mientras que entre el dispositivo de referencia de fotopletismografía y el electrocardiograma es de 4.9 ms. Por otro lado, se ha desarrollado una aplicación para el análisis conjunto de la actividad física y el ritmo cardíaco. Se recogieron los datos de 11 personas utilizando esta aplicación, divididas en dos grupos de 5 y 6 personas durante 3 y 6 semanas respectivamente. A partir del análisis de los datos recogidos se ha encontrado que el nivel de la actividad física desciende a lo largo del tiempo y que existe alguna asociación entre la constancia en la práctica de la actividad física y los cambios en el estado de ánimo. Sin embargo, estas asociaciones se han de tomar con precaución debido al reducido número de sujetos que han participado en este estudio. Por lo tanto, el sistema desarrollado supone un punto de partida para evaluar la adherencia a un estilo de vida saludable de forma unificada en una única aplicación. Finalmente, una de las consecuencias de llevar un estilo de vida poco saludable es el empobrecimiento de la calidad del sueño que puede provocar la somnolencia diurna. Esto puede resultar un grave peligro para la salud, por ejemplo si se produce mientras se está al volante. Para prevenir esto, se ha propuesto y validado un sistema de detección de somnolencia temprana a partir del análisis de la señal respiratoria basado en la variabilidad del ritmo respiratorio. El algoritmo diseñado ha sido validado con 15 sujetos y se ha obtenido una especificidad del 96.6% y una sensibilidad del 90.3%

Driver drowsiness detection based on respiratory signal analysis

Drowsy driving is a prevalent and serious public health issue that deserves attention. Recent studies estimate around 20% of car crashes have been caused by drowsy drivers. Nowadays, one of the main goals in the development of new advanced driver assistance systems is the trustworthy drowsiness detection. In this paper, a drowsiness detection method based on changes in the respiratory signal is proposed. The respiratory signal, which has been obtained using an inductive plethysmography belt, has been processed in real-time in order to classify the driver’s state of alertness as drowsy or awake. The proposed algorithm is based on the analysis of the respiratory rate variability (RRV) in order to detect the fight against to fall asleep. Moreover, a method to provide a quality level of the respiratory signal is also proposed. Both methods have been combined to reduce false alarms due to changes of measured RRV associated not to drowsiness but body movements. A driving simulator cabin has been used to perform the validation tests and external observers have rated the drivers’ state of alertness in order to evaluate the algorithm performance. It has been achieved a specificity of 96.6%, sensitivity of 90.3% and Cohen’s Kappa agreement score of 0.75 on average across all subjects through a leave-one-subject-out cross-validation. A novel algorithm for driver’s state of alertness monitoring through the identification of the fight against to fall asleep has been validated. The proposed algorithm may be a valuable vehicle safety system to alert drowsiness while drivingPeer ReviewedPostprint (published version

Image Analysis System for Early Detection of Cardiothoracic Surgery Wound Alterations Based on Artificial Intelligence Models

Funding Information: This work is part of a research project funded by Fundação para a Ciência e Tecnologia, which aims to design and implement a post-surgical digital telemonitoring service for cardiothoracic surgery patients. The main goals of the research project are: to study the impact of daily telemonitoring on early diagnosis, to reduce hospital readmissions, and to improve patient safety, during the 30-day period after hospital discharge. This remote follow-up involves a digital remote patient monitoring kit which includes a sphygmomanometer, a scale, a smartwatch, and a smartphone, allowing daily patient data collection. One of the daily outcomes was the daily photographs taken by patients regarding surgical wounds. Every day, the clinical team had to analyze the image of each patient, which could take a long time. The automatic analysis of these images would allow implementing an alert related to the detection of wound modifications that could represent a risk of infection. Such an alert would spare time for the clinical team in follow-up care. Funding Information: This research has been supported by Fundação para a Ciência e Tecnologia (FCT) under CardioFollow.AI project (DSAIPA/AI/0094/2020), Lisboa-05-3559-FSE-000003 and UIDB/04559/2020. Publisher Copyright: © 2023 by the authors.Cardiothoracic surgery patients have the risk of developing surgical site infections which cause hospital readmissions, increase healthcare costs, and may lead to mortality. This work aims to tackle the problem of surgical site infections by predicting the existence of worrying alterations in wound images with a wound image analysis system based on artificial intelligence. The developed system comprises a deep learning segmentation model (MobileNet-Unet), which detects the wound region area and categorizes the wound type (chest, drain, and leg), and a machine learning classification model, which predicts the occurrence of wound alterations (random forest, support vector machine and k-nearest neighbors for chest, drain, and leg, respectively). The deep learning model segments the image and assigns the wound type. Then, the machine learning models classify the images from a group of color and textural features extracted from the output region of interest to feed one of the three wound-type classifiers that reach the final binary decision of wound alteration. The segmentation model achieved a mean Intersection over Union of 89.9% and a mean average precision of 90.1%. Separating the final classification into different classifiers was more effective than a single classifier for all the wound types. The leg wound classifier exhibited the best results with an 87.6% recall and 52.6% precision.publishersversionpublishe

A deep learning based object identification system for forest fire detection

POCI-01-0247-FEDER-038342Forest fires are still a large concern in several countries due to the social, environmental and economic damages caused. This paper aims to show the design and validation of a proposed system for the classification of smoke columns with object detection and a deep learning-based approach. This approach is able to detect smoke columns visible below or above the horizon. During the dataset labelling, the smoke object was divided into three different classes, depending on its distance to the horizon, a cloud object was also added, along with images without annotations. A comparison between the use of RetinaNet and Faster R-CNN was also performed. Using an independent test set, an F1-score around 80%, a G-mean around 80% and a detection rate around 90% were achieved by the two best models: both were trained with the dataset labelled with three different smoke classes and with augmentation; Faster R-CNNN was the model architecture, re-trained during the same iterations but following different learning rate schedules. Finally, these models were tested in 24 smoke sequences of the public HPWREN dataset, with 6.3 min as the average time elapsed from the start of the fire compared to the first detection of a smoke column.publishersversionpublishe

SeniorFit : Una aplicación móvil para el seguimiento de la adherencia a estilos de vida saludable para gente mayor

El envejecimiento progresivo de la población en los países desarrollados exige la promoción de estilos de vida saludables para fomentar el envejecimiento activo. En este trabajo se presenta una aplicación denominada SeniorFit que pretende facilitar la autoevaluación de la adherencia al estilo de vida mediante una herramienta sencilla, cómoda y fiable. La aplicación está desarrollada para móviles y permite medir de forma no intrusiva la actividad física, el pulso cardíaco y evaluar el estado de ánimo utilizando únicamente el propio móvil. Esta aplicación ha sido utilizada por un grupo de gente mayor durante 3 semanas y en condiciones libres. Los usuarios han manifestado un alto grado de satisfacción y la facilidad de su uso.Postprint (published version

Comparison of video-based methods for respiration rhythm measurement

The aim of this work is to characterize the di erences in the respiratory rhythm obtained through three video based methods by comparing the obtained respiratory signals with the one obtained with the gold standard method in adult population. The analysed methods are an RGB camera, a depth camera and a thermal camera while the gold standard is an inductive thorax plethysmography system (Respiband system from BioSignals Plux). 21 healthy subjects where measured, performing 4 tests for each subject. The respiratory rhythm and its variability was obtained from the four respiratory signals (3 video methods and gold standard). The signal acquisition was performed with custom and proprietary algorithms. To characterize the respiratory rhythm and its variability obtained with the di erent video sources and gold standard, the instantaneous frequency, Bland-Altman plots and standard deviation of the error between video methods and the gold standard have been computed. The depth and RGB camera present high agreement with no statistical di erences between them, with errors when comparing with the gold standard in the range of mHz. The thermal camera performs poorly if compared with the two other methods, nevertheless it cannot be discarded directly because some errors produced by the subjects head movement could not be corrected. From these results we conclude that the depth and RGB camera, and their respective acquisition algorithms) can be used in controlled conditions to measure respiration rhythm and its variability. The thermal camera on the other hand, although it can not be discarded directly, performed poorly if compared with the other two methods. Further studies are needed to con rm that these methods can be used in real life conditions.Postprint (author's final draft

A photoplethysmography smartphone-based method for heart rate variability assessment: device model and breathing influences

© 2019 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A measurement method of heart rate and heart rate variability (HRV) based on smartphone has been developed and validated. The method is based on photoplethysmography (PPG) acquired with the smartphone camera (SPPG). SPPG was compared with the electrocardiogram (ECG), used as the gold standard, and with an external PPG sensor. Twenty-three healthy subjects were measured using two different smartphone models in three different breathing conditions. The error of the first differentiation between SPPG and ECG series is minimized with the fiducial point at maximum first derivative of the SPPG. The obtained standard deviation of error (SDE) between SPPG and ECG is around 5.4 ms and it is similar to SDE between PPG and ECG. Good agreement between SPPG and ECG for NN, SDNN and RMSSD have been found but it is insufficient agreement for LF/HF. Similar levels of agreement for SPPG-ECG and PPG-ECG have been obtained for the HRV indices. Finally, the differences between smartphone models for HRV indices are slight. Therefore, the smartphone can be used for measuring accurately the following HRV indices: NN, SDNN and RMSSD.Peer ReviewedPostprint (published version

Using smartphone bases biodevices for analyzing physiological, psychological and behavioral user’s habits

As a consequence of increasing life expectancy, the promotion of lifestyles that allow aging wellbeing guarantees has acquired great importance in the developed countries. However, the adherence to healthy behaviors in young and adult people remains as a big problem in the community health field. The development of markers of adherence to healthy lifestyles and the evaluation its effectiveness is a goal of many research groups. This paper presents a system for analyzing physiological, psychological and behavioural user’s habits using a smartphone and externals biodevices. We use an Android smartphone with an internal tri-axial accelerometer and GPS to monitor physical activity. The smartphone is connected via Bluetooth to a respiratory sensor for breath monitoring. In addition, Android application contains psychological questionnaires to analyze user’s mood state and at the same, social interaction is analyzed tracking phone usage and user’s social network. Finally, the collected information is sent to a remote server for a long-term processing.Postprint (published version

Prototipo de pulsera para la medida de ECG bajo demanda y la medida continua de la onda de pulso

El interés en la variabilidad de ritmo cardiaco (HRV) y la estimación de la presión arterial a partir del tiempo de llegada de la onda de pulso (PAT) se ha incrementado exponencialmente en los últimos años debido a los indicadores que se pueden extraer. En este trabajo se presenta un prototipo de pulsera que, sin ningún otro medio externo a esta, es capaz de medir electrocardiograma (ECG), onda de pulso y, por consiguiente el PAT, de forma no invasiva. Además, está diseñada para la realización de medidas oportunistas y pensando en la comodidad del usuario. Se presentan también los materiales y electrónica empleados para la implementación del prototipo. A partir de los resultados obtenidos se da una guía para la colocación de los sensores en la pulsera. Finalmente, los resultados indican que la calidad de las medidas realizadas es suficiente para ser utilizadas en el estudio de HRV y PAT.Postprint (updated version