This page has only limited features, please log in for full access.
Diabetic retinopathy is a complication of diabetes mellitus. Its early diagnosis can prevent its progression and avoid the development of other major complications such as blindness. Deep learning and transfer learning appear in this context as powerful tools to aid in diagnosing this condition. The present work proposes to experiment with different models of pre-trained convolutional neural networks to determine which one fits best the problem of predicting diabetic retinopathy. The Diabetic Retinopathy Detection dataset supported by the EyePACS competition is used for evaluation. Seven pre-trained CNN models implemented in the Keras library developed in Python and, in this case, executed in the Kaggle platform, are used. Results show that no architecture performs better in all evaluation metrics. From a balanced behaviour perspective, the MobileNetV2 model stands out, with execution times almost half that of the slowest CNNs and without falling into overfitting with 20 learning epochs. InceptionResNetV2 stands out from the perspective of best performance, with a Kappa coefficient of 0.7588.
Ciro Rodriguez-Leon; William Arevalo; Oresti Banos; Claudia Villalonga. Deep Learning for Diabetic Retinopathy Prediction. Lecture Notes in Computer Science 2021, 537 -546.
AMA StyleCiro Rodriguez-Leon, William Arevalo, Oresti Banos, Claudia Villalonga. Deep Learning for Diabetic Retinopathy Prediction. Lecture Notes in Computer Science. 2021; ():537-546.
Chicago/Turabian StyleCiro Rodriguez-Leon; William Arevalo; Oresti Banos; Claudia Villalonga. 2021. "Deep Learning for Diabetic Retinopathy Prediction." Lecture Notes in Computer Science , no. : 537-546.
People with autism spectrum disorder (ASD) are known to show difficulties in the interpretation of human conversational facial expressions. With the recent advent of artificial intelligence, and more specifically, deep learning techniques, new possibilities arise in this context to support people with autism in the recognition of such expressions. This work aims at developing a deep neural network model capable of recognizing conversational facial expressions which are prone to misinterpretation in ASD. To that end, a publicly available dataset of conversational facial expressions is used to train various CNN-LSTM architectures. Training results are promising; however, the model shows limited generalization. Therefore, better conversational facial expressions datasets are required before attempting to build a full-fledged ASD-oriented support system.
Pablo Salgado; Oresti Banos; Claudia Villalonga. Facial Expression Interpretation in ASD Using Deep Learning. Computer Algebra in Scientific Computing 2021, 322 -333.
AMA StylePablo Salgado, Oresti Banos, Claudia Villalonga. Facial Expression Interpretation in ASD Using Deep Learning. Computer Algebra in Scientific Computing. 2021; ():322-333.
Chicago/Turabian StylePablo Salgado; Oresti Banos; Claudia Villalonga. 2021. "Facial Expression Interpretation in ASD Using Deep Learning." Computer Algebra in Scientific Computing , no. : 322-333.
Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now with the advent of wearable, mobile, and ubiquitous technologies that we can aim at sensing and recognizing emotions, continuously and in the wild. This Special Issue aims at bringing together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and recognition of human emotions.
Oresti Banos; Luis Castro; Claudia Villalonga. Ubiquitous Technologies for Emotion Recognition. Applied Sciences 2021, 11, 7019 .
AMA StyleOresti Banos, Luis Castro, Claudia Villalonga. Ubiquitous Technologies for Emotion Recognition. Applied Sciences. 2021; 11 (15):7019.
Chicago/Turabian StyleOresti Banos; Luis Castro; Claudia Villalonga. 2021. "Ubiquitous Technologies for Emotion Recognition." Applied Sciences 11, no. 15: 7019.
BACKGROUND Heavy physical and mental loads are typical for professional soccer players during the competitive season. COVID-19 lockdowns had recently forced competitions to be interrupted and later disputed in a shrunken calendar. Wearable sensors and mobile phones could be potentially useful in monitoring players’ training load in such highly demanding environments. OBJECTIVE The aim of this study was to explore whether remote heart rate variability (HRV) monitoring and self-reported wellness of professional soccer players could be useful to monitor players’ internal training load and to estimate their performance during the continuation of the 2020 season after the COVID-19 lockdown in Spain. METHODS A total of 21 professional soccer players participated in a 6-week study. Participants used an Android or iOS-based smartphone and a Polar H10 wearable ECG monitor for the duration of the study. Every morning they recorded their HRV and answered a questionnaire about their perceived recovery, muscle soreness, stress and sleep satisfaction. Smallest worthwhile change (SWC) and coefficient of variation (CV) were calculated for the logarithm of the root mean square of the successives differences (LnRMSSD) of the HRV. Players’ in-game performance was evaluated subjectively by independent observers and classified as high, normal and low. In order to find which variables could be potentially linked to performance, we studied their correlation and tested for significant differences among distributions. We also trained random forest models with cross-validation and bootstrapping to find the wellness and HRV features with best predictive ability for performance. RESULTS We found the usability of Readiness Soccer in a real scenario to be very good, with 81.36 points in the System Usability Scale. A total of 241 measurements of HRV and self-reported wellness were recorded. For a entire training microcycle (ie, time in between matches), self-reported high recovery (Mann-Whitney U, P=.003), low muscle soreness (P=.002), high sleep satisfaction (P=.02), low stress (Anderson-Darling, P=.03), and not needing more than 30 minutes to sleep since going to bed (Chi-Squared, P=.02), were found significant to differentiate high from normal match performance. Performance estimation models achieved the highest accuracy (73.4%) when combining self-reported wellness and HRV features. CONCLUSIONS HRV and self-reported wellness data were useful to monitor the evolution of professional soccer players’ internal load and to predict match performance levels out of measures in a training microcycle. Despite the limitations, these findings highlight opportunities for long-term monitoring of soccer players during the competitive season as well as real-time interventions aimed at early management of overtraining and boosting individual performance.
Salvador Moreno-Gutierrez; Oresti Banos; Miguel Damas; Hector Pomares; Paula Postigo-Martin; Irene Cantarero-Villanueva; Manuel Arroyo-Morales. Monitoring Internal Load with a Mobile App in Professional Soccer Players after the COVID-19 Lockdown (Readiness Soccer): Observational Study (Preprint). 2021, 1 .
AMA StyleSalvador Moreno-Gutierrez, Oresti Banos, Miguel Damas, Hector Pomares, Paula Postigo-Martin, Irene Cantarero-Villanueva, Manuel Arroyo-Morales. Monitoring Internal Load with a Mobile App in Professional Soccer Players after the COVID-19 Lockdown (Readiness Soccer): Observational Study (Preprint). . 2021; ():1.
Chicago/Turabian StyleSalvador Moreno-Gutierrez; Oresti Banos; Miguel Damas; Hector Pomares; Paula Postigo-Martin; Irene Cantarero-Villanueva; Manuel Arroyo-Morales. 2021. "Monitoring Internal Load with a Mobile App in Professional Soccer Players after the COVID-19 Lockdown (Readiness Soccer): Observational Study (Preprint)." , no. : 1.
Recognizing human activities seamlessly and ubiquitously is now closer than ever given the myriad of sensors readily deployed on and around users. However, the training of recognition systems continues to be both time and resource-consuming, as datasets must be collected ad-hoc for each specific sensor setup a person may encounter in their daily life. This work presents an alternate approach based on transfer learning to opportunistically train new unseen or target sensor systems from existing or source sensor systems. The approach uses system identification techniques to learn a mapping function that automatically translates the signals from the source sensor domain to the target sensor domain, and vice versa. This can be done for sensor signals of the same or cross modality. Two transfer models are proposed to translate recognition systems based on either activity templates or activity models, depending on the characteristics of both source and target sensor systems. The proposed transfer methods are evaluated in a human–computer interaction scenario, where the transfer is performed in between wearable sensors placed at different body locations, and in between wearable sensors and an ambient depth camera sensor. Results show that a good transfer is possible with just a few seconds of data, irrespective of the direction of the transfer and for similar and cross sensor modalities.
Oresti Banos; Alberto Calatroni; Miguel Damas; Hector Pomares; Daniel Roggen; Ignacio Rojas; Claudia Villalonga. Opportunistic Activity Recognition in IoT Sensor Ecosystems via Multimodal Transfer Learning. Neural Processing Letters 2021, 1 -29.
AMA StyleOresti Banos, Alberto Calatroni, Miguel Damas, Hector Pomares, Daniel Roggen, Ignacio Rojas, Claudia Villalonga. Opportunistic Activity Recognition in IoT Sensor Ecosystems via Multimodal Transfer Learning. Neural Processing Letters. 2021; ():1-29.
Chicago/Turabian StyleOresti Banos; Alberto Calatroni; Miguel Damas; Hector Pomares; Daniel Roggen; Ignacio Rojas; Claudia Villalonga. 2021. "Opportunistic Activity Recognition in IoT Sensor Ecosystems via Multimodal Transfer Learning." Neural Processing Letters , no. : 1-29.
The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.
Leonardo Gutierrez; Kashif Rabbani; Oluwashina Ajayi; Samson Gebresilassie; Joseph Rafferty; Luis Castro; Oresti Banos. Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management. International Journal of Environmental Research and Public Health 2021, 18, 1327 .
AMA StyleLeonardo Gutierrez, Kashif Rabbani, Oluwashina Ajayi, Samson Gebresilassie, Joseph Rafferty, Luis Castro, Oresti Banos. Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management. International Journal of Environmental Research and Public Health. 2021; 18 (3):1327.
Chicago/Turabian StyleLeonardo Gutierrez; Kashif Rabbani; Oluwashina Ajayi; Samson Gebresilassie; Joseph Rafferty; Luis Castro; Oresti Banos. 2021. "Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management." International Journal of Environmental Research and Public Health 18, no. 3: 1327.
As advances in technology continue relentlessly, intriguing possibilities for smart home services have emerged
Juan Ye; Michael O’Grady; Oresti Banos. Sensor Technology for Smart Homes. Sensors 2020, 20, 7046 .
AMA StyleJuan Ye, Michael O’Grady, Oresti Banos. Sensor Technology for Smart Homes. Sensors. 2020; 20 (24):7046.
Chicago/Turabian StyleJuan Ye; Michael O’Grady; Oresti Banos. 2020. "Sensor Technology for Smart Homes." Sensors 20, no. 24: 7046.
BACKGROUND Diabetes mellitus is a metabolic disorder that affects hundreds of millions of people worldwide and causes several million deaths every year. Such a dramatic scenario puts some pressure on administrations, care services, and the scientific community to seek novel solutions that may help control and deal effectively with this condition and its consequences. OBJECTIVE This study aims to review the literature on the use of modern mobile and wearable technology for monitoring parameters that condition the development or evolution of diabetes mellitus. METHODS A systematic review of articles published between January 2010 and July 2020 was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Manuscripts were identified through searching the databases Web of Science, Scopus, and PubMed as well as through hand searching. Manuscripts were included if they involved the measurement of diabetes-related parameters such as blood glucose level, performed physical activity, or feet condition via wearable or mobile devices. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. RESULTS The search yielded 1981 articles. A total of 26 publications met the eligibility criteria and were included in the review. Studies predominantly used wearable devices to monitor diabetes-related parameters. The accelerometer was by far the most used sensor, followed by the glucose monitor and heart rate monitor. Most studies applied some type of processing to the collected data, mainly consisting of statistical analysis or machine learning for activity recognition, finding associations among health outcomes, and diagnosing conditions related to diabetes. Few studies have focused on type 2 diabetes, even when this is the most prevalent type and the only preventable one. None of the studies focused on common diabetes complications. Clinical trials were fairly limited or nonexistent in most of the studies, with a common lack of detail about cohorts and case selection, comparability, and outcomes. Explicit endorsement by ethics committees or review boards was missing in most studies. Privacy or security issues were seldom addressed, and even if they were addressed, they were addressed at a rather insufficient level. CONCLUSIONS The use of mobile and wearable devices for the monitoring of diabetes-related parameters shows early promise. Its development can benefit patients with diabetes, health care professionals, and researchers. However, this field is still in its early stages. Future work must pay special attention to privacy and security issues, the use of new emerging sensor technologies, the combination of mobile and clinical data, and the development of validated clinical trials.
Ciro Rodriguez-León; Claudia Villalonga; Manuel Munoz-Torres; Jonatan R Ruiz; Oresti Banos. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review (Preprint). 2020, 1 .
AMA StyleCiro Rodriguez-León, Claudia Villalonga, Manuel Munoz-Torres, Jonatan R Ruiz, Oresti Banos. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review (Preprint). . 2020; ():1.
Chicago/Turabian StyleCiro Rodriguez-León; Claudia Villalonga; Manuel Munoz-Torres; Jonatan R Ruiz; Oresti Banos. 2020. "Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review (Preprint)." , no. : 1.
Diabetes mellitus is a metabolic disorder that affects hundreds of millions of people worldwide and causes several million deaths every year. Such a dramatic scenario puts some pressure on administrations, care services, and the scientific community to seek novel solutions that may help control and deal effectively with this condition and its consequences. This study aims to review the literature on the use of modern mobile and wearable technology for monitoring parameters that condition the development or evolution of diabetes mellitus. A systematic review of articles published between January 2010 and July 2020 was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Manuscripts were identified through searching the databases Web of Science, Scopus, and PubMed as well as through hand searching. Manuscripts were included if they involved the measurement of diabetes-related parameters such as blood glucose level, performed physical activity, or feet condition via wearable or mobile devices. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. The search yielded 1981 articles. A total of 26 publications met the eligibility criteria and were included in the review. Studies predominantly used wearable devices to monitor diabetes-related parameters. The accelerometer was by far the most used sensor, followed by the glucose monitor and heart rate monitor. Most studies applied some type of processing to the collected data, mainly consisting of statistical analysis or machine learning for activity recognition, finding associations among health outcomes, and diagnosing conditions related to diabetes. Few studies have focused on type 2 diabetes, even when this is the most prevalent type and the only preventable one. None of the studies focused on common diabetes complications. Clinical trials were fairly limited or nonexistent in most of the studies, with a common lack of detail about cohorts and case selection, comparability, and outcomes. Explicit endorsement by ethics committees or review boards was missing in most studies. Privacy or security issues were seldom addressed, and even if they were addressed, they were addressed at a rather insufficient level. The use of mobile and wearable devices for the monitoring of diabetes-related parameters shows early promise. Its development can benefit patients with diabetes, health care professionals, and researchers. However, this field is still in its early stages. Future work must pay special attention to privacy and security issues, the use of new emerging sensor technologies, the combination of mobile and clinical data, and the development of validated clinical trials.
Ciro Rodriguez-Leon; Claudia Villalonga; Manuel Munoz-Torres; Jonatan R Ruiz; Oresti Banos. Mobile and Wearable Sensing for the Monitoring of Diabetes-related Parameters: Systematic Review (Preprint). JMIR mHealth and uHealth 2020, 9, e25138 .
AMA StyleCiro Rodriguez-Leon, Claudia Villalonga, Manuel Munoz-Torres, Jonatan R Ruiz, Oresti Banos. Mobile and Wearable Sensing for the Monitoring of Diabetes-related Parameters: Systematic Review (Preprint). JMIR mHealth and uHealth. 2020; 9 (6):e25138.
Chicago/Turabian StyleCiro Rodriguez-Leon; Claudia Villalonga; Manuel Munoz-Torres; Jonatan R Ruiz; Oresti Banos. 2020. "Mobile and Wearable Sensing for the Monitoring of Diabetes-related Parameters: Systematic Review (Preprint)." JMIR mHealth and uHealth 9, no. 6: e25138.
The study of cosmic rays remains as one of the most challenging research fields in Physics. From the many questions still open in this area, knowledge of the type of primary for each event remains as one of the most important issues. All of the cosmic rays observatories have been trying to solve this question for at least six decades, but have not yet succeeded. The main obstacle is the impossibility of directly detecting high energy primary events, being necessary to use Monte Carlo models and simulations to characterize generated particles cascades. This work presents the results attained using a simulated dataset that was provided by the Monte Carlo code CORSIKA, which is a simulator of high energy particles interactions with the atmosphere, resulting in a cascade of secondary particles extending for a few kilometers (in diameter) at ground level. Using this simulated data, a set of machine learning classifiers have been designed and trained, and their computational cost and effectiveness compared, when classifying the type of primary under ideal measuring conditions. Additionally, a feature selection algorithm has allowed for identifying the relevance of the considered features. The results confirm the importance of the electromagnetic-muonic component separation from signal data measured for the problem. The obtained results are quite encouraging and open new work lines for future more restrictive simulations.
Luis Javier Herrera; Carlos José Todero Peixoto; Oresti Baños; Juan Miguel Carceller; Francisco Carrillo; Alberto Guillén. Composition Classification of Ultra-High Energy Cosmic Rays. Entropy 2020, 22, 998 .
AMA StyleLuis Javier Herrera, Carlos José Todero Peixoto, Oresti Baños, Juan Miguel Carceller, Francisco Carrillo, Alberto Guillén. Composition Classification of Ultra-High Energy Cosmic Rays. Entropy. 2020; 22 (9):998.
Chicago/Turabian StyleLuis Javier Herrera; Carlos José Todero Peixoto; Oresti Baños; Juan Miguel Carceller; Francisco Carrillo; Alberto Guillén. 2020. "Composition Classification of Ultra-High Energy Cosmic Rays." Entropy 22, no. 9: 998.
The identification of daily life events that trigger significant changes on our affective state has become a fundamental task in emotional research. To achieve it, the affective states must be assessed in real-time, along with situational information that could contextualize the affective data acquired. However, the objective monitoring of the affective states and the context is still in an early stage. Mobile technologies can help to achieve this task providing immediate and objective data of the users’ context and facilitating the assessment of their affective states. Previous works have developed mobile apps for monitoring affective states and context, but they use a fixed methodology which does not allow for making changes based on the progress of the study. This work presents a multimodal platform which leverages the potential of the smartphone sensors and the Experience Sampling Methods (ESM) to provide a continuous monitoring of the affective states and the context in an ubiquitous way. The platform integrates several elements aimed to expedite the real-time management of the ESM questionnaires. In order to show the potential of the platform, and evaluate its usability and its suitability for real-time assessment of affective states, a pilot study has been conducted. The results demonstrate an excellent usability level and a good acceptance from the users and the specialists that conducted the study, and lead to some suggestions for improving the data quality of mobile context-aware ESM-based systems.
Carlos Bailon; Miguel Damas; Hector Pomares; Daniel Sanabria; Pandelis Perakakis; Carmen Goicoechea; Oresti Banos. Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods. Sensors 2019, 19, 3430 .
AMA StyleCarlos Bailon, Miguel Damas, Hector Pomares, Daniel Sanabria, Pandelis Perakakis, Carmen Goicoechea, Oresti Banos. Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods. Sensors. 2019; 19 (15):3430.
Chicago/Turabian StyleCarlos Bailon; Miguel Damas; Hector Pomares; Daniel Sanabria; Pandelis Perakakis; Carmen Goicoechea; Oresti Banos. 2019. "Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods." Sensors 19, no. 15: 3430.
The automatic recognition of physical activities typically involves various signal processing and machine learning steps used to transform raw sensor data into activity labels. One crucial step has to do with the segmentation or windowing of the sensor data stream, as it has clear implications on the eventual accuracy level of the activity recogniser. While prior studies have proposed specific window sizes to generally achieve good recognition results, in this work we explore the potential of fusing multiple equally-sized subwindows to improve such recognition capabilities. We tested our approach for eight different subwindow sizes on a widely-used activity recognition dataset. The results show that the recognition performance can be increased up to 15% when using the fusion of equally-sized subwindows compared to using a classical single window.
Oresti Banos; Juan-Manuel Galvez; Miguel Damas; Alberto Guillen; Luis-Javier Herrera; Hector Pomares; Ignacio Rojas; Claudia Villalonga. Improving Wearable Activity Recognition via Fusion of Multiple Equally-Sized Data Subwindows. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 360 -367.
AMA StyleOresti Banos, Juan-Manuel Galvez, Miguel Damas, Alberto Guillen, Luis-Javier Herrera, Hector Pomares, Ignacio Rojas, Claudia Villalonga. Improving Wearable Activity Recognition via Fusion of Multiple Equally-Sized Data Subwindows. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():360-367.
Chicago/Turabian StyleOresti Banos; Juan-Manuel Galvez; Miguel Damas; Alberto Guillen; Luis-Javier Herrera; Hector Pomares; Ignacio Rojas; Claudia Villalonga. 2019. "Improving Wearable Activity Recognition via Fusion of Multiple Equally-Sized Data Subwindows." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 360-367.
Recent technological advances have enabled the continuous and unobtrusive monitoring of human behaviour. However, most of the existing studies focus on detecting human behaviour under the limitation of one behavioural aspect, such as physical behaviour and not addressing human behaviour in a broad sense. For this reason, we propose a novel framework that will serve as the principal generator of knowledge on the user’s behaviour. The proposed framework moves beyond the current trends in automatic behaviour analysis by detecting and inferring human behaviour automatically, based on multimodal sensor data. In particular, the framework analyses human behaviour in a holistic approach, focusing on different behavioural aspects at the same time; namely physical, social, emotional and cognitive behaviour. Furthermore, the suggested framework investigates user’s behaviour over different periods, introducing the concept of short-term and long-term behaviours and how these change over time.
Kostas Konsolakis; Hermie Hermens; Oresti Banos. A Novel Framework for the Holistic Monitoring and Analysis of Human Behaviour. Proceedings 2019, 31, 43 .
AMA StyleKostas Konsolakis, Hermie Hermens, Oresti Banos. A Novel Framework for the Holistic Monitoring and Analysis of Human Behaviour. Proceedings. 2019; 31 (1):43.
Chicago/Turabian StyleKostas Konsolakis; Hermie Hermens; Oresti Banos. 2019. "A Novel Framework for the Holistic Monitoring and Analysis of Human Behaviour." Proceedings 31, no. 1: 43.
Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.
Jaehun Bang; Taeho Hur; Dohyeong Kim; Thien Huynh-The; Jongwon Lee; Yongkoo Han; Oresti Banos; Jee-In Kim; Sungyoung Lee. Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments. Sensors 2018, 18, 3744 .
AMA StyleJaehun Bang, Taeho Hur, Dohyeong Kim, Thien Huynh-The, Jongwon Lee, Yongkoo Han, Oresti Banos, Jee-In Kim, Sungyoung Lee. Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments. Sensors. 2018; 18 (11):3744.
Chicago/Turabian StyleJaehun Bang; Taeho Hur; Dohyeong Kim; Thien Huynh-The; Jongwon Lee; Yongkoo Han; Oresti Banos; Jee-In Kim; Sungyoung Lee. 2018. "Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments." Sensors 18, no. 11: 3744.
Smartphones are revolutionizing the way people perceive and interact with both physical and cyber worlds. Mobile coaching (m-coaching) is expected to become a crucial contributor to such a revolution by enabling a new way of providing and receiving personalized healthcare.
Oresti Banos; Chris Nugent. M-Coaching: Towards the Next Generation of Mobile-Driven Healthcare Support Services. Computer 2018, 51, 14 -17.
AMA StyleOresti Banos, Chris Nugent. M-Coaching: Towards the Next Generation of Mobile-Driven Healthcare Support Services. Computer. 2018; 51 (8):14-17.
Chicago/Turabian StyleOresti Banos; Chris Nugent. 2018. "M-Coaching: Towards the Next Generation of Mobile-Driven Healthcare Support Services." Computer 51, no. 8: 14-17.
Stroke affects the mobility, hence the quality of life of people victim of this cerebrovascular disease. Part of research has been focusing on the development of exoskeletons bringing support to the user's joints to improve their gait and to help regaining independence in daily life. One example is Xosoft, a soft modular exoskeleton currently being developed in the framework of the European project of the same name. On top of its assistive properties, the soft exoskeleton will provide therapeutic feedback via the analysis of kinematic data stemming from inertial sensors mounted on the exoskeleton. Prior to these analyses however, the activities performed by the user must be known in order to have sufficient behavioral context to interpret the data. Four activity recognition chains, based on machine learning algorithm, were implemented to automatically identify the nature of the activities performed by the user. To be consistent with the application they are being used for (i.e. wearable exoskeleton), focus was made on reducing energy consumption by configuration minimization and bringing robustness to these algorithms. In this study, movement sensor data was collected from eleven stroke survivors while performing daily-life activities. From this data, we evaluated the influence of sensor reduction and position on the performances of the four algorithms. Moreover, we evaluated their resistance to sensor failures. Results show that in all four activity recognition chains, and for each patient, reduction of sensors is possible until a certain limit beyond which the position on the body has to be carefully chosen in order to maintain the same performance results. In particular, the study shows the benefits of avoiding lower legs and foot locations as well as the sensors positioned on the affected side of the stroke patient. It also shows that robustness can be brought to the activity recognition chain when the data stemming from the different sensors are fused at the very end of the classification process.
Fanny Recher; Oresti Banos; Corien D.M. Nikamp; Leendert Schaake; Chris T.M Baten; Jaap H. Buurkc. Optimizing Activity Recognition in Stroke Survivors for Wearable Exoskeletons. 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) 2018, 173 -178.
AMA StyleFanny Recher, Oresti Banos, Corien D.M. Nikamp, Leendert Schaake, Chris T.M Baten, Jaap H. Buurkc. Optimizing Activity Recognition in Stroke Survivors for Wearable Exoskeletons. 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). 2018; ():173-178.
Chicago/Turabian StyleFanny Recher; Oresti Banos; Corien D.M. Nikamp; Leendert Schaake; Chris T.M Baten; Jaap H. Buurkc. 2018. "Optimizing Activity Recognition in Stroke Survivors for Wearable Exoskeletons." 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) , no. : 173-178.
People living in both developed and developing countries face serious health challenges related to sedentary lifestyles. It is therefore essential to find new ways to improve health so that people can live longer and age well. With an ever-growing number of smart sensing systems developed and deployed across the globe, experts are primed to help coach people to have healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support, but also gives us an incentive to set goals and do more. This paper outlines some of the recent efforts made towards automatic and autonomous identification and coaching of troublesome behaviors to procure lasting, beneficial behavioral changes.
Oresti Banos; Hermie Hermens; Christopher Nugent; Hector Pomares. Smart Sensing Technologies for Personalised e-Coaching. Sensors 2018, 18, 1751 .
AMA StyleOresti Banos, Hermie Hermens, Christopher Nugent, Hector Pomares. Smart Sensing Technologies for Personalised e-Coaching. Sensors. 2018; 18 (6):1751.
Chicago/Turabian StyleOresti Banos; Hermie Hermens; Christopher Nugent; Hector Pomares. 2018. "Smart Sensing Technologies for Personalised e-Coaching." Sensors 18, no. 6: 1751.
Oresti Banos; Ramón Hervás. Ubiquitous computing for health applications. Journal of Ambient Intelligence and Humanized Computing 2018, 10, 2091 -2093.
AMA StyleOresti Banos, Ramón Hervás. Ubiquitous computing for health applications. Journal of Ambient Intelligence and Humanized Computing. 2018; 10 (6):2091-2093.
Chicago/Turabian StyleOresti Banos; Ramón Hervás. 2018. "Ubiquitous computing for health applications." Journal of Ambient Intelligence and Humanized Computing 10, no. 6: 2091-2093.
As the population ages, cognitive decline is becoming a worldwide threat to older adults’ independence and quality of life. Cognitive decline involves problems with memory, language, thinking and judgement, thus severely compromising multiple aspects of people’s everyday life. Diagnosis of cognitive disorders is currently performed through clinical questionnaire-based assessments, which are typically conducted by medical experts once symptoms appear. Digital technologies can help providing more immediate, pervasive and seamless assessment, which could, in turn, allow for much earlier diagnosis of cognitive disorders and decline. In this work, we present MobileCogniTracker, a digital tool for facilitating momentary, seamless and ubiquitous clinically-validated cognitive measurements. The proposed tool develops digital cognitive tests in the form of multimedia experience sampling questionnaires, which can run on a smartphone and can be scheduled and assessed remotely. The tool further integrates the digital cognitive experience sampling with passive smartphone sensor data streams that may be used to study the interplay of cognition and physical, social and emotional behaviours. The Mini-Mental State Examination test, a clinical questionnaire extensively used to measure cognitive disorders, has been particularly implemented here to showcase the possibilities offered by our tool. A usability test showed the tool to be usable for performing digital cognitive examinations, and that cognitively unimpaired persons in the relevant age-group are capable of performing such digital examination. A qualitative expert-driven validation also shows a high inter-reliability between the digital and pencil-and-paper version of the test.
Jan Wohlfahrt-Laymann; Hermie Hermens; Claudia Villalonga; Miriam Vollenbroek-Hutten; Oresti Banos. MobileCogniTracker. Journal of Ambient Intelligence and Humanized Computing 2018, 10, 2143 -2160.
AMA StyleJan Wohlfahrt-Laymann, Hermie Hermens, Claudia Villalonga, Miriam Vollenbroek-Hutten, Oresti Banos. MobileCogniTracker. Journal of Ambient Intelligence and Humanized Computing. 2018; 10 (6):2143-2160.
Chicago/Turabian StyleJan Wohlfahrt-Laymann; Hermie Hermens; Claudia Villalonga; Miriam Vollenbroek-Hutten; Oresti Banos. 2018. "MobileCogniTracker." Journal of Ambient Intelligence and Humanized Computing 10, no. 6: 2143-2160.
E-coaching is an emerging computing area in which intelligent systems are used to encourage progress toward specific health-related goals by providing tailored training and guidance. Progress in this field could well be a key enabler of increasing health span and well-being for our increasingly care-demanding society.
Oresti Banos; Christopher Nugent. E-Coaching for Health. Computer 2018, 51, 12 -15.
AMA StyleOresti Banos, Christopher Nugent. E-Coaching for Health. Computer. 2018; 51 (3):12-15.
Chicago/Turabian StyleOresti Banos; Christopher Nugent. 2018. "E-Coaching for Health." Computer 51, no. 3: 12-15.