Oscar Mayora - Profile on Academia.edu (original) (raw)

Papers by Oscar Mayora

Research paper thumbnail of Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

Research paper thumbnail of Paving the Way for a Pervasive, User-centered and Preventive Healthcare Model

Paving the Way for a Pervasive, User-centered and Preventive Healthcare Model

Research paper thumbnail of Multidimensional Study on Users’ Evaluation of the KRAKEN Personal Data Sharing Platform

Applied Sciences

Background: Recent advances in the design of blockchain-based personal data sharing platforms bri... more Background: Recent advances in the design of blockchain-based personal data sharing platforms bring the benefit of empowering users with more control and privacy-preserving measures in sharing data products. However, so far very little is known about users’ intentions to adopt such platforms for providing or consuming data products. Objective: This study aims to investigate users’ main expectations, preferences, and concerns regarding the adoption of blockchain-based personal data sharing platforms in the health and education domains. Methods: Fifteen participants were involved in a multidimensional evaluation of a prototyped release of the KRAKEN blockchain-based data sharing platform and asked to assess it in the health or education pilot domains. Data collected during online group interviews with participants were analyzed by applying the micro interlocutor technique to provide a descriptive overview of participant responses. Results: Participants showed a marginal acceptance of ...

Research paper thumbnail of Enabling prescription-based health apps

Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2017

We describe an innovative framework for prescription of personalised health apps by integrating P... more We describe an innovative framework for prescription of personalised health apps by integrating Personal Health Records (PHR) with disease-specific mobile applications for managing medical conditions and the communication with clinical professionals. The prescribed apps record multiple variables including medical history enriched with innovative features such as integration with medical monitoring devices and wellbeing trackers to provide patients and clinicians with a personalised support on disease management. Our framework is based on an existing PHR ecosystem called TreC, uniquely positioned between healthcare provider and the patients, which is being used by over 70.000 patients in Trentino region in Northern Italy. We also describe three important aspects of health app prescription and how medical information is automatically encoded through the TreC framework and is prescribed as a personalised app, ready to be installed in the patients' smartphone.

Research paper thumbnail of Participants’ Experience and Adherence in Repeated Measurement Studies Among Office-Based Workers

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, 2021

While diary studies, especially when applying ecological momentary assessment (EMA), are a great ... more While diary studies, especially when applying ecological momentary assessment (EMA), are a great way to capture self perceptions to later use as labels for other data, they can be a burden for study participants. To increase their adherence to the study design, it is important to tailor it to their needs and take their feedback into account. This paper reports on a data collection process in a study focused on occupational stress. The data collection is briefly described and the participants' responses are analysed in terms of adherence. Participants' feedback was collected at the end of the study and its main themes are summarized. These experiences are compared to the ones in another study focusing on stress and burnout, with a very similar methodological design. Some general conclusions are drawn from both with suggestions on how to best carry out an EMA study. CCS CONCEPTS • Human-centered computing → Empirical studies in ubiquitous and mobile computing; Ubiquitous and mobile computing design and evaluation methods; • Applied computing → Psychology.

Research paper thumbnail of Use of eHealth Platforms and Apps to Support Monitoring and Management of Home-Quarantined Patients With COVID-19 in the Province of Trento, Italy: App Development and Implementation

JMIR Formative Research, 2021

Background Italy was the first country to largely experience the COVID-19 epidemic among other We... more Background Italy was the first country to largely experience the COVID-19 epidemic among other Western countries during the so-called first wave of the COVID-19 pandemic. Proper management of an increasing number of home-quarantined individuals created a significant challenge for health care authorities and professionals. This was especially true when considering the importance of remote surveillance to detect signs of disease progression and consequently regulate access to hospitals and intensive care units on a priority basis. Objective In this paper, we report on an initiative promoted to cope with the first wave of the COVID-19 epidemic in the Spring/Summer of 2020, in the Autonomous Province of Trento, Italy. A purposefully built app named TreCovid19 was designed to provide dedicated health care staff with a ready-to-use tool for remotely monitoring patients with progressive symptoms of COVID-19, who were home-quarantined during the first wave of the epidemic, and to focus on t...

Research paper thumbnail of Choosing the Best Sensor Fusion Method: A Machine-Learning Approach

Sensors, 2020

Multi-sensor fusion refers to methods used for combining information coming from several sensors ... more Multi-sensor fusion refers to methods used for combining information coming from several sensors (in some cases, different ones) with the aim to make one sensor compensate for the weaknesses of others or to improve the overall accuracy or the reliability of a decision-making process. Indeed, this area has made progress, and the combined use of several sensors has been so successful that many authors proposed variants of fusion methods, to the point that it is now hard to tell which of them is the best for a given set of sensors and a given application context. To address the issue of choosing an adequate fusion method, we recently proposed a machine-learning data-driven approach able to predict the best merging strategy. This approach uses a meta-data set with the Statistical signatures extracted from data sets of a particular domain, from which we train a prediction model. However, the mentioned work is restricted to the recognition of human activities. In this paper, we propose to...

Research paper thumbnail of Unobtrusive Stress Assessment Using Smartphones

IEEE Transactions on Mobile Computing, 2020

Stress assessment is a complex issue and numerous studies have examined factors that influence st... more Stress assessment is a complex issue and numerous studies have examined factors that influence stress in working environments. Research studies have shown that monitoring individuals' behaviour parameters during daily life can also help assess stress levels. In this study, we examine assessment of work-related stress using features derived from sensors in smartphones. In particular, we use information from physical activity levels, location, social-interactions, social-activity and application usage during working days. Our study included 30 employees chosen from two different private companies, monitored over a period of 8 weeks in real work environments. The findings suggest that information from phone sensors shows important correlation with employees perceived stress level. Secondly, we used machine learning methods to classify perceived stress levels based on the analysis of information provided by smartphones. We used decision trees obtaining 67.57% accuracy and 71.73% after applying a semi-supervised method. Our results show that stress levels can be monitored in unobtrusive manner, through analysis of smartphone data.

Research paper thumbnail of Multi-Sensor Fusion for Activity Recognition—A Survey

Sensors, 2019

In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the cont... more In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examin...

Research paper thumbnail of Virtual Sensors for Optimal Integration of Human Activity Data

Sensors, 2019

Sensors are becoming more and more ubiquitous as their price and availability continue to improve... more Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as “virtual sensors”, in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the “signatures” of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality.

Research paper thumbnail of Pervasive or Ubiquitous Healthcare?

Methods of Information in Medicine, 2010

Research paper thumbnail of Smartphone-based self-monitoring in bipolar disorder: evaluation of usability and feasibility of two systems

International Journal of Bipolar Disorders, 2019

Background: The aims of the present multicenter pilot study were to examine the feasibility and u... more Background: The aims of the present multicenter pilot study were to examine the feasibility and usability of two different smartphone-based monitoring systems (the Pulso system and the Trilogis-Monsenso system) from two IT companies in patients with bipolar disorder, developed and selected to be testes as a part of a European Union funded Pre-Commercial Procurement (the NYMPHA-MD project). Methods: Patients with bipolar disorder (ICD-10), > 18 years of age during a remitted, partial remitted or mild to moderate depressive state (HDRS-17 < 25) from Italy, Spain and Denmark were included. Patients were randomized 1:1 to the use of one of two smartphone-based monitoring systems. The randomization was stratified according to study location (Italy, Spain, Denmark) and all patients were followed for a 4 weeks study period. Usability and feasibility were evaluated using the Computer System Usability Questionnaire, and the Usefulness, Satisfaction, and Ease of use Questionnaire. Results: A total of 60 patients aged 18-69 years with bipolar disorder (ICD-10) recruited from Italy, Spain, Denmark were included-59 patients completed the study. In Denmark, the patients evaluated the Trilogis-Monsenso system with a statistically significant higher usability compared with the Pulso system. In Italy and Spain, the patients evaluated no statistically significant difference between the two systems in any of the categories, except for the usefulness category favoring the Trilogis-Monsenso system (z = 2.68, p < 0.01). Conclusions: Both monitoring systems showed acceptable usability and feasibility. There were differences in patient-based evaluations of the two monitoring systems related to the country of the study. Studies investigating the usability and feasibility during longer follow-up periods could perhaps reveal different findings. Future randomized controlled trials investigating the possible positive and negative effects of smartphone-based monitoring systems are needed.

Research paper thumbnail of Wearable Therapy - Detecting Information from Wearables and Mobiles that are Relevant to Clinical and Self-directed Therapy

Methods of information in medicine, Jan 9, 2017

This accompanying editorial provides a brief introduction into the focus theme "Wearable The... more This accompanying editorial provides a brief introduction into the focus theme "Wearable Therapy". The focus theme "Wearable Therapy" aims to present contributions which target wearable and mobile technologies to support clinical and self-directed therapy. A call for papers was announced to all participants of the "9th International Conference on Pervasive Computing Technologies for Healthcare" and was published in November 2015. A peer review process was conducted to select the papers for the focus theme. Six papers were selected to be included in this focus theme. The paper topics cover a broad range including an approach to build a health informatics research program, a comprehensive literature review of self-quantification for health self-management, methods for affective state detection of informal care givers, social-aware handling of falls, smart shoes for supporting self-directed therapy of alcohol addicts, and reference information model for pe...

Research paper thumbnail of Using Intermediate Models and Knowledge Learning to Improve Stress Prediction

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2016

Motor activity in physical and psychological stress exposure has been studied almost exclusively ... more Motor activity in physical and psychological stress exposure has been studied almost exclusively with self-assessment questionnaires and from reports that derive from human observer, such as verbal rating and simple descriptive scales. However, these methods are limited in objectively quantifying typical behaviour of stress. We propose to use accelerometer data from smartphones to objectively quantify stress levels. Used data was collected in real-world setting, from 29 employees in two different organisations over 5 weeks. To improve classification performance we propose to use intermediate models. These intermediate models represent the mood state of a person which is used to build the final stress prediction model. In particular, we obtained an accuracy of 78.2% to classify stress levels.

Research paper thumbnail of Stress modelling and prediction in presence of scarce data

Journal of Biomedical Informatics, 2016

Objective: Stress at work is a significant occupational health concern. Recent studies have used ... more Objective: Stress at work is a significant occupational health concern. Recent studies have used various sensing modalities to model stress behaviour based on non-obtrusive data obtained from smartphones. However, when the data for a subject is scarce it becomes a challenge to obtain a good model. Methods: We propose an approach based on a combination of techniques: semi-supervised learning, ensemble methods and transfer learning to build a model of a subject with scarce data. Our approach is based on the comparison of decision trees to select the closest subject for knowledge transfer. Results: We present a real-life, unconstrained study carried out with 30 employees within two organisations. The results show that using information (instances or model) from similar subjects can improve the accuracy of the subjects with scarce data. However, using transfer learning from dissimilar subjects can have a detrimental effect on the accuracy. Our proposed ensemble approach increased the accuracy by %10% to 71.58% compared to not using any transfer learning technique. Conclusions: In contrast to high precision but highly obtrusive sensors, using smartphone sensors for measuring daily behaviours allowed us to quantify behaviour changes, relevant to occupational stress. Furthermore, we have shown that use of transfer learning to select data from close models is a useful approach to improve accuracy in presence of scarce data.

Research paper thumbnail of Stress Modelling Using Transfer Learning in Presence of Scarce Data

Lecture Notes in Computer Science, 2015

Stress at work is a significant occupational health concern nowadays. Thus, researchers are looki... more Stress at work is a significant occupational health concern nowadays. Thus, researchers are looking to find comprehensive approaches for improving wellness interventions relevant to stress. Recent studies have been conducted for inferring stress in labour settings; they model stress behaviour based on non-obtrusive data obtained from smartphones. However, if the data for a subject is scarce, a good model cannot be obtained. We propose an approach based on transfer learning for building a model of a subject with scarce data. It is based on the comparison of decision trees to select the closest subject for knowledge transfer. We present an study carried out on 30 employees within two organisations. The results show that the in the case of identifying a "similar" subject, the classification accuracy is improved via transfer learning.

Research paper thumbnail of Utilizing Smartphones as an Effective Way to Support Patients with Bipolar Disorder: Results of the Monarca Study

Utilizing Smartphones as an Effective Way to Support Patients with Bipolar Disorder: Results of the Monarca Study

European Psychiatry, 2015

Background Bipolar disorder is characterized by depressive and manic episodes, each with its own ... more Background Bipolar disorder is characterized by depressive and manic episodes, each with its own specific outcomes. To guarantee the best therapy it is important and necessary to assess the episodes of the disease and its exact degree of severity at an early stage. Methods During a time period of 12 weeks, 9 patients suffering from bipolar disorder were provided with a commercially available smartphone in order to collect behavioral patterns by the phone's internal sensors. These sensors included acceleration, GPS-traces, phone-call behavior and sound. During the trial the patients were also asked to fill out a daily self-assessment questionnaire that included a self-rating. Additionally, to gain ground truth psychological state examinations were performed every three weeks. Results The sensor traces are very similar to the diagnosed scores and thus clearly provide an accurate representation of the patient's state. Further, our data suggest a strong empirical evidence that the sensor based data are, on average, a more reliable and objective way of monitoring the mental state and mood than the patient's self-assessment. Conclusion The MONARCA system introduces new opportunities for the treatment of patients with bipolar disorder. The acquired data allow for identifying changes in the patient's condition at an early stage and therefore support the timely intervention by psychiatrists.

Research paper thumbnail of Mobile Health Systems for Bipolar Disorder

International Journal of Handheld Computing Research, 2014

This paper presents a series of challenges for developing mobile health solutions for mental heal... more This paper presents a series of challenges for developing mobile health solutions for mental health as a result of MONARCA project three-year activities. The lessons learnt on the design, development and evaluation of a mobile health system for supporting the treatment of bipolar disorder. The findings presented here are the result of over 3 years of activity within the MONARCA EU project. The challenges listed and detailed in this paper may be used in future research as a starting point for identifying important non-functional requirements involved in mobile health provisioning that are fundamental for the successful implementation of mobile health services in real life contexts.

Research paper thumbnail of Sensor Monitoring in the Home: Giving Voice to Elderly People

Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques, 2013

This paper describes the approach used to identify elderly people's needs and attitudes towards a... more This paper describes the approach used to identify elderly people's needs and attitudes towards applying ambient sensor systems for monitoring daily activities in the home. As elderly are typically unfamiliar with such ambient technology, interactive tools for explicating sensor monitoring-an interactive dollhouse and iPad applications for displaying live monitored sensor activity data-were developed and used for this study. Furthermore, four studies conducted by occupational therapists with more than 60 elderly participants-including questionnaires (n=41), interviews (n=6), user sessions (n=14) and field studies (n=2)-were conducted. The experiences from these studies suggest that this approach helped to democratically engage the elderly as end-user and identify acceptance issues.

Research paper thumbnail of Mobile monitoring of formal and informal social interactions at workplace

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014

This paper proposes using mobile technologies to provide an insight into social context at workpl... more This paper proposes using mobile technologies to provide an insight into social context at workplace. It provides takeaways for extracting features that are relevant for interpreting social context and types of social interactions, formal or informal. Our approach uses mobile phones and accelerometers to detect interpersonal spatial and speech related features, achieving accuracy of around 80% in classifying between formal and informal social interactions, based on the study of 53 social interactions. One of the potential impacts of this work is on studying communication channels to enable more efficient knowledge transfer between knowledge workers. There is an ongoing debate in social sciences whether formal or informal social interactions foster productivity more. However, the consensus is that improving communication between workers requires deeper understanding of both formal and informal types of interactions.

Research paper thumbnail of Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

Research paper thumbnail of Paving the Way for a Pervasive, User-centered and Preventive Healthcare Model

Paving the Way for a Pervasive, User-centered and Preventive Healthcare Model

Research paper thumbnail of Multidimensional Study on Users’ Evaluation of the KRAKEN Personal Data Sharing Platform

Applied Sciences

Background: Recent advances in the design of blockchain-based personal data sharing platforms bri... more Background: Recent advances in the design of blockchain-based personal data sharing platforms bring the benefit of empowering users with more control and privacy-preserving measures in sharing data products. However, so far very little is known about users’ intentions to adopt such platforms for providing or consuming data products. Objective: This study aims to investigate users’ main expectations, preferences, and concerns regarding the adoption of blockchain-based personal data sharing platforms in the health and education domains. Methods: Fifteen participants were involved in a multidimensional evaluation of a prototyped release of the KRAKEN blockchain-based data sharing platform and asked to assess it in the health or education pilot domains. Data collected during online group interviews with participants were analyzed by applying the micro interlocutor technique to provide a descriptive overview of participant responses. Results: Participants showed a marginal acceptance of ...

Research paper thumbnail of Enabling prescription-based health apps

Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2017

We describe an innovative framework for prescription of personalised health apps by integrating P... more We describe an innovative framework for prescription of personalised health apps by integrating Personal Health Records (PHR) with disease-specific mobile applications for managing medical conditions and the communication with clinical professionals. The prescribed apps record multiple variables including medical history enriched with innovative features such as integration with medical monitoring devices and wellbeing trackers to provide patients and clinicians with a personalised support on disease management. Our framework is based on an existing PHR ecosystem called TreC, uniquely positioned between healthcare provider and the patients, which is being used by over 70.000 patients in Trentino region in Northern Italy. We also describe three important aspects of health app prescription and how medical information is automatically encoded through the TreC framework and is prescribed as a personalised app, ready to be installed in the patients' smartphone.

Research paper thumbnail of Participants’ Experience and Adherence in Repeated Measurement Studies Among Office-Based Workers

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, 2021

While diary studies, especially when applying ecological momentary assessment (EMA), are a great ... more While diary studies, especially when applying ecological momentary assessment (EMA), are a great way to capture self perceptions to later use as labels for other data, they can be a burden for study participants. To increase their adherence to the study design, it is important to tailor it to their needs and take their feedback into account. This paper reports on a data collection process in a study focused on occupational stress. The data collection is briefly described and the participants' responses are analysed in terms of adherence. Participants' feedback was collected at the end of the study and its main themes are summarized. These experiences are compared to the ones in another study focusing on stress and burnout, with a very similar methodological design. Some general conclusions are drawn from both with suggestions on how to best carry out an EMA study. CCS CONCEPTS • Human-centered computing → Empirical studies in ubiquitous and mobile computing; Ubiquitous and mobile computing design and evaluation methods; • Applied computing → Psychology.

Research paper thumbnail of Use of eHealth Platforms and Apps to Support Monitoring and Management of Home-Quarantined Patients With COVID-19 in the Province of Trento, Italy: App Development and Implementation

JMIR Formative Research, 2021

Background Italy was the first country to largely experience the COVID-19 epidemic among other We... more Background Italy was the first country to largely experience the COVID-19 epidemic among other Western countries during the so-called first wave of the COVID-19 pandemic. Proper management of an increasing number of home-quarantined individuals created a significant challenge for health care authorities and professionals. This was especially true when considering the importance of remote surveillance to detect signs of disease progression and consequently regulate access to hospitals and intensive care units on a priority basis. Objective In this paper, we report on an initiative promoted to cope with the first wave of the COVID-19 epidemic in the Spring/Summer of 2020, in the Autonomous Province of Trento, Italy. A purposefully built app named TreCovid19 was designed to provide dedicated health care staff with a ready-to-use tool for remotely monitoring patients with progressive symptoms of COVID-19, who were home-quarantined during the first wave of the epidemic, and to focus on t...

Research paper thumbnail of Choosing the Best Sensor Fusion Method: A Machine-Learning Approach

Sensors, 2020

Multi-sensor fusion refers to methods used for combining information coming from several sensors ... more Multi-sensor fusion refers to methods used for combining information coming from several sensors (in some cases, different ones) with the aim to make one sensor compensate for the weaknesses of others or to improve the overall accuracy or the reliability of a decision-making process. Indeed, this area has made progress, and the combined use of several sensors has been so successful that many authors proposed variants of fusion methods, to the point that it is now hard to tell which of them is the best for a given set of sensors and a given application context. To address the issue of choosing an adequate fusion method, we recently proposed a machine-learning data-driven approach able to predict the best merging strategy. This approach uses a meta-data set with the Statistical signatures extracted from data sets of a particular domain, from which we train a prediction model. However, the mentioned work is restricted to the recognition of human activities. In this paper, we propose to...

Research paper thumbnail of Unobtrusive Stress Assessment Using Smartphones

IEEE Transactions on Mobile Computing, 2020

Stress assessment is a complex issue and numerous studies have examined factors that influence st... more Stress assessment is a complex issue and numerous studies have examined factors that influence stress in working environments. Research studies have shown that monitoring individuals' behaviour parameters during daily life can also help assess stress levels. In this study, we examine assessment of work-related stress using features derived from sensors in smartphones. In particular, we use information from physical activity levels, location, social-interactions, social-activity and application usage during working days. Our study included 30 employees chosen from two different private companies, monitored over a period of 8 weeks in real work environments. The findings suggest that information from phone sensors shows important correlation with employees perceived stress level. Secondly, we used machine learning methods to classify perceived stress levels based on the analysis of information provided by smartphones. We used decision trees obtaining 67.57% accuracy and 71.73% after applying a semi-supervised method. Our results show that stress levels can be monitored in unobtrusive manner, through analysis of smartphone data.

Research paper thumbnail of Multi-Sensor Fusion for Activity Recognition—A Survey

Sensors, 2019

In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the cont... more In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examin...

Research paper thumbnail of Virtual Sensors for Optimal Integration of Human Activity Data

Sensors, 2019

Sensors are becoming more and more ubiquitous as their price and availability continue to improve... more Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as “virtual sensors”, in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the “signatures” of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality.

Research paper thumbnail of Pervasive or Ubiquitous Healthcare?

Methods of Information in Medicine, 2010

Research paper thumbnail of Smartphone-based self-monitoring in bipolar disorder: evaluation of usability and feasibility of two systems

International Journal of Bipolar Disorders, 2019

Background: The aims of the present multicenter pilot study were to examine the feasibility and u... more Background: The aims of the present multicenter pilot study were to examine the feasibility and usability of two different smartphone-based monitoring systems (the Pulso system and the Trilogis-Monsenso system) from two IT companies in patients with bipolar disorder, developed and selected to be testes as a part of a European Union funded Pre-Commercial Procurement (the NYMPHA-MD project). Methods: Patients with bipolar disorder (ICD-10), > 18 years of age during a remitted, partial remitted or mild to moderate depressive state (HDRS-17 < 25) from Italy, Spain and Denmark were included. Patients were randomized 1:1 to the use of one of two smartphone-based monitoring systems. The randomization was stratified according to study location (Italy, Spain, Denmark) and all patients were followed for a 4 weeks study period. Usability and feasibility were evaluated using the Computer System Usability Questionnaire, and the Usefulness, Satisfaction, and Ease of use Questionnaire. Results: A total of 60 patients aged 18-69 years with bipolar disorder (ICD-10) recruited from Italy, Spain, Denmark were included-59 patients completed the study. In Denmark, the patients evaluated the Trilogis-Monsenso system with a statistically significant higher usability compared with the Pulso system. In Italy and Spain, the patients evaluated no statistically significant difference between the two systems in any of the categories, except for the usefulness category favoring the Trilogis-Monsenso system (z = 2.68, p < 0.01). Conclusions: Both monitoring systems showed acceptable usability and feasibility. There were differences in patient-based evaluations of the two monitoring systems related to the country of the study. Studies investigating the usability and feasibility during longer follow-up periods could perhaps reveal different findings. Future randomized controlled trials investigating the possible positive and negative effects of smartphone-based monitoring systems are needed.

Research paper thumbnail of Wearable Therapy - Detecting Information from Wearables and Mobiles that are Relevant to Clinical and Self-directed Therapy

Methods of information in medicine, Jan 9, 2017

This accompanying editorial provides a brief introduction into the focus theme "Wearable The... more This accompanying editorial provides a brief introduction into the focus theme "Wearable Therapy". The focus theme "Wearable Therapy" aims to present contributions which target wearable and mobile technologies to support clinical and self-directed therapy. A call for papers was announced to all participants of the "9th International Conference on Pervasive Computing Technologies for Healthcare" and was published in November 2015. A peer review process was conducted to select the papers for the focus theme. Six papers were selected to be included in this focus theme. The paper topics cover a broad range including an approach to build a health informatics research program, a comprehensive literature review of self-quantification for health self-management, methods for affective state detection of informal care givers, social-aware handling of falls, smart shoes for supporting self-directed therapy of alcohol addicts, and reference information model for pe...

Research paper thumbnail of Using Intermediate Models and Knowledge Learning to Improve Stress Prediction

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2016

Motor activity in physical and psychological stress exposure has been studied almost exclusively ... more Motor activity in physical and psychological stress exposure has been studied almost exclusively with self-assessment questionnaires and from reports that derive from human observer, such as verbal rating and simple descriptive scales. However, these methods are limited in objectively quantifying typical behaviour of stress. We propose to use accelerometer data from smartphones to objectively quantify stress levels. Used data was collected in real-world setting, from 29 employees in two different organisations over 5 weeks. To improve classification performance we propose to use intermediate models. These intermediate models represent the mood state of a person which is used to build the final stress prediction model. In particular, we obtained an accuracy of 78.2% to classify stress levels.

Research paper thumbnail of Stress modelling and prediction in presence of scarce data

Journal of Biomedical Informatics, 2016

Objective: Stress at work is a significant occupational health concern. Recent studies have used ... more Objective: Stress at work is a significant occupational health concern. Recent studies have used various sensing modalities to model stress behaviour based on non-obtrusive data obtained from smartphones. However, when the data for a subject is scarce it becomes a challenge to obtain a good model. Methods: We propose an approach based on a combination of techniques: semi-supervised learning, ensemble methods and transfer learning to build a model of a subject with scarce data. Our approach is based on the comparison of decision trees to select the closest subject for knowledge transfer. Results: We present a real-life, unconstrained study carried out with 30 employees within two organisations. The results show that using information (instances or model) from similar subjects can improve the accuracy of the subjects with scarce data. However, using transfer learning from dissimilar subjects can have a detrimental effect on the accuracy. Our proposed ensemble approach increased the accuracy by %10% to 71.58% compared to not using any transfer learning technique. Conclusions: In contrast to high precision but highly obtrusive sensors, using smartphone sensors for measuring daily behaviours allowed us to quantify behaviour changes, relevant to occupational stress. Furthermore, we have shown that use of transfer learning to select data from close models is a useful approach to improve accuracy in presence of scarce data.

Research paper thumbnail of Stress Modelling Using Transfer Learning in Presence of Scarce Data

Lecture Notes in Computer Science, 2015

Stress at work is a significant occupational health concern nowadays. Thus, researchers are looki... more Stress at work is a significant occupational health concern nowadays. Thus, researchers are looking to find comprehensive approaches for improving wellness interventions relevant to stress. Recent studies have been conducted for inferring stress in labour settings; they model stress behaviour based on non-obtrusive data obtained from smartphones. However, if the data for a subject is scarce, a good model cannot be obtained. We propose an approach based on transfer learning for building a model of a subject with scarce data. It is based on the comparison of decision trees to select the closest subject for knowledge transfer. We present an study carried out on 30 employees within two organisations. The results show that the in the case of identifying a "similar" subject, the classification accuracy is improved via transfer learning.

Research paper thumbnail of Utilizing Smartphones as an Effective Way to Support Patients with Bipolar Disorder: Results of the Monarca Study

Utilizing Smartphones as an Effective Way to Support Patients with Bipolar Disorder: Results of the Monarca Study

European Psychiatry, 2015

Background Bipolar disorder is characterized by depressive and manic episodes, each with its own ... more Background Bipolar disorder is characterized by depressive and manic episodes, each with its own specific outcomes. To guarantee the best therapy it is important and necessary to assess the episodes of the disease and its exact degree of severity at an early stage. Methods During a time period of 12 weeks, 9 patients suffering from bipolar disorder were provided with a commercially available smartphone in order to collect behavioral patterns by the phone's internal sensors. These sensors included acceleration, GPS-traces, phone-call behavior and sound. During the trial the patients were also asked to fill out a daily self-assessment questionnaire that included a self-rating. Additionally, to gain ground truth psychological state examinations were performed every three weeks. Results The sensor traces are very similar to the diagnosed scores and thus clearly provide an accurate representation of the patient's state. Further, our data suggest a strong empirical evidence that the sensor based data are, on average, a more reliable and objective way of monitoring the mental state and mood than the patient's self-assessment. Conclusion The MONARCA system introduces new opportunities for the treatment of patients with bipolar disorder. The acquired data allow for identifying changes in the patient's condition at an early stage and therefore support the timely intervention by psychiatrists.

Research paper thumbnail of Mobile Health Systems for Bipolar Disorder

International Journal of Handheld Computing Research, 2014

This paper presents a series of challenges for developing mobile health solutions for mental heal... more This paper presents a series of challenges for developing mobile health solutions for mental health as a result of MONARCA project three-year activities. The lessons learnt on the design, development and evaluation of a mobile health system for supporting the treatment of bipolar disorder. The findings presented here are the result of over 3 years of activity within the MONARCA EU project. The challenges listed and detailed in this paper may be used in future research as a starting point for identifying important non-functional requirements involved in mobile health provisioning that are fundamental for the successful implementation of mobile health services in real life contexts.

Research paper thumbnail of Sensor Monitoring in the Home: Giving Voice to Elderly People

Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques, 2013

This paper describes the approach used to identify elderly people's needs and attitudes towards a... more This paper describes the approach used to identify elderly people's needs and attitudes towards applying ambient sensor systems for monitoring daily activities in the home. As elderly are typically unfamiliar with such ambient technology, interactive tools for explicating sensor monitoring-an interactive dollhouse and iPad applications for displaying live monitored sensor activity data-were developed and used for this study. Furthermore, four studies conducted by occupational therapists with more than 60 elderly participants-including questionnaires (n=41), interviews (n=6), user sessions (n=14) and field studies (n=2)-were conducted. The experiences from these studies suggest that this approach helped to democratically engage the elderly as end-user and identify acceptance issues.

Research paper thumbnail of Mobile monitoring of formal and informal social interactions at workplace

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014

This paper proposes using mobile technologies to provide an insight into social context at workpl... more This paper proposes using mobile technologies to provide an insight into social context at workplace. It provides takeaways for extracting features that are relevant for interpreting social context and types of social interactions, formal or informal. Our approach uses mobile phones and accelerometers to detect interpersonal spatial and speech related features, achieving accuracy of around 80% in classifying between formal and informal social interactions, based on the study of 53 social interactions. One of the potential impacts of this work is on studying communication channels to enable more efficient knowledge transfer between knowledge workers. There is an ongoing debate in social sciences whether formal or informal social interactions foster productivity more. However, the consensus is that improving communication between workers requires deeper understanding of both formal and informal types of interactions.