Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological ...dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify selfreported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.
Today's health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients' symptoms (from temperature through blood pressure to ...complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self-assessment. This is due to the fact that mental illnesses manifest themselves mainly in the way patients behave throughout their daily life and, until recently there were no "behavior measurement devices." This is now changing with the progress in wearable activity recognition and sensor enabled smartphones. In this paper, we introduce a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder. Drawing upon a real-life dataset of ten patients, recorded over a time period of 12 weeks (in total over 800 days of data tracing 17 state changes) by four different sensing modalities, we could extract features corresponding to all disease-relevant aspects in behavior. Using these features, we gain recognition accuracies of 76% by fusing all sensor modalities and state change detection precision and recall of over 97%. This paper furthermore outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.
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 examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.
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 extend our previous work to other very different contexts, such as gas detection and grammatical face expression identification, in order to test its generality. The extensions of the method are presented in this paper. Our experimental results show that our extended model predicts the best fusion method well for a given data set, making us able to claim a broad generality for our sensor fusion method.
Fondazione Bruno Kessler is developing a mobile app prototype for empowering citizens to improve their health conditions through different lifestyle interventions that will be incorporated into a ...mobile application for lifestyle promotion of the Province of Trento in the context of the Trentino Salute 4.0 Competence Center. The envisioned interventions are based on promoting behaviour change in various domains such as physical activity, mental health and nutrition. In particular, the nutrition component is a self-monitoring module that collects dietary habits to analyse them and recommend healthier eating behaviours. Dietary assessment is completed using a Food Frequency Questionnaire on the Mediterranean diet that is presented to the user as a grid of images. The questionnaire returns feedback on 11 aspects of nutrition. Although the questionnaire used in the application only consists of 24 questions, it still could be a bit overwhelming and a bit crowded when shown on the screen. In this paper, we tried to find a machine-learning-based solution to reduce the number of questions in the questionnaire. We proposed a method that uses the user’s previous answers as additional information to find the goals that need more attention. We compared this method with a case where the subset of questions is randomly selected and with a case where the subset is chosen using feature selection. We also explored how large the subset should be to obtain good predictions. All the experiments are conducted as a multi-target regression problem, which means several goals are predicted simultaneously. The proposed method adjusts well to the user in question and has the slightest error when predicting the goals.
Editorial: Applications of Future Internet Mayora, Oscar; Sucar, L. Enrique
Journal on special topics in mobile networks and applications/Mobile networks and applications,
10/2019, Letnik:
24, Številka:
5
Journal Article
Virtual reality (VR) adventure games can offer ideal technological solutions for training social skills in adolescents with autism spectrum disorder (ASD), leveraging their support for multisensory ...and multiplayer interactions over distance, which may lower barriers to training access and increase user motivation. However, the design of VR-based game environments for social skills training is still understudied and deserves the deployment of an inclusive design approach to ensure its acceptability by target users.
We aimed to present the inclusive design process that we had followed to develop the Zentastic VR adventure game to foster social skills training in adolescents with ASD and to investigate its feasibility as a training environment for adolescents.
The VR game supports multiplayer training sessions involving small groups of adolescents and their therapists, who act as facilitators. Adolescents with ASD and their therapists were involved in the design and in an explorative acceptability study of an initial prototype of the gaming environment, as well as in a later feasibility multisession evaluation of the VR game final release.
The feasibility study demonstrated good acceptability of the VR game by adolescents and an enhancement of their social skills from baseline to posttraining.
The findings provide preliminary evidence of the benefits that VR-based games can bring to the training of adolescents with ASD and, potentially, other neurodevelopmental disorders.
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.
There is growing amount of scientific evidence that motor activity is the most consistent indicator of bipolar disorder. Motor activity includes several areas such as body movement, motor response ...time, level of psychomotor activity, and speech related motor activity. Studies of motor activity in bipolar disorder have typically used self-reported questionnaires with clinical observer-rated scales, which are therefore subjective and have often limited effectiveness. Motor activity information can be used to classify episode type in bipolar patients, which is highly relevant, since severe depression and manic states can result in mortality. This paper introduces a system able to classify the state of patients suffering from bipolar disorder using sensed information from smartphones. We collected audio, accelerometer and self-assessment data from five patients over a time-period of 12 weeks during their real-life activities. In this research we evaluated the performance of several classifiers, different sets of features and the role of the questionnaires for classifying bipolar disorder episodes. In particular, we have shown that it is possible to classify with high confidence (≈85%) the course of mood episodes or relapse in bipolar patients. To our knowledge, no research to date has focused on naturalistic observation of day-to-day phone conversation to classify impaired life functioning in individuals with bipolar disorder.
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 the prototype usability, asking for some improvements of the user experience and for a more transparent presentation of the platform security and privacy preserving capabilities. Participants expressed interest in using the platform as data providers and consumers as well as setting privacy policies for sharing data products with third parties, including the possibility of revoking access to data. Conclusions: Blockchain-based data sharing platforms are more likely to engage target users when technical design is informed by a deeper knowledge of their needs, expectations, and relevant concerns.
PDF
