595 research outputs found
Overview of context-sensitive technologies for well-being
Today smart devices such as smartphones, smartwatches and activity trackers are widely available and accepted in most developed societies. These devices present a broad set of sensors capable of extracting detailed information about different situations of daily life, which, if used for good, have the potential to improve the quality of life not only for individuals but also for the society in general. One of the key areas where this type of information can help to improve the quality of life is in healthcare since it allows to monitor and infer the current level of well-being of the smart devices carriers. In this paper, some of the available literature about well-being sensing through context-aware data is reviewed. Also, the main types of mechanisms used in these studies are identified. These mechanisms are related to monitoring, generalization, inference, feedback, energy management and privacy. Furthermore, a description of the mechanisms used in each study is presented.info:eu-repo/semantics/acceptedVersio
Overview of context-sensitive technologies for well-being
Today smart devices such as smartphones,
smartwatches and activity trackers are widely available and
accepted in most developed societies. These devices present a
broad set of sensors capable of extracting detailed information
about different situations of daily life, which, if used for good,
have the potential to improve the quality of life not only for
individuals but also for the society in general. One of the key
areas where this type of information can help to improve the
quality of life is in healthcare since it allows to monitor and infer
the current level of well-being of the smart devices carriers. In
this paper, some of the available literature about well-being
sensing through context-aware data is reviewed. Also, the main
types of mechanisms used in these studies are identified. These
mechanisms are related to monitoring, generalization, inference,
feedback, energy management and privacy. Furthermore, a
description of the mechanisms used in each study is presented.info:eu-repo/semantics/publishedVersio
BiHeartS: Bilateral Heart Rate from multiple devices and body positions for Sleep measurement Dataset
Sleep is the primary mean of recovery from accumulated fatigue and thus plays
a crucial role in fostering people's mental and physical well-being. Sleep
quality monitoring systems are often implemented using wearables that leverage
their sensing capabilities to provide sleep behaviour insights and
recommendations to users. Building models to estimate sleep quality from sensor
data is a challenging task, due to the variability of both physiological data,
perception of sleep quality, and the daily routine across users. This challenge
gauges the need for a comprehensive dataset that includes information about the
daily behaviour of users, physiological signals as well as the perceived sleep
quality. In this paper, we try to narrow this gap by proposing Bilateral Heart
rate from multiple devices and body positions for Sleep measurement (BiHeartS)
dataset. The dataset is collected in the wild from 10 participants for 30
consecutive nights. Both research-grade and commercial wearable devices are
included in the data collection campaign. Also, comprehensive self-reports are
collected about the sleep quality and the daily routine.Comment: 5 page
Mobile Phone and Wearable Sensor-Based mHealth Approach for Psychiatric Disorders and Symptoms : Systematic Review and Link to the m-RESIST Project
Background: Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration. Objective: To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy. Methods: A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied. Results: Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression. Conclusions: Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.Peer reviewe
Mobile Phone and Wearable Sensor-Based mHealth Approach for Psychiatric Disorders and Symptoms : Systematic Review and Link to the m-RESIST Project
Background: Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration. Objective: To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy. Methods: A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied. Results: Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression. Conclusions: Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.Peer reviewe
Having a Bad Day? Detecting the Impact of Atypical Life Events Using Wearable Sensors
Life events can dramatically affect our psychological state and work
performance. Stress, for example, has been linked to professional
dissatisfaction, increased anxiety, and workplace burnout. We explore the
impact of positive and negative life events on a number of psychological
constructs through a multi-month longitudinal study of hospital and aerospace
workers. Through causal inference, we demonstrate that positive life events
increase positive affect, while negative events increase stress, anxiety and
negative affect. While most events have a transient effect on psychological
states, major negative events, like illness or attending a funeral, can reduce
positive affect for multiple days. Next, we assess whether these events can be
detected through wearable sensors, which can cheaply and unobtrusively monitor
health-related factors. We show that these sensors paired with embedding-based
learning models can be used ``in the wild'' to capture atypical life events in
hundreds of workers across both datasets. Overall our results suggest that
automated interventions based on physiological sensing may be feasible to help
workers regulate the negative effects of life events.Comment: 10 pages, 4 figures, and 3 table
A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices
Repeated experiences of negative emotions, such as stress, anger or anxiety, can have long-term consequences for health. These episodes of negative emotion can be associated with inflammatory changes in the body, which are clinically relevant for the development of disease in the long-term. However, the development of effective coping strategies can mediate this causal chain. The proliferation of ubiquitous and unobtrusive sensor technology supports an increased awareness of those physiological states associated with negative emotion and supports the development of effective coping strategies. Smartphone and wearable devices utilise multiple on-board sensors that are capable of capturing daily behaviours in a permanent and comprehensive manner, which can be used as the basis for self-reflection and insight. However, there are a number of inherent challenges in this application, including unobtrusive monitoring, data processing, and analysis. This paper posits a mobile lifelogging platform that utilises wearable technology to monitor and classify levels of stress. A pilot study has been undertaken with six participants, who completed up to ten days of data collection. During this time, they wore a wearable device on the wrist during waking hours to collect instances of heart rate (HR) and Galvanic Skin Resistance (GSR). Preliminary data analysis was undertaken using three supervised machine learning algorithms: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Decision Tree (DT). An accuracy of 70% was achieved using the Decision Tree algorithm
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