3,392 research outputs found
TOBE: Tangible Out-of-Body Experience
We propose a toolkit for creating Tangible Out-of-Body Experiences: exposing
the inner states of users using physiological signals such as heart rate or
brain activity. Tobe can take the form of a tangible avatar displaying live
physiological readings to reflect on ourselves and others. Such a toolkit could
be used by researchers and designers to create a multitude of potential
tangible applications, including (but not limited to) educational tools about
Science Technologies Engineering and Mathematics (STEM) and cognitive science,
medical applications or entertainment and social experiences with one or
several users or Tobes involved. Through a co-design approach, we investigated
how everyday people picture their physiology and we validated the acceptability
of Tobe in a scientific museum. We also give a practical example where two
users relax together, with insights on how Tobe helped them to synchronize
their signals and share a moment
Wireless body sensor networks for health-monitoring applications
This is an author-created, un-copyedited version of an article accepted for publication in
Physiological Measurement. The publisher is
not responsible for any errors or omissions in this version of the manuscript or any version
derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01
Activity-aware Stress Sensory System
Continuous stress monitoring may able to help analyzing and enhance the awareness of an individual on their stress patterns and provide more reliable data information for physicians in interventions. In the past years research, studies on mental stress sensory system were limited inside laboratory environment. However, excluding the effects of physical activity can be impractical while developing a wearable stress sensory system for daily use. In this project, effects of external factors from environment on Galvanic Skin Response (GSR) measurements and integration of several stress sensory system were studied. Electrocardiogram (ECG), GSR, and Activity Recognition System (ARS) were studied under different physical activities: sitting, standing, lying and walking. It is showed from the studies that an overall accuracy of 94.7% in ARS is achieved by using two sensor node system (at thigh and ankle each) which an improvement of 27.3% from using single sensor node system. It is further demonstrated that ARS could help improve in accuracy of wearable stress sensory system
Complexity index from a personalized wearable monitoring system for assessing remission in mental health
This study discusses a personalized wearable monitoring system, which provides information and communication technologies to patients with mental disorders and physicians managing such diseases. The system, hereinafter called the PSYCHE system, is mainly comprised of a comfortable t-shirt with embedded sensors, such as textile electrodes, to monitor electrocardiogram-heart rate variability (HRV) series, piezoresistive sensors for respiration activity, and triaxial accelerometers for activity recognition. Moreover, on the patient-side, the PSYCHE system uses a smartphone-based interactive platform for electronic mood agenda and clinical scale administration, whereas on the physician-side provides data visualization and support to clinical decision. The smartphone collects the physiological and behavioral data and sends the information out to a centralized server for further processing. In this study, we present experimental results gathered from ten bipolar patients, wearing the PSYCHE system, with severe symptoms who exhibited mood states among depression (DP), hypomania(HM), mixed state (MX), and euthymia (EU), i.e., the good affective balance. In analyzing more than 400 h of cardiovascular dynamics, we found that patients experiencing mood transitions from a pathological mood state (HM, DP, or MX - where depressive and hypomanic symptoms are simultaneously present) to EU can be characterized through a commonly used measure of entropy. In particular, the SampEn estimated on long-term HRV series increases according to the patients' clinical improvement. These results are in agreement with the current literature reporting on the complexity dynamics of physiological systems and provides a promising and viable support to clinical decision in order to improve the diagnosis and management of psychiatric disorders
Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients
This is the Accepted Manuscript version of the following article: I. Mporas, D. Triantafyllopoulos, V. Megalooikonomou, âReal-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patientsâ, Journal of Medical Systems, Vol. 40(45), December 2015. The final published versions is available at: https://link.springer.com/article/10.1007%2Fs10916-015-0403-3 © Springer Science+Business Media New York 2015.New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patientâs physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data.Peer reviewedFinal Accepted Versio
An intelligent information forwarder for healthcare big data systems with distributed wearable sensors
© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed
Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step
Increase in workload across many organisations and consequent increase in
occupational stress is negatively affecting the health of the workforce.
Measuring stress and other human psychological dynamics is difficult due to
subjective nature of self- reporting and variability between and within
individuals. With the advent of smartphones it is now possible to monitor
diverse aspects of human behaviour, including objectively measured behaviour
related to psychological state and consequently stress. We have used data from
the smartphone's built-in accelerometer to detect behaviour that correlates
with subjects stress levels. Accelerometer sensor was chosen because it raises
fewer privacy concerns (in comparison to location, video or audio recording,
for example) and because its low power consumption makes it suitable to be
embedded in smaller wearable devices, such as fitness trackers. 30 subjects
from two different organizations were provided with smartphones. The study
lasted for 8 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 self reported 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.Comment: in IEEE Journal of Biomedical and Health Informatics, 201
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