Time filter

Source Type

Boras, Sweden

University of Borås or Borås University College is a university college in Borås, Sweden. Wikipedia.

Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers. Source

Wittek P.,University of Boras
Journal of Computational Physics

Clustering methods in machine learning may benefit from borrowing metaphors from physics. Dynamic quantum clustering associates a Gaussian wave packet with the multidimensional data points and regards them as eigenfunctions of the Schrödinger equation. The clustering structure emerges by letting the system evolve and the visual nature of the algorithm has been shown to be useful in a range of applications. Furthermore, the method only uses matrix operations, which readily lend themselves to parallelization. In this paper, we develop an implementation on graphics hardware and investigate how this approach can accelerate the computations. We achieve a speedup of up to two magnitudes over a multicore CPU implementation, which proves that quantum-like methods and acceleration by graphics processing units have a great relevance to machine learning. © 2012 Elsevier Inc. Source

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms. © 2014 Akadémiai Kiadó, Budapest, Hungary. Source

Lundgren I.,University of Boras

Objective: to describe women's experiences of doula support during childbirth. Design and setting: a qualitative study using a hermeneutic approach. Data were collected via tape-recorded interviews in the women's homes or at a place chosen by the women, one to eight months after the birth. Participants: nine women, seven primiparous and two multiparous, aged between 15 and 40 years, who had received antenatal care at a special clinic for single mothers in Gothenburg, Sweden between 2006 and 2007. Key findings: the role of the doula lies between natural care and professional care, veering towards professional care. Professional aspects include being a mediator to the unknown, and a human life line to help the woman to play her part in the birth. Furthermore, the doula is a coach who mediates a belief in the woman's capacity to give birth. The midwives' supporting role is not clear to the women, which can be the result of doulas having a more professional supporting role than giving natural care. Midwives are unable to offer continuity of care and constant support during the birth. Implications for practice: the different supporting roles of doulas and midwives in maternity care should be addressed. Furthermore, maternity care should be organised in a way that gives the woman an opportunity to access continuity of care and constant support. © 2008 Elsevier Ltd. All rights reserved. Source

Wittek P.,University of Boras | Tan C.L.,National University of Singapore
IEEE Transactions on Pattern Analysis and Machine Intelligence

Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L-2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels. © 2011 IEEE. Source

Discover hidden collaborations