A textile-based platform for real-time sweat collection and analysis

The ability to perform real-time chemical measurements of body fluids is an exciting concept for the healthcare sector and the sports industry. This work is part of the BIOTEX project, an EU FP6 project which involved the development of textile-based sensors to measure the chemical composition of sweat. This is a challenging task involving the collection of sweat samples, delivery to an active surface and the removal of waste products. A textile based platform which would be in immediate contact with the skin was developed for this purpose. The system uses capillary action and exhibits a passive pumping mechanism. This is achieved by using a combination of moisture wicking fabric and a highly absorbent material. A fabric channel is created for the integration of sensors. The channel is produced using a mask and screen-printing hydrophobic material onto the fabric. Different channel lengths and widths affect the flow rate of the system. The channel dimensions were designed based on typical sweat rates and also to accommodate sensor placement. A textile patch was developed and integrated into a waistband for collection of sweat on the lower back. Real-time measurements of sweat pH, sodium concentration, conductivity and temperature were measured during exercise using the textile patch

QRS Complex Separation from Convolutive Mixtures of Biolectrical Signals Acquired by Wearable Systems

Independent component analysis (ICA) has been widely used to remove artefacts from multichannel biomedical signal acquisitions under the hypothesis that there is statistical independence among the original sources. However, the basic ICA model does not take into account the influence on the mixing process of the different paths from the signal sources to the sensors In this study we propose a convolutive mixtures model in order to overcome the limitations of the basic ICA approach. The independent components are estimated in the frequency domain, where the convolutive model can be solved through an instantaneous mixing model. The signals are reconstructed back to the observation space resolving the ICA model ambiguities. Simulations are carried out to optimize of the proposed method for convolutive mixtures of electrocardiographic (ECG) and motion artefacts signals. The algorithm is tested on real ECG signals acquired by wearable systems in order to preserve the QRS complex when the signals are degraded by real life conditions of acquisition

Textile sensors to measure sweat pH and sweat-rate during exercise

Sweat analysis can provide a valuable insight into a person’s well-being. Here we present wearable textile-based sensors that can provide real-time information regarding sweat activity. A pH sensitive dye incorporated into a fabric fluidic system is used to determine sweat pH. To detect the onset of sweat activity a sweat rate sensor is incorporated into a textile substrate. The sensors are integrated into a waistband and controlled by a central unit with wireless connectivity. The use of such sensors for sweat analysis may provide valuable physiological information for applications in sports performance and also in healthcare

WEALTHY – a wearable healthcare system: new frontier on e-textile, Journal of Telecommunications and Information Technology, 2005, nr 4

A comfortable health monitoring system named WEALTHY is presented. The system is based on a wearable interface implemented by integrating fabric sensors, advanced signal processing techniques and modern telecommunication systems, on a textile platform. Conducting and piezoresistive materials in form of fibre and yarn are integrated in a garment and used as sensors, connectors and electrode elements. Simultaneous recording of vital signs allows extrapolation of more complex parameters and inter-signal elaboration that contribute to produce alert messages and patient table. The purpose of this publication is to evaluate the performance of the textile platform and the possibility of the simultaneous acquisition of several biomedical signals

BIOTEX-biosensing textiles for personalised healthcare management.

Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting

Measurements of the charge asymmetry in top-quark pair production in the dilepton final state at s √ =8 TeV with the ATLAS detector

Measurements of the top-antitop quark pair production charge asymmetry in the dilepton channel, characterized by two high-pT leptons (electrons or muons), are presented using data corresponding to an integrated luminosity of 20.3  fb−1 from pp collisions at a center-of-mass energy s√=8  TeV collected with the ATLAS detector at the Large Hadron Collider at CERN. Inclusive and differential measurements as a function of the invariant mass, transverse momentum, and longitudinal boost of the tt¯ system are performed both in the full phase space and in a fiducial phase space closely matching the detector acceptance. Two observables are studied: AℓℓC based on the selected leptons and Att¯C based on the reconstructed tt¯ final state. The inclusive asymmetries are measured in the full phase space to be AℓℓC=0.008±0.006 and Att¯C=0.021±0.016, which are in agreement with the Standard Model predictions of AℓℓC=0.0064±0.0003 and Att¯C=0.0111±0.0004

Study of the B-c(+) -> J/psi D-s(+) and Bc(+) -> J/psi D-s*(+) decays with the ATLAS detector

The decays B-c(+) -> J/psi D-s(+) and B-c(+) -> J/psi D-s*(+) are studied with the ATLAS detector at the LHC using a dataset corresponding to integrated luminosities of 4.9 and 20.6 fb(-1) of pp collisions collected at centre-of-mass energies root s = 7 TeV and 8 TeV, respectively. Signal candidates are identified through J/psi -> mu(+)mu(-) and D-s(()*()+) -> phi pi(+)(gamma/pi(0)) decays. With a two-dimensional likelihood fit involving the B-c(+) reconstructed invariant mass and an angle between the mu(+) and D-s(+) candidate momenta in the muon pair rest frame, the yields of B-c(+) -> J/psi D-s(+) and B-c(+) -> J/psi D-s*(+), and the transverse polarisation fraction in B-c(+) -> J/psi D-s*(+) decay are measured. The transverse polarisation fraction is determined to be Gamma +/-+/-(B-c(+) -> J/psi D-s*(+))/Gamma(B-c(+) -> J/psi D-s*(+)) = 0.38 +/- 0.23 +/- 0.07, and the derived ratio of the branching fractions of the two modes is B-Bc+ -> J/psi D-s*+/B-Bc+ -> J/psi D-s(+) = 2.8(-0.8)(+1.2) +/- 0.3, where the first error is statistical and the second is systematic. Finally, a sample of B-c(+) -> J/psi pi(+) decays is used to derive the ratios of branching fractions B-Bc+ -> J/psi D-s*+/B-Bc+ -> J/psi pi(+) = 3.8 +/- 1.1 +/- 0.4 +/- 0.2 and B-Bc+ -> J/psi D-s*+/B-Bc+ -> J/psi pi(+) = 10.4 +/- 3.1 +/- 1.5 +/- 0.6, where the third error corresponds to the uncertainty of the branching fraction of D-s(+) -> phi(K+ K-)pi(+) decay. The available theoretical predictions are generally consistent with the measurement