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Patients suspected of ventilator-associated lower respiratory tract infections (VA-LRTIs) commonly receive broad-spectrum antimicrobial therapy unnecessarily. We tested whether exhaled breath analysis can discriminate between patients suspected of VA-LRTI with confirmed infection, from patients with negative cultures. Breath from 108 patients suspected of VA-LRTI was analysed by gas chromatography-mass spectrometry. The breath test had a sensitivity of 98% at a specificity of 49%, confirmed with a second analytical method. The breath test had a negative predictive value of 96% and excluded pneumonia in half of the patients with negative cultures. Trial registration number: UKCRN ID number 19086, registered May 2015. [ABSTRACT FROM AUTHOR]

Tibetan singing bowls are employed worldwide for meditation, music, relaxation, personal wellbeing, and religious practices. Each Tibetan bowl can produce a limited number of sounds, defined by the size and material of the bowl, and the actuator device used. Usually, there is a need for a second person to actuate the bowl. Addressing these limitations, we built an electronic device, named eBowl, which can mimic the acoustics of Tibetan bowls, and beyond that, can produce a wide range of other sounds. Furthermore, it can be used for relaxation and sound massage without the need for a second person. The eBowl generates auditory beats that are in EEG alpha frequency range, which can cause brainwave entrainment and lead to relaxation. User tests measuring physiological parameters revealed the eBowl's effectiveness for relaxation, showing that eBowl influences skin conductance, heart rate, and respiration rate and induces relaxation. [ABSTRACT FROM AUTHOR]

Synthesizing expressive facial animation is a very challenging topic within the graphics community. In this paper, we present an expressive facial animation synthesis system enabled by automated learning from facial motion capture data. Accurate 3D motions of the markers on the face of a human subject are captured while he/she recites a predesigned corpus, with specific spoken and visual expressions. We present a novel motion capture mining technique that "learns" speech coarticulation models for diphones and triphones from the recorded data. A Phoneme-Independent Expression Eigenspace (PIEES) that encloses the dynamic expression signals is constructed by motion signal processing (phoneme-based time-warping and subtraction) and Principal Component Analysis (PCA) reduction. New expressive facial animations are synthesized as follows: First, the learned coarticulation models are concatenated to synthesize neutral visual speech according to novel speech input, then a texture-synthesis-based approach is used to generate a novel dynamic expression signal from the PIEES model, and finally the synthesized expression signal is blended with the synthesized neutral visual speech to create the final expressive facial animation. Our experiments demonstrate that the system can effectively synthesize realistic expressive facial animation. [ABSTRACT FROM AUTHOR]

Measuring the respiration and heart rate unobtrusively in home settings is an important goal for health monitoring. In this work, use of a pressure sensitive mat was explored. Two methods using body morphology information, based on shoulder blades and weighted centroid, were developed for respiration rate (RR) calculation. Heart rate (HR) was calculated by combining the frequency information from different body regions. Experimental data were collected from 15 participants in a supine position via a pressure sensitive mat placed under the upper torso. RR and HR estimations derived from accelerometer sensors attached to participants' bodies were used as references to evaluate the accuracy of the proposed methods. All three methods achieved a reasonable estimation compared to the reference. The root mean squared error of the proposed RR estimation methods were 1.32 and 0.87 breath/minute respectively, and the root mean squared error of the HR estimation method was 5.55 bpm.

We introduce a novel monitoring solution for fluid accumulation in the human body (e.g. internal bleeding), based on observation of a selected energy-describing feature of the Ballistocardiogram (BCG) signal. It is hypothesized that, because of additional damping generated by the fluid, BCG signal energy decreases as compared to its baseline value. Data were collected from 15 human volunteers via accelerometers attached to the participants' body, and an electromechanical-film (EMFi) sensor-equipped bed. Fluid accumulation along the gastrointestinal (GI) tract was induced by means of water intake by the participants, and the BCG signal was recorded before and after intake. Based on performance evaluation, we selected a suitable energy feature and sensing channel amongst the ones investigated. The chosen feature showed a significant decrease in signal energy from baseline to after-intake condition (p-value<0.001), and identified the presence of fluid accumulation with high sensitivity (90% in bed-based, and 100% in standing-position monitoring).

Synthesizing expressive facial animation is a very challenging topic within the graphics community. In this paper, we present an expressive facial animation synthesis system enabled by automated learning from facial motion capture data. Accurate 3D motions of the markers on the face of a human subject are captured while he/she recites a predesigned corpus, with specific spoken and visual expressions. We present a novel motion capture mining technique that "learns" speech coarticulation models for diphones and triphones from the recorded data. A Phoneme-Independent Expression Eigenspace (PIEES) that encloses the dynamic expression signals is constructed by motion signal processing (phoneme-based time-warping and subtraction) and Principal Component Analysis (PCA) reduction. New expressive facial animations are synthesized as follows: First, the learned coarticulation models are concatenated to synthesize neutral visual speech according to novel speech input, then a texture-synthesis-based approach is used to generate a novel dynamic expression signal from the PIEES model, and finally the synthesized expression signal is blended with the synthesized neutral visual speech to create the final expressive facial animation. Our experiments demonstrate that the system can effectively synthesize realistic expressive facial animation.