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The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even though the raw sensor responses are not concluded repeatable.

We present SNIFFPHONE, a handy and easy-To-use device that allows the non-invasive detection of gastric diseases. It analyzes the user's exhaled breath using specifically developed gas sensors. The device is coupled to a smartphone, which governs the breath analysis process, sends the data measurements to an external data analysis server, and finally gives feedback to the user. In this work, we describe the SNIFFPHONE device and the general platform under development.

The aim of this paper is to present and evaluate algorithms for heartbeat interval estimation from multiple spatially distributed force sensors integrated into a bed. Moreover, the benefit of using multichannel systems as opposed to a single sensor is investigated. While it might seem intuitive that multiple channels are superior to a single channel, the main challenge lies in finding suitable methods to actually leverage this potential. To this end, two algorithms for heart rate estimation from multichannel vibration signals are presented and compared against a single-channel sensing solution. The first method operates by analyzing the cepstrum computed from the average spectra of the individual channels, while the second method applies Bayesian fusion to three interval estimators, such as the autocorrelation, which are applied to each channel. This evaluation is based on 28 night-long sleep lab recordings during which an eight-channel polyvinylidene fluoride-based sensor array was used to acquire cardiac vibration signals. The recruited patients suffered from different sleep disorders of varying severity. From the sensor array data, a virtual single-channel signal was also derived for comparison by averaging the channels. The single-channel results achieved a beat-to-beat interval error of 2.2% with a coverage (i.e., percentage of the recording which could be analyzed) of 68.7%. In comparison, the best multichannel results attained a mean error and coverage of 1.0% and 81.0%, respectively. These results present statistically significant improvements of both metrics over the single-channel results ($p

Magnetic resonance imaging (MRI) thermometry is applied for guiding thermal therapy of tumorous tissues using high-intensity focused ultrasound (HIFU). Fast imaging sequences are needed for the on-line MRI operation, and strong artifacts on the temperature map can be found especially for image regions having poor signal-to-noise (S/N) ratio. Detection and masking of the regions with unreliable temperature measurement would be important to avoid unnecessary notification of the HIFU operator or even cancelation of the sonication procedure. This paper presents a specific image processing procedure to improve detection of typical MRI thermometry artifacts. First a region for well behaving temperature map is selected. Then, the remaining region of distorted temperature surface is processed with a novel method using oriented filter on the surface gradient. The result gives a more stable surface over the artifact regions, which can be used further to improve consistency in detection and masking of the temperature errors. Evaluation data is from N=7 patients having thermal therapy for uterine fibroid, including about 300 different sonications and 30,000 MRI thermometry images. The results show that the suggested method decreases variance of the temperature difference between consequent image frames in comparison with a conventional method using 2D phase unwrapping.

This paper investigates whether a driver's useful field of view (UFOV) can be measured during normal driving tasks without the driver being aware of the system. We were able to develop an online method to measure the driver's UFOV (oUFOV) automatically using a real car driving test, in the city of Tampere. The system was merged with two different technical solutions that estimated if the driver paid attention to the traffic objects that appeared in his or her peripheral vision. Here, the original laboratory method using a PC display was expanded to monitor the driver for much longer periods. By incorporating the oUFOV method into a driving simulation environment in Japan, we were also able to conduct safer tests for the drivers with reduced visual conditions, such as the driver's mental status change (hasty driving), and replace more accurate control parameters with simulation software in cases of risky driving scenarios. Thus, we confirmed that the oUFOV can measure the visual conditions for the driver and be deployed as a real-time driver support system with an automatic intrusive HMI adaptation function or immediate alarming, in the future, without driver awareness, according to the driver's visual attention capability.