A novel, convenient ambient electric arc ionization (AEAI) device was developed as mass spectrometry ion source for versatile sample analysis. AEAI could be considered as a soft ionization technique in...

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Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often limited since the complete contact area is invisible during manipulation. In this paper, the authors propose an object curvature estimation method based on an artificial neural network algorithm through a lab-developed sparse tactile sensor array. The compliant layer covering on the sensor is indispensable for fitting the curved surface. Three types (plane, convex sphere, and convex cylinder) of sample and each type of sample including 30 different radiuses (1 mm to 30 mm) were used in the experiment. The overall classification accuracy was 93.1%. The average curvature radius estimating error based on an artificial neural network (ANN) algorithm was 1.87 mm. When the radius of curvature was bigger than 5 mm, the average relative error was smaller than 20%. As a comparison, the sensor array density we used in this paper was less than 9/cm2, which was smaller than the density of human SAII receptors, but the discrimination result was close to the SAII receptors. Comparison with the curvature discrimination ability of the human body showed that this method has a promising application prospect.

Sensor–artery alignment has always been a significant problem in arterial tonometry devices and prevents their application to wearable continuous blood pressure (BP) monitoring. Traditional solutions are to use a complex servo system to search for the best measurement position or to use an inefficient pressure sensor array. In this study, a novel solid–liquid mixture pressure sensing module is proposed. A flexible film with unique liquid-filled structures greatly reduces the pulse measurement error caused by sensor misplacement. The ideal measuring location was defined as −2.5 to 2.5 mm from the center of the module and the pressure variation was within 5.4%, which is available in the real application. Even at a distance of ±4 mm from the module center, the pressure decays by 23.7%, and its dynamic waveform is maintained. In addition, the sensing module is also endowed with the capability of measuring the pulse wave transmit time as a complementary method for BP measuring. The capability of the developed alignment-free sensing module in BP measurement was been validated. Twenty subjects were selected for the BP measurement experiment, which followed IEEE standards. The experimental results showed that the mean error of SBP is −4.26 mmHg with a standard deviation of 7.0 mmHg, and the mean error of DBP is 2.98 mmHg with a standard deviation of 5.07 mmHg. The device is expected to provide a new solution for wearable continuous BP monitoring.