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.

Surface texture information plays an important role in the cognition and manipulation of an object. Vision and touch are the two main methods for extracting an object’s surface texture information. However, vision is often limited since the viewing angle is uncertain during manipulation. In this article, we propose a fine surface texture detection method based on a stochastic resonance algorithm through a novel solid–liquid composite flexible tactile sensor array. A thin flexible layer and solid–liquid composite conduction structure on the sensor effectively reduce the attenuation of the contact force and enhance the sensitivity of the sensor. A series of ridge texture samples with different heights (0.9, 4, 10 μm), different widths (0.3, 0.5, 0.7, 1 mm), but the same spatial period (2 mm) of ridges were used in the experiment. The experimental results prove that the stochastic resonance algorithm can significantly improve the signal characteristic of the output signal of the sensor. The sensor has the capability to detect fine ridge texture information. The mean relative error of the estimation for the spatial period was 1.085%, and the ridge width and ridge height, respectively, have a monotonic mapping relationship with the corresponding model output parameters. The sensing capability to sense a fine texture of tactile senor surpasses the limit of human fingers.

Objective: This work focuses on development a unique mini sensing module to alleviate muscle action induced rtefacts and in the meanwhile to enable the alignment free capability for radial BP measurement. Design and method: The coupling variations from motion rtefacts tremendously distorts the sensing signal thus leads the measuring results unreliable and limits the applications of wearable blood pressure monitoring in daily life. It is key to have capability of sensing and recognizing the muscle action induced stress at the measuring site and then have the opportunity to eliminate misleading coupling variation. The mini sensing module consists of two independent sensing channels, which are neighboring distributed in parallel (Fig. 1). The channels are arranged enough close with the spacing only 2 mm in order to guarantee them sensing the same induced stress. The two channels have identical dimension feature with10 mm length and 3 mm width. And a 3 mm × 3 mm hard block is integrated into the middle segment of one channel. The channels are filled with degassed silicon oil and a silicon die is embedded and serves as pressure sensor unit. TPU flexible film is used to seal the channels, which serves also as the mechanical stimulation interface. The smart module is fixed to wrist with channel direction transversal to the arterial path during radial BP measuring. The blocked channel isolates completely (partly at least) stimulating from the vascular and serves as skin stress sensing reference, and the other sensing channel gets combination of the skin stress together with fully stimulating of vascular BP. It is straightforward that the difference two signal can partly at least eliminate the stress induced error, which also is presented in Fig.2. Further rtefacts reduction can be achieved by exploiting specific adaptive filtering algorithm with the blocked signal as disturbance input and the other as source. Results: Experiments have proved the effectiveness of the scheme. Conclusions: By comparison with the BP signal under optimal condition, the sensing module can provide with accurate BP pulse waves in the presence of motion interference.