In this letter, we present a custom-designed and flexible readout circuitry for the characterization of flexible and planar electrolyte-gated carbon nanotube field-effect transistor-based biosensors for ammonium detection in sweat. We employed spray-deposited semiconducting carbon nanotubes as active material, and functionalized the devices with previously synthesized ion-selective membrane, based on the nonactin ionophore. In the design of the readout circuitry, we focused on enabling low-power operation with a single coin-cell battery with compact wireless data transmission. To maximize the bendability of the flexible PCB, particular attention was taken in layout routing as well as in selecting small-sized packages for the commercial integrated circuits and all-in-one systems such as programmable Bluetooth system-on-chip (i.e., ST microelectronics BlueNRG SoC). We recorded a high sensitivity of 4.516 μ A/decade for sweat ammonium level analysis between 0.1 and 100 mM, which fully covers the relevant range of interest in the context of sport monitoring. The characterization was carried out with the introduced front-end readout, and the results were benchmarked with a “gold standard” instrument, showing good and reliable performance of the developed fully flexible bioelectronic system.
Tactile information is usually important for object manipulation and grasping. Typically, soft hands and tactile sensors can deform passively and contribute to stable grasping. In particular, soft tactile sensors that use conductive materials as sensor elements can acquire contact information, including the contact position and pressure; however, this tactile information cannot be classified. This study attempts to classify tactile information based on conductive material arrangements. To this end, we develop a soft tactile sensor composed of a silicone rubber body with two channels filled with a conductive material. The two channels are arranged so that they are parallel from the top view and angled with different slopes from the side view. Our experimental data reveal that the contact position parallel to the channels can be determined based on the resistance changes in the two channels, whereas the pressure can be obtained through a model based on the estimated value of the contact position.
In this letter, we present a smart glove for Tactile Internet applications. The individual finger motions are measured via resistive strain sensors. The strain sensors are directly integrated with the textile glove and are produced in an automated process. The sensor glove is integrated with sensor conditioning, controller, wireless frontend, and battery. We investigate the measured sensor data for a variety of gestures, demonstrating the good quality of the data allowing for easy and low-energy gesture recognition.