An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements

Abstract

Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.

Machine Learning-Assisted Multifunctional Environmental Sensing Based on a Piezoelectric Cantilever

“…The integration of multiple sensors into one platform is often linked with significant increases in energy consumption and decreases in stability and reliability, while the sensing platform also suffers from signal interferences among the multiple sensing elements . Recently, some multifunctional sensors based on a single sensing element and machine learning have been developed. − However, how to detect multiple environmental parameters simultaneously using a single element is still a challenge.…”

Flat Silk Cocoon Pressure Sensor Based on a Sea Urchin-like Microstructure for Intelligent Sensing

“…By pressing the pressure sensor and holding for 1 s and then removing the pressure, the response/recovery times of the five-layer HMSFP–FSC pressure sensor were obtained as 140 and 130 ms, respectively, as shown in Figure b. Compared to the average human response time of 200 ms, the response/recovery time of the sensor is less than 200 ms, indicating that the sensor can quickly respond to external pressure and meet the requirements of rapid detection. The minimum detection limit of the pressure sensor is the minimum pressure that the sensor can respond to the external pressure, and it can reflect the ability of the sensor to distinguish small pressures.…”

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

“…25 Therefore, machine learning (ML) is one of the most rapidly developing and significant subfields of AI research. 26–33 At the very beginning of ML development (1950s–1960s), there are three major branches, that is, symbolic learning proposed by Hunt et al , statistical methods by Nilsson, and neural networks by Rosenblatt. 34 Nowadays, these branches develop advanced methods and can be divided into four categories, that is, classification, regression, clustering, and dimensionality reduction.…”