With the arrival of the Internet of Things era, the demand for tactile sensors continues to grow. However, traditional sensors mostly require an external power supply to meet real-time monitoring, which brings many drawbacks such as short service life, environmental pollution, and difficulty in replacement, which greatly limits their practical applications. Therefore, the development of a passive self-power supply of tactile sensors has become a research hotspot in academia and the industry. In this review, the development of self-powered tactile sensors in the past several years is introduced and discussed. First, the sensing principle of self-powered tactile sensors is introduced. After that, the main performance parameters of the tactile sensors are briefly discussed. Finally, the potential application prospects of the tactile sensors are discussed in detail.

Wearable tactile sensors with high sensitivity can be potentially used to continuously monitoring physiological signals that are closely related to disease diagnosis and health condition tracking. However, the development of such tactile sensors involves a number of challenges, including a series of expensive patterning processes for microstructure manufacturing and addressing the large thickness of the microstructured composite film. Herein, a mold-free approach is presented to develop an ultra-thin ZnO/PEDOT:PSS composite film with flower-like microstructures via a feasible solution process for highly sensitive tactile sensors. The fabricated tactile sensors exhibit a high sensitivity of 4 × 10³ kPa⁻¹ in the pressure range 0–10 kPa, a fast response to various pressures in merits of the hierarchical microstructures on top of the ultra-thin composite films. Thanks to the fascinating performance of the devices, the tactile sensors are demonstrated with the ability to monitor physiological signals, subtle human body motions, and spatial pressure distribution.

With the core advantages of in-memory computing and parallel processing, neuromorphic computing has become a crucial approach to meet the requirements of artificial intelligence in the era of big data. As the fundamental hardware platform for neuromorphic vision systems, high-performance optoelectronic synaptic devices constructed by a cost-effective approach is still highly desired. In this work, Au/Bi3.3Sm0.7Ti3O12 (BST)/ITO optoelectronic synaptic devices are successfully fabricated via a low-cost sol-gel method. The devices can accurately mimic the core plasticity properties of biological synapses. Under electrical and optical stimulation, a series of typical synaptic behaviors are successfully realized, including short-term plasticity (STP/STD), long-term plasticity (LTP/LTD), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), spike-number-dependent plasticity (SNDP), and spike-width-dependent plasticity (SWDP). An artificial neural network (ANN) based on the Au/BST/ITO device is developed with recognition accuracies of 92.1% and 85.21% on the MNIST and Fashion-MNIST datasets, respectively.