Acceleration sensors capable of human physiological information and unmanned equipment operating state evaluating have significant applications in advanced sensing and intelligent control. Especially, achieving self-powered sensing of various vibration information in irregular space is still a challenge. Herein, a multi-source fusion self-powered acceleration sensor integrated two triboelectric sensing modules and two electromagnetic sensing modules is successfully proposed via heterogeneous integration strategy and micro-capacitor model, which realizes the high-accuracy and high-reliability vibration acceleration monitoring. In addition, the flexible symmetric structure imparts the device with conformal property, broadening its application scenarios. Combined with customed four-channel acquisition storage circuit and linear fitting algorithm of multi-source sensory data, the developed acceleration sensor has excellent practicality to accurately sense and predict various acceleration states with accuracy of 96%. This capability of the sensing system underscores the practical utility of human sole and unmanned aerial vehicle wing to evaluate physiological and operating state, which addresses the critical acceleration sensory challenges in irregular space by providing a sensitive, reliable and conformal sensing strategy.

Imbuing artificial sensory system with intelligence of the biological counterpart is limited by challenges in emulating perceptual learning ability at the device level. In biological systems, stimuli from the surrounding environment are detected, transmitted, and processed by receptor, afferent nerve, and brain, respectively. This process allows the living creatures to identify the potential hazards and improve their adaptability in various environments. Here, inspired by the biological olfaction system, a gas sensory system with perceptual learning is developed. As a proof-of-concept, H₂S gas with various concentrations is used as the stimulation and the stimuli will be converted to pulse-like physiological signals in the designed system, which consists of a gas sensor, a flexible oscillator, and a memristor-type artificial synapse. Furthermore, the learning ability is implemented using a supervised learning method based on k-nearest neighbors (KNN) algorithm. The recognition accuracy can be enhanced by repeating training, illustrating a great potential to be used as the neuromorphic sensory system with a learning ability for the applications in robotics.

Flexible mechanosensors with a high sensitivity and fast response speed may advance the wearable and implantable applications of healthcare devices, such as real-time heart rate, pulse, and respiration monitoring. In this paper, we introduce a novel flexible electronic eardrum (EE) based on single-walled carbon nanotubes, poly-ethylene, and poly-dimethylsiloxane with micro-structured pyramid arrays. The EE device shows a high sensitivity, high signal-to-noise ratio (approximately 55 dB), and fast response time (76.9 μs) in detecting and recording sound within a frequency domain of 20–13, 000 Hz. The mechanism for sound detection is investigated and the sensitivity is determined using the micro-structure, thickness, and strain state. We also demonstrated that the device is able to distinguish human voices. This unprecedented performance of the flexible electronic eardrum has implications for many applications such as implantable acoustical bioelectronics and personal voice recognition.