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Integrating self‐powered sensors or energy harvesters with everyday clothes brings a novel insight into wearable electronics and improves personal intelligence in the era of the Internet of Things. This work develops a customizable screen‐printed textile triboelectric nanogenerator (SPT‐TENG) for biomechanical energy harvesting and human‐interactive applications. The as‐fabricated textile TENG not only presents excellent wearing experiences such as lightweight, permeability, and washability, but also exhibits high electrical output performance to power sustainably some portable electronics such as a watch or calculator. The system‐level wearable human‐interactive applications, including a wireless keyboard and a smart home appliance control, are also demonstrated with the advantages of cyber security, easy operation, low cost, and wearability through screen‐printing pattern‐customized TENG arrays. Benefiting from these features, the SPT‐TENG shows promising applications in wearable electronics, human‐machine interfaces, and artificial intelligence.

Respiratory signals are basic indicators of human life and health that are used as effective biomarkers to detect respiratory diseases in clinics, including cardiopulmonary function, breathing disorders, and breathing system infections. Therefore, it is necessary to continuously measure respiratory signals. However, there is still a lack of effective portable electronic devices designed to meet the needs of daily respiratory monitoring. This study presents an intelligent, portable, and wireless respiratory monitoring system for real-time evaluation of human respiratory behaviors. The system consists of a triboelectric respiratory sensor; circuit board hardware for data acquisition, preprocessing, and wireless transmission; a machine learning algorithm for enhancing recognition accuracy; and a mobile terminal app. The triboelectric sensor—fabricated by the screen-printing method—is lightweight, non-invasive, and biocompatible. It provides a clear response to the frequency and intensity of respiratory airflow. The portable circuit board is reusable and cost-effective. The decision tree model algorithm is used to identify the respiratory signals with an average accuracy of 97.2%. The real-time signal and statistical results can be uploaded to a server network and displayed on various mobile terminals for body health warnings and advice. This work promotes the development of wearable health monitoring systems.

The accurate detection of very weak biomagnetic signals with a sensitivity of fT levels and mapping nanoscale magnetic field variations are two of the most significant challenges for biosensing. Compared to the limited sensitivity and spatial resolution of traditional magnetic field sensors, quantum‐based magnetic field sensors are emerging as a promising solution. In this review, the latest developments of three representative quantum‐based magnetic field sensors, including superconducting quantum interference device (SQUID) magnetometers, spin exchange relaxation free (SERF) atomic magnetometers, and nitrogen‐vacancy centers in diamond, are summarized. Both virtues and limitations of these sensors are analyzed systematically, and typical applications in magnetocardiography, magnetoencephalography, detection of neuronal action potentials, magnetic imaging of living cells, biomedical diagnosis, etc., are presented. Furthermore, SQUIDs combined with microfluidics for magnetic immunoassay diagnostics, the chip‐scale SERF atomic magnetometers fabricated by microelectromechanical systems that offer wearable flexibility, and nanodiamonds functionalized with magnetic nanoparticles to improve the sensitivity of nanothermometers are discussed.

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