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Epidermal electrophysiological monitoring has garnered significant attention for its potential in medical diagnosis and healthcare, particularly in continuous signal recording. However, simultaneously satisfying skin compliance, mechanical properties, environmental adaptation, and biocompatibility to avoid signal attenuation and motion artifacts is challenging, and accurate physiological feature extraction necessitates effective signal-processing algorithms. This review presents the latest advancements in smart electrodes for epidermal electrophysiological monitoring, focusing on materials, structures, and algorithms. First, smart materials incorporating self-adhesion, self-healing, and self-sensing functions offer promising solutions for long-term monitoring. Second, smart meso-structures, together with micro/nanostructures endowed the electrodes with self-adaption and multifunctionality. Third, intelligent algorithms give smart electrodes a “soul,” facilitating faster and more-accurate identification of required information via automatic processing of collected electrical signals. Finally, the existing challenges and future opportunities for developing smart electrodes are discussed. Recognized as a crucial direction for next-generation epidermal electrodes, intelligence holds the potential for extensive, effective, and transformative applications in the future.

The study of wearable systems based on surface electromyography (sEMG) signals has attracted widespread attention and plays an important role in human–computer interaction, physiological state monitoring, and other fields. Traditional sEMG signal acquisition systems are primarily targeted at body parts that are not in line with daily wearing habits, such as the arms, legs, and face. In addition, some systems rely on wired connections, which impacts their flexibility and user-friendliness. This paper presents a novel wrist-worn system with four sEMG acquisition channels and a high common-mode rejection ratio (CMRR) greater than 120 dB. The circuit has an overall gain of 2492 V/V and a bandwidth of 15~500 Hz. It is fabricated using flexible circuit technologies and is encapsulated in a soft skin-friendly silicone gel. The system acquires sEMG signals at a sampling rate of over 2000 Hz with a 16-bit resolution and transmits data to a smart device via low-power Bluetooth. Muscle fatigue detection and four-class gesture recognition experiments (accuracy greater than 95%) were conducted to validate its practicality. The system has potential applications in natural and intuitive human–computer interaction and physiological state monitoring.