Three‐in‐One Portable Electronic Sensory System Based on Low‐Impedance Laser‐Induced Graphene On‐Skin Electrode Sensors for Electrophysiological Signal Monitoring. Issue 3 (1st December 2022) - Zhang, Quan; Qu, Menglong; Liu, Xingye; Cui, Yilei; Hu, Haining; Li, Qianyun; Jin, Meifu; Xian, Jieyu; Nie, Zhengwei; Zhang, Cheng
Abstract: On‐skin sensors can precisely perceive important electrophysiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG). Despite significant advances in the development of soft materials as electrode sensors, data acquisition (DAQ) unit—another indispensable component of on‐skin electronic sensory systems—typically exhibits bulkiness or unimodal sensing, which is detrimental to the portability of the sensory system or the comprehensiveness of the perceived information. Here, a portable and multimodal DAQ unit to tackle these challenges is designed. By assembling the DAQ unit with low‐impedance (<100 Ω) laser‐induced graphene on‐skin electrode sensors, a wireless communication module, a power supply module, and a 3D printed protective shell, the completed sensory system can realize three‐in‐one monitoring of EEG, ECG, and EMG with a light weight of 22 g and a low cost of 25. Moreover, a mobile App is developed to display the perceived electrophysiological signals in real time. Human–machine interface and embedded machine learning are demonstrated using the designed sensory system, indicating its potential applications in artificial intelligence. The success of this inexpensive three‐in‐one portable electronic sensory system sheds light on design, fabrication, and commercialization of multifunctional wearable electronics with wide applications in fitness tracking, medical diagnostics, and human–machine interface. Abstract : A three‐in‐one portable electronic system is developed for monitoring of EEG, ECG, and EMG signals. The system includes a self‐designed integrated circuit and low‐impedance laser‐induced graphene on‐skin electrode sensors. Besides multimodal electrophysiological signal monitoring, human–machine interface and embedded machine learning are demonstrated using the designed sensory system, indicating its potential applications in artificial intelligence.