A transient dual-type sensor based on MXene/cellulose nanofibers composite for intelligent sedentary and sitting postures monitoring. (5th November 2022)

• Record Type: Journal Article
• Authors: Huang, Haizhou; Dong, Yide; Wan, Shu; Shen, Jiaxin; Li, Chen; Han, Longxiang; Dou, Guangbin; Sun, Litao
• Is Part Of: Carbon. Volume 200(2022)
• Page Start: 327
• Page End: 336
• Publication Date: 2022-11-05
• Subjects: Transient sensor -- MXene -- Pressure sensor -- Intelligent system -- Health monitoring; Carbon -- Periodicals; Carbone -- Périodiques; Koolstof; Toepassingen; Electronic journals; 546.681
• DOI: 10.1016/j.carbon.2022.08.070
• Languages: English
• ISSNs: 0008-6223

Abstract

Abstract: Sedentary and poor sitting postures will significantly raise the risk of cardiovascular disease and musculoskeletal disorders in humans. Thus, it is urgent to realize sedentary and sitting postures monitoring through various types of sensors. However, these sensors are commonly rigid, and non-degradable with low sensitivity and sensing range, which greatly restricts their practical applications in seamless, eco-friendly, and accurate monitoring in real-time. Here, we report flexible, degradable, transient, highly sensitive MXene/cellulose nanofibers (CNF) dual-type sensors including pressure sensor and bioelectric sensor. The pressure sensor is fabricated with hierarchical multi-layer MXene/CNF (MMC) as sensing layer, which shows wide detection range (0–950 kPa), high sensitivity (34.6 kPa −1 ), excellent stability (3000 cycles), transient and degradability. Moreover, the bioelectric sensor is obtained by delaminated MXene/CNF (DMC) sensing film, exhibiting high conductive, low contact impedance in high-quality electrocardiogram signal recording. Furthermore, with the aid of the deep neural network, the dual sensors can be integrated into an intelligent health monitoring system to achieve continous electrocardiogram, heart rate, sedentary, and sitting posture monitoring simultaneously. The system can realize multi-users identification and sitting postures recognition with high accuracy (>97%), showing great potential in health status monitoring with more privacy compared with previous methods. Graphical abstract: Image 1