Shengbo Sang,Chenhui Bai,Wenxu Wang,Saeed Ahmed Khan,Zhaosu Wang,Xinru Yang,Zhiyi Zhang,Hulin Zhang
Inappropriate pen-holding postures can result in increased muscle strain and reduced writing speed, and may even contribute to the onset of conditions such as myopia, arthritis, and cervical spondylosis. However, challenges persist in current one-size-fits-all pens toward actively monitoring handwriting habits due to structural complexity and indispensability of power supply. In this study, a finger temperature-driven thermoelectric gel-based smart pen has been invented for identity recognition, stroke analysis, and grip posture assessment by coupling thermogalvanic and piezoresistive effects derived from finger temperature and pressure on a PVA/gelatin gel. This dual-network gel exhibits not only a high thermopower of − 2.05 mV K −1 and a rapid response of 130 ms, but also full flexibility, significant resistance to desiccation, and a high tensile capacity of up to 300%. By integrating thermogalvanic gel units within a pen shaft, our self-powered smart pen achieved a recognition accuracy of 98.1% for eight basic Chinese character strokes with the aid of deep learning. Furthermore, with a four-channel expansion, the recognition accuracy reaches 98.8% for eight distinct identities and 99.1% for nine different pen-holding postures, respectively, while offering real-time visualization of monitoring results on a user-friendly human-machine interface. This machine learning-assisted self-powered smart gel pen presents a new viable solution for wearable bioelectronics toward future intelligent era.
Identity recognition as the first barrier of intelligent security plays a vital role, which is facing new challenges that are unable to meet the need of intelligent era due to low accuracy, complex configuration and dependence on power supply. Here, a finger temperature-driven intelligent identity recognition strategy is presented based on a thermogalvanic hydrogel (TGH) by actively discerning biometric characteristics of fingers. The TGH is a dual network PVA/Agar hydrogel in an H 2 O/glycerol binary solvent with [Fe(CN) 6 ] 3−/4− as a redox couple. Using a concave-arranged TGH array, the characteristics of users can be distinguished adequately even under an open environment by extracting self-existent intrinsic temperature features from five typical sites of fingers. Combined with machine learning, the TGH array can recognize different users with a high average accuracy of 97.6%. This self-powered identity recognition strategy is further applied to a smart lock, attaining a more reliable security protection from biometric characteristics than bare passwords. This work provides a promising solution for achieving better identity recognition, which has great advantages in intelligent security and human-machine interaction toward future Internet of everything.
危险品化学品经营许可证（带存储）