RESUMO
Personalized healthcare management is an emerging field that requires the development of environment-friendly, integrated, and electrochemical multimodal devices. In this study, the concept of integrated paper-based biosensors (IFP-Multi ) for personalized healthcare management is introduced. By leveraging ink printing technology and a ChatGPT-bioelectronic interface, these biosensors offer ultrahigh areal-specific capacitance (74633 mF cm-2 ), excellent mechanical properties, and multifunctional sensing and humidity power generation capabilities. More importantly, the IFP-Multi devices have the potential to simulate deaf-mute vocalization and can be integrated into wearable sensors to detect muscle contractions and bending motions. Moreover, they also enable monitoring of physiological signals from various body parts, such as the throat, nape, elbow, wrist, and knee, and successfully record sharp and repeatable signals generated by muscle contractions. In addition, the IFP-Multi devices demonstrate self-powered handwriting sensing and moisture power generation for sweat-sensing applications. As a proof-of-concept, a GPT 3.5 model-based fine-tuning and prediction pipeline that utilizes recorded physiological signals through IFP-Multi is showcased, enabling artificial intelligence with multimodal sensing capabilities for personalized healthcare management. This work presents a promising and ecofriendly approach to developing paper-based electrochemical multimodal devices, paving the way for a new era of healthcare advancements.
