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Since 2021, the concept of the metaverse has gained significant popularity and attention, not only among the general public but also among researchers who are interested in novel technologies and human-machine interfaces. Sensors, a critical component of human-machine interaction, have seen rapid advancements in recent years, particularly graphene-based sensors. These sensors offer a number of benefits, including flexibility, lightweight, ease of integration, and outstanding electrical properties. Over the past decade, our research team has focused on developing advanced graphene sensors for use in human-machine interaction and wearable healthcare. In this review, we showcase our team’s efforts by presenting the design, manufacturing process, and performance of various graphene-based sensors, focusing on their suitability for diverse human-machine interaction needs across the human body. Additionally, we discuss potential future directions for the development of graphene-based sensors in human-machine interaction and share our insights.
Since 2021, the concept of the metaverse has gained significant popularity and attention, not only among the general public but also among researchers who are interested in novel technologies and human-machine interfaces. Sensors, a critical component of human-machine interaction, have seen rapid advancements in recent years, particularly graphene-based sensors. These sensors offer a number of benefits, including flexibility, lightweight, ease of integration, and outstanding electrical properties. Over the past decade, our research team has focused on developing advanced graphene sensors for use in human-machine interaction and wearable healthcare. In this review, we showcase our team’s efforts by presenting the design, manufacturing process, and performance of various graphene-based sensors, focusing on their suitability for diverse human-machine interaction needs across the human body. Additionally, we discuss potential future directions for the development of graphene-based sensors in human-machine interaction and share our insights.
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This work was supported by the National Key R&D Program of China (2021YFC3002200 and 2022YFB3204100), the National Natural Science Foundation of China (U20A20168, 51861145202, 61874065, and 62022047).
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