Multi-Modal Sensor Capturing Gas, Pressure, and Respiratory Signals Simultaneously… Achieving 97.6% Accuracy When Combined with AI Analysis<\/p>\n\n\n\n
Professor Kim Jong-seung’s research team in the Department of Chemistry at Korea University, in collaboration with Professor Sulin’s research team at Jilin University, China, has developed an artificial epidermis-based smart healthcare platform<\/strong> capable of early detection of asthma risk signals. The researchers implemented a ‘3D artificial epidermis sensor’<\/strong> that mimics the multi-layered structure of human skin, securing a technology to precisely measure various bio-signals related to asthma\u2014such as gas, pressure, and respiration\u2014with a single device. Asthma involves the simultaneous occurrence of diverse signals, including increased nitrogen dioxide ($\\text{NO}_2$) concentration, abnormal breathing patterns, and muscle weakness, but existing wearable devices struggled to detect these in a complex manner. The research team overcame these limitations by combining a biomimetic structural design with high-sensitivity material technology.<\/p>\n\n\n\n The new artificial epidermis sensor demonstrated fast and sensitive responsiveness, detecting nitrogen dioxide in just 7 seconds. It exhibited 12.3 times higher selectivity than other gases, significantly improving accuracy, and operated stably even in environments with humidity changes. The pressure sensing function was also robust, consistently measuring signals without performance degradation even after over 300 bending and pressing tests. Due to its skin-like flexible structure, it adheres naturally to the body, is comfortable for long-term wear, and maintains sensing performance.<\/p>\n\n\n\n The research team expanded the sensor into a smart warning platform by integrating it with a flexible electronic circuit, enabling wireless, real-time signal measurement and transmission. This platform simultaneously collects nitrogen dioxide exposure levels, breathing patterns, and pressure changes, and distinguishes risk signals with 97.6% accuracy<\/strong> through Artificial Intelligence (AI) analysis<\/strong>. This suggests the potential for next-generation healthcare technology that enables the early detection of asthma attack risks and personalized response. Broad extensibility is also expected in the fields of telemedicine and wearable healthcare.<\/p>\n\n\n\n The research findings were recognized for their technical excellence by being published in the international journal Nano-Micro Letters<\/strong>, which ranks in the top 1% in the fields of materials and nanotechnology. Professor Kim Jong-seung stated that this research is a “platform that integrates artificial epidermis, multi-modal sensing, and AI-based signal recognition,” and that it will “contribute to the advancement of not only early asthma diagnosis but also diverse disease monitoring and remote healthcare technology.” This research was supported by the Ministry of Science and ICT and the National Research Foundation of Korea’s Leader Researcher Support Project.<\/p>\n\n\n\n #KoreaUniversity #EarlyAsthmaDiagnosis #ArtificialEpidermisSensor #SmartHealthcare #AISensing #MultiModalSensor #NanoMicroLetters<\/p>\n","protected":false},"excerpt":{"rendered":" Multi-Modal Sensor Capturing Gas, Pressure, and Respiratory Signals Simultaneously… Achieving 97.6% Accuracy When Combined with AI Analysis Professor Kim Jong-seung’s research team in the Department of Chemistry at Korea University, in collaboration with Professor Sulin’s research team at Jilin University, China, has developed an artificial epidermis-based smart healthcare platform capable of early detection of asthma\u2026<\/p>\n","protected":false},"author":14,"featured_media":8629,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[121],"tags":[],"class_list":["post-8628","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-university"],"_links":{"self":[{"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/posts\/8628","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/comments?post=8628"}],"version-history":[{"count":0,"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/posts\/8628\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/media\/8629"}],"wp:attachment":[{"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/media?parent=8628"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/categories?post=8628"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.spotlightuniv.com\/en\/wp-json\/wp\/v2\/tags?post=8628"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}