Candlelight-style, blue hazard-free light sources have been long desired in order to minimize the threats of blue emission on human health, artifacts, ecosystems, and night skies. Flexible light sources can enable numerous innovative products that are wearable, rollable, foldable, and portable. If also transparent, more applications including smart displays, lighting, signage, etc., can be implemented. However, current flexible lighting sources have issues like the use of environmentally unfriendly materials and poor lifetimes. Here, we show the fabrication and lifetime study of a candlelight-style organic light-emitting diode (LED) on high-flexibility, transparent mica that is 100% plastic-free. We found that the mica substrate coated with a transparent conductor survived a record-high bending curvature, i.e., 1/5 mm–1 and 50,000 bending cycles at a 7.5 mm bending radius. The candlelight organic LED fabricated thereon showed a good lifetime that is 83% of that on glass. The blue light-less emission permits at 100 lx an exposure limit of 47,000 s, while the exposure limit is 320 s for a cold-white counterpart; upon exposure at night for 1.5 h, it suppresses 1.6% melatonin, while 30% melatonin is suppressed for the cold-white counterpart. Our results demonstrate how blue hazard-free lighting sources are fabricated on eco-friendly mica that is highly flexible, and the resultant device showed a lifetime approaching that on glass due to its high resistance against moisture and oxygen penetration. The findings suggest that mica can be a good candidate for flexible transparent devices. We expect that these could bring up opportunities to promote omnifriendly lighting applications with a higher degree of design flexibility.

In this paper, we develop a real-time motion capturing system. Based on 3-D hand model with structural constraint and kinematical constraint, we may estimate human hand state in high degree of freedom space by using the proposed separable statebased particle filter (SSBPF). We separate the states of the variable and track five different fingers individually by using the particle filter. Combining the hand model and SSBPF, we implement a human hand gesture reconstruction system