Hydrogen peroxide (H2O2) quantification in biomedicine is valuable as inflammation biomarker but also in assays employing enzymes that generate or consume H2O2 linked to a specific biomarker. Optical H2O2 detection is typically performed through peroxidase-coupled reactions utilizing organic dyes that suffer, however, from poor stability/reproducibility and also cannot be employed in situ in dynamic complex cell cultures to monitor H2O2 levels in real-time. Here, we utilize enzyme-mimetic CeO2 nanocrystals that are sensitive to H2O2 and study the effect of H2O2 presence on their electronic and luminescent properties. We produce and dope with Eu3+ these particles in a single-step by flame synthesis and directly deposit them on Si and glass substrates to fabricate nanoparticle layers to monitor in real-time and in situ the H2O2 concentrations generated by Streptococcus pneumoniae clinical isolates. Furthermore, the small CeO2:Eu3+ nanocrystals are combined in a single-step with larger, non-responsive Y2O3:Tb3+ nanoparticles during their double-nozzle flame synthesis to engineer hybrid luminescent nanoaggregates as ratiometric robust biosensors. We demonstrate the functionality of these biosensors by monitoring their response in the presence of a broad range of H2O2 concentrations in vitro from S. pneumoniae, highlighting their potential for facile real-time H2O2 detection in vitro in cell cultures.

Robust, fast, and accurate human state -6D pose and posture -estimation remains a challenging problem. For real-world applications, the ability to estimate the human state in real-time is highly desirable. In this paper, we present BodySLAM++, a fast, efficient, and accurate human and camera state estimation framework relying on visualinertial data. BodySLAM++ extends an existing visual-inertial state estimation framework, OKVIS2, to solve the dual task of estimating camera and human states simultaneously. Our system improves the accuracy of both human and camera state estimation with respect to baseline methods by 26% and 12%, respectively, and achieves real-time performance at 15+ frames per second on an Intel i7-model CPU. Experiments were conducted on a custom dataset containing both ground truth human and camera poses collected with an indoor motion tracking system.