The “Lower Town” archaeological site in Mycenae, Argolis, Greece has been excavated since 2007 and multiple periods of occupation and abandonment are represented in the stratigraphy uncovered. Sedimentary deposits were grouped into two general categories during excavation and these categories shaped fieldwork decisions: yellow-orange sediment with denser artifact concentrations representing potential occupation and red sediment with sparser artifacts representing abandonment. The distributions of point locations of artifacts within these bodies of sediment are analyzed statistically for spatial homogeneity using Ripley’s K in a GIS environment to test these site formation assumptions. Statistically significant spatial clustering in artifacts is assumed for autochthonous occupation deposits. These analyses were designed to be used to explicitly test otherwise implicit assumptions during fieldwork in future fieldwork. Results are mixed, with several factors complicating the interpretation of results without the hindsight of postieldwork artifactual and geoarchaeological analyses.

Background Camera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment. Methods We recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity. Results The results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations. Conclusions The proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.

Background Camera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment. Methods We recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity. Results The results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations. Conclusions The proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.