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. 2026 Apr 1;20:1702124. doi: 10.3389/fnins.2026.1702124

Figure 2.

Panel A shows a webcam, panel B displays a face with tracking points and a detection grid overlay, panel C presents a scatterplot of face grid trajectories with color-coded dot clusters, panel D features a manual blood pressure cuff with gauge and bulb, panel E depicts a set of three BioStamp sensors and a Samsung smartphone on a docking station labeled BioStamp, and panel F illustrates sensor placement diagrams on a bicep, anterior thigh, and chest, each labeled with recommended analytics and measurement types.

Data acquisition tools. (A) A webcam collects video of the participant for the full duration of the task. (B) OpenFace software is used to estimate the facial grid from the video of one of the authors (Dr. Elsayed), along with the 3D gaze and head orientation, saving action units responsible for the micro-motions of the facial muscles. (C) Trigeminal subdivision based on the facial nerves innervating three main regions: V1 (ophthalmic), V2 (maxillary), and V3 (mandibular). The facial grid is subdivided into V1 (red), V2 (blue), and V3 (black) subregions to study the stochasticity of the micro-motions. (D) Blood pressure cuff used to evoke the sensation of pressure pain in the non-performing arm. (E) The MC10 Biostamp sensors and phone used to collect time series data by registering different biorhythms. (F) Locations on the body where the Biostamp sensors were placed to co-register data from the biceps, anterior thigh, and heart (image acquired via registered study on the MC100 Biostamp-n-Point Cloud system, now acquired by Medidata).