Over the last few years, a large number of studies have been conducted on the monitoring of human behavior remaining beyond conscious control. One area of application for such monitoring systems is lie detection. The most popular method currently used for this purpose is polygraph examination, which has proven its usefulness in the field and in laboratories, but it is not without its drawbacks. Technological advances in data acquisition and automated analysis have ensured that contactless tools are in high demand in security fields like airport screening or pre-employment procedures. As a result, there has been a shift in interest away from traditional polygraph examinations toward the analysis of facial expressions, voice, and speech patterns, as well as eye-tracking signals to detect deceptive behavior. In this paper, we focus on the last aspect, offer a comprehensive overview of two distinct lie detection methodologies based on eye tracking, and examine the commonly used oculomotor feature analysis. Furthermore, we explore current research directions and their results within the context of their potential applications in the field of forensics. We also highlight future research prospects, suggesting the utilization of eye tracking and scan path interpretation methodologies as a potential fully functional alternative for the conventional polygraph in the future. These considerations refer to legal and ethical issues related to the use of new technology to detect lies.

Motivation: The advancement of preventive medicine and, subsequently, telemedicine drives the need for noninvasive and remote measurements in patients’ natural environments. Heart rate (HR) measurements are particularly promising and extensively researched due to their quick assessment and comprehensive representation of patients’ conditions. However, in scenarios such as endurance training or emergencies, where HR measurement was not anticipated and direct access to victims is limited, no method enables obtaining HR results that are suitable even for triage. Methods: This paper presents the possibility of remotely measuring of human HR from a series of in-flight videos using videoplethysmography (VPG) along with skin detection, human pose estimation and image stabilization methods. An unmanned aerial vehicle (UAV) equipped with a camera captured ten segments of video footage featuring volunteers engaged in free walking and running activities in natural sunlight. The human pose was determined using the OpenPose algorithm, and subsequently, skin areas on the face and forearms were identified and tracked in consecutive frames. Ultimately, HR was estimated using several VPG methods: the green channel (G), green-red difference (GR), excess green (ExG), independent component analysis (ICA), and a plane orthogonal to the skin (POS). Results: When compared to simultaneous readings from a reference ECG-based wearable recorder, the root-mean-squared error ranged from 17.7 (G) to 27.7 (POS), with errors of less than 3.5 bpm achieved for the G and GR methods. Conclusions: These results demonstrate the acceptable accuracy of touchless human pulse measurement with the accompanying UAV-mounted camera. The method bridges the gap between HR-transmitting wearables and emergency HR recorders, and it has the potential to be advantageous in training or rescue scenarios in mountain, water, disaster, or battlefield settings.

We propose a method for noninvasive heart rate (HR) estimation from ultra-high-definition (4K) UAV-recorded videos, leveraging general-purpose instance segmentation without retraining or manual annotation. The method integrates Fast Segment Anything (FastSAM) with silhouette tracking and signal processing techniques. To address segmentation reliability, we introduce a lightweight heuristic metric (C_seg) that detects abrupt changes in the region of interest, enabling the rejection of unreliable frames. Furthermore, a genetic algorithm is further employed to fine-tune preprocessing and estimation parameters. The method was evaluated on two datasets: the indoor, controlled UBFC-rPPG dataset and a challenging UAV dataset recorded outdoors. On the UBFC-rPPG dataset, our approach achieved up to 76% improvement in heart rate estimation accuracy compared to a baseline algorithm. On the UAV dataset, results indicate more modest gains, limited by motion artifacts, compression, and lighting variability. This work highlights the potential of generic foundation segmentation models in remote physiological monitoring and identifies key challenges for real-world UAV-based applications. Of particular importance here is the fact that the studied objects are moving, while previous studies mainly have focused on stationary objects. Our findings demonstrate that carefully designed segmentation and parameter tuning strategies can enhance rPPG performance in favorable conditions, while also emphasizing the need for future work in motion compensation, segmentation validation, and deployment on embedded systems.