Automatic Calculation of the Rheoencephalographic Pulse Wave Peaks

Istvan Pinter, Mihaly Bagany, Sandor Szabo, Michael Bodo — Tue, 27 Jan 2026 00:00:00 +0000

To create an algorithm to detect rheoencephalographic (REG) pulse wave second peak increase, which may detect an increase in intracranial pressure (ICP). REG was measured in 19 healthy volunteers during control and a 15-degree head-down tilt (HDT), which caused an increase in ICP. We developed an algorithm for automatically calculating P1 and P2 from the REG pulse waveform. The result was compared to manual measurements during control and HDT positions. The automatic determination of the peaks’ time instants was considered a series of two-class decision problems in ±15-ms-wide sliding decision windows. We achieved an accuracy of 0.9826, a sensitivity of 0.7727, and a specificity of 0.9902. We used the correlation coefficient between manual measurements and automated data and the normalized mean absolute error (NMAE) metric to characterize the precision of peak amplitude value estimation. We achieved a high correlation (> 0.8) in 92% of all tests, and NMAE < 0.3 in 96%. The remaining cases were analyzed using Bland-Altman plots to uncover the main causes of differences. We tested the hypothesis of increasing P2 peak amplitude during HDT compared to the control position. In the female group, the number of significant increases was 4 out of 6 (67%), both in manual and automatic measurements. In the case of the male group’s manual measurements, 12 of 13 (92%), and the automatic calculation gave 10 of 13 (77%). The significance is to switch from invasive ICP to noninvasive REG to have the same information for decision-making at the bedside to save the lives of neurocritical care patients. Future REG correlation studies suggested using REG in neurocritical care monitoring, space research, and military medical practice.

The Vilans Observation Tool for Human-Technology Interaction

Eniko Agotai, Bob M Hofstede — Thu, 29 Jan 2026 00:00:00 +0000

The increasing development and adoption of gerontechnology, which are assistive technologies designed to support older adults’ independence, well-being, and social connection, underscore the critical need for robust methods to assess their real-world impact. While observational tools are essential for this purpose, existing methods often present a trade-off between efficiency and depth. Rating scales are simple to use but lack the temporal and behavioural granularity needed to analyse interaction processes, while detailed behavioural coding schemes are resource-intensive and inflexible. This paper addresses a significant gap by introducing the Vilans Observation Tool for Human-Technology Interaction (VOTHI) framework, a novel, low-threshold, hybrid observational tool. Developed through a literature analysis of existing instruments, VOTHI combines the structural clarity of rating scales with the temporal sensitivity of coding schemes, organized in a modular structure to adapt to various research contexts and technologies. The framework’s design principles emphasise the holistic capture of interactional dynamics, including verbal, non-verbal, affective, and contextual cues, focusing on the process of interaction rather than just the outcome. Pilot-testing demonstrates VOTHI’s capacity to provide a comprehensive, adaptable, and practical method for researchers to analyse human-technology interactions. The tool serves as a valuable solution for capturing subtle, dynamic, and process-oriented aspects of engagement, particularly in gerontechnological contexts.

International Journal of Methodology

Automatic Calculation of the Rheoencephalographic Pulse Wave Peaks

The Vilans Observation Tool for Human-Technology Interaction