Bloodstain impact pattern Area of Origin estimation using least-squares angles: A HemoVision validation study. (April 2022)

• Record Type: Journal Article
• Authors: Joris, Philip; Jenar, Els; Moermans, Ruben; Van de Voorde, Wim; Vandermeulen, Dirk; Claes, Peter
• Is Part Of: Forensic science international. Volume 333(2022)
• Publication Date: 2022-04
• Subjects: BPA -- HemoVision -- Area of Origin -- Validation -- Optimisation; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2022.111211
• Languages: English
• ISSNs: 0379-0738

Abstract

Highlights: HemoVision is software that automates bloodstain impact pattern analysis. Has not been validated and uses tangent method, which is known to be biased. Propose method that formulates Area of Origin estimation as optimisation problem. Present results from validation study using controlled experiments. Proposed method significantly outperforms others and is significantly more robust. Graphical Abstract: ga1 Abstract: Bloodstain impact pattern Area of Origin (AO) estimation is an important but time-consuming process in criminal investigations. HemoVision is a software package that automates and accelerates this process. To date, however, no study has been published that evaluates HemoVision's accuracy. Moreover, HemoVision relies on an automated variant of the tangent method to estimate a pattern's AO, even though the use of front-view projections has been shown to provide biased AO estimates. Therefore, the goal of this paper is twofold. First, a novel AO estimation method is proposed, whereby AO estimation is formulated as a least-squares optimisation problem that operates in three dimensions directly, eliminating the need for front view projections. Second, ten impact patterns with known AO coordinates at both 50 cm and 100 cm with respect to the target wall are created and used to compare the proposed approach's accuracy and robustness to the manual tangent method, HemoSpat, and HemoVision's automated tangent method. Results show that for impacts at 100 cm or less to the target wall, the proposed approach achieves the lowest average error of 17.29 cm with the least uncertainty, and that it performs significantly better than the manual tangent and automated tangent methods at a 5% significance level. Moreover, it is shown to achieve errors of less than 30 cm at these distances with just nine stains, whereas the automated tangent method requires a minimum of 16 stains to achieve the same average error.