Using digital archaeology and machine learning to determine sex in finger flutings.

ABSTRACT: One of the earliest and most enigmatic forms of rock art are finger flutings and previous methods of studying them relied on biometric finger ratios from modern populations to make assumptions about the people who left the flutings, which is theoretically and methodologically problematic. This work is a proof-of-concept for a paradigm shift away from error-prone human measurements and controversial theories to computational digital archaeology methods for an innovative experimental design using a tactile, virtual, and machine learning approach. We propose a digital archaeology experiment using a tactile and virtual approach based on multiple samples from 96 participants. We trained a machine learning model on the known data to determine the sex of the person who made the fluting. While the

SUBMITTER: Jalandoni A 

PROVIDER: S-EPMC12531323 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature