Evaluating the probative value of forensic gait analysis evidence using empirical data

TL;DR

This study evaluates the probative value of forensic gait analysis evidence using empirical data, exploring variability, correlation, and likelihood ratio modeling to assist expert judgment.

Contribution

It introduces a likelihood ratio model based on gait features and PCA, highlighting the importance of human expertise in interpretation.

Findings

High correlations between gait features suggest they should not be independent evidence.

Likelihood ratio model was accurate in over 90% of comparisons using PCA.

Mis-specification of within-individual variability increases risk of misleading results.

Abstract

Forensic gait analysis can aid the investigation of crimes through comparing features of gait captured in video footage. Modelling the probative value of gait evidence requires an understanding of the variation of features of gait between individuals in the population and within the same individuals. We address this question using a previously described population dataset and newly collected datasets with repeated observations of the same individuals on separate occasions. In addition to exploring the level of variability, correlation between features of gait, and the effect of demographic factors, we developed a likelihood ratio model through recoding features of gait as dichotomous variables and dimension reduction using PCA. High correlations between some features were observed, confirming that they should not contribute independently to the weight of evidence. The likelihood ratio model produced misleading likelihood ratios in less than 10% of the comparisons using the first four principal components. However, the risk increases when within-individual variability is mis-specified. Therefore, while the current model provides assistance to the judgement of gait experts, human expertise is indispensable to decide whether or not the difference in walking and/or recording conditions between the reference and questioned footage could have caused any observed differences in the features of gait. We discuss future directions in understanding the sources of the variability, improving statistical modelling and note the need to consider carefully how to select the relevant population for model fitting.