Validating dynamic time warping as a measure of gesture form similarity

Dynamic time warping (DTW) is a well-known algorithm used to assess the similarity between signals of varying lengths. Initially developed for automatic speech recognition, DTW has found applications in psycholinguistics, particularly in analyzing gesture form similarity. An open question in this domain is how effectively DTW captures gesture form similarity. Here, we validate DTW against human annotations of gesture form similarity across two multimodal interaction corpora and explore its utility as an automatic, continuous measure of gesture form similarity. Our findings reveal weak to moderate correlations between DTW distance and the number of gesture features---such as hand shape, movement, orientation, and position---suggesting that DTW serves as a useful proxy for gesture form similarity. Additionally, we highlight the importance of qualitative analysis of raw data and DTW predictions in enhancing DTW's predictive accuracy. Our study offers a rigorous validation of DTW as a measure of gesture form similarity and presents a detailed framework for preprocessing motion tracking data and calculating DTW distance. While none of the methods is perfect, the combination of automatic and manual measures provides a comprehensive approach to understanding and measuring gesture form similarity.