Online research has revolutionized behavioral research by offering access to diverse and large sample sizes, quicker data collection, and opening up new research avenues not feasible in laboratory settings. However, online methods have not benefited multimodal research as much, which typically includes audiovisual stimuli and collection of audiovisual data. In such research, collection, transfer, and storage of audiovisual data online pose significant challenges. This paper discusses the methodological challenges in conducting online multimodal research, reviews the current experimental tools for online behavioral research, and explains why these tools fail to meet the specific requirements of online multimodal research. We then introduce AME, the Autonomous Multimodal Experiment System, a modular experiment system designed to support interactive online multimodal research in an autonomous way. We discuss the technical stacks that AME employs and how the architecture and combination of technical stacks addresses the challenges of conducting multimodal online research and opens up immense potential for online multimodal research. We validate the autonomous workflow of AME using a gesture production task, demonstrating the efficiency and reliability of AME's workflow.
No takes yet. Share an insight, caveat, or question.
Yang et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: