Measuring Human Comprehension from Nonverbal Behaviour using Artificial Neural Networks

O'Shea, JD, Crockett, K, Bandar, Z and Buckingham, F (2016) Measuring Human Comprehension from Nonverbal Behaviour using Artificial Neural Networks. In: The 2012 International Joint Conference on Neural Networks (IJCNN).

Preview

Available under License In Copyright. Download (169kB) | Preview

Abstract

This paper presents the adaptation and application of Silent Talker, a psychological profiling system in the measurement of human comprehension through the monitoring of multiple channels of facial nonverbal behaviour using Artificial Neural Networks (ANN). Everyday human interactions are abundant with almost unconscious nonverbal behaviours accounting for approximately 93% of communication, providing a potentially rich source of information once decoded. Existing comprehension assessments techniques are inhibited by inconsistencies, limited to the verbal communication dimension and are often time-consuming with feedback delay. Major weaknesses hinder humans as accurate decoders of nonverbal behaviour with being error prone, inconsistent and poor at simultaneously focusing on multiple channels. Furthermore, human decoders are susceptible to fatigue and require training resulting in a costly, time-consuming process. ANNs are powerful, adaptable, scalable computational models that are able to overcome human decoder and pattern classification weaknesses. Therefore, the neural networks computer-based Silent Talker system has been trained and validated in the measurement of human comprehension using videotaped participant nonverbal behaviour from an informed consent field study. A series of experiments on training backpropagation ANNs with different topologies were conducted. The results show that comprehension and non comprehension patterns exist within the monitored multichannels of facial NVB with both experiments consistently yielding classification accuracies above 80%.