This paper presents the application of FATHOM, a computerised non-verbal comprehension detection system, to distinguish participant comprehension levels in an interactive tutorial. FATHOM detects high and low levels of human comprehension by concurrently tracking multiple non-verbal behaviours using artificial neural networks. Presently, human comprehension is predominantly monitored from written and spoken language. Therefore, a large niche exists for exploring human comprehension detection from a non-verbal behavioral perspective using artificially intelligent computational models such as neural networks. In this paper, FATHOM was applied to a video-recorded exploratory study containing a learning task designed to elicit high and low comprehension states from the learner. The learning task comprised of watching a video on termites, suitable for the general public and an interview led question and answer session. This paper describes how FATHOM's comprehension classifier artificial neural network was trained and validated in comprehension detection using the standard backpropagation algorithm. The results show that high and low comprehension states can be detected from learner's non-verbal behavioural cues with testing classification accuracies above 76%.

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%.

This paper presents a case study on the development and deployment of a computerised, non-invasive psychological profiling system which detects human comprehension through the monitoring of multiple channels of facial nonverbal behaviour using Artificial Neural Networks (ANN). Prior work on an earlier system known as Silent Talker, led to collaborations and a funded project with Family Health International 360 (FHI 360) in collaboration with the National Institute of Medical Research (NIMR), to produce the FATHOM system for measuring comprehension of the informed consent process amongst women in Tanzania. This paper describes the process of taking a working research prototype and deploying the system in a real working environment. The paper discusses the process of contract negotiation, global ethics and intellectual property rights. The FATHOM system and initial results are described.