Detecting human comprehension from nonverbal behaviour using artificial neural networks

Every day, communication between humans is abundant with an array of nonverbal behaviours. Nonverbal behaviours are signals emitted without using words such as facial expressions, eye gaze and body movement. Nonverbal behaviours have been used to identify a person’s emotional state in previous research. With nonverbal behaviour being continuously available and almost unconscious, it provides a potentially rich source of knowledge once decoded. Humans are weak decoders of nonverbal behaviour due to being error prone, susceptible to fatigue and poor at simultaneously monitoring numerous nonverbal behaviours. Human comprehension is primarily assessed from written and spoken language. Existing comprehension assessments tools are inhibited by inconsistencies and are often time-consuming with feedback delay. Therefore, there is a niche for attempting to detect human comprehension from nonverbal behaviour using artificially intelligent computational models such as Artificial Neural Networks (ANN), which are inspired by the structure and behaviour of biological neural networks such as those found within the human brain. This Thesis presents a novel adaptable system known as FATHOM, which has been developed to detect human comprehension and non-comprehension from monitoring multiple nonverbal behaviours using ANNs. FATHOM’s Comprehension Classifier ANN was trained and validated on human comprehension detection using the errorbackpropagation learning algorithm and cross-validation in a series of experiments with nonverbal datasets extracted from two independent comprehension studies where each participant was digitally video recorded: (1) during a mock informed consent field study and (2) in a learning environment. The Comprehension Classifier ANN repeatedly achieved averaged testing classification accuracies (CA) above 84% in the first phase of the mock informed consent field study. In the learning environment study, the optimised Comprehension Classifier ANN achieved a 91.385% averaged testing CA. Overall, the findings revealed that human comprehension and noncomprehension patterns can be automatically detected from multiple nonverbal behaviours using ANNs

An exploration into the influence of schizotypic maternal personality on early sensory development

It has been known for some time that maternal personality is an influential factor in determining developmental and clinical outcomes in childhood risk for mental health. Current literature describes schizotypy as a multidimensional construct, representing a vulnerability to the schizophrenia-spectrum. This thesis investigates atypicalities observed throughout the spectrum aiming to determine whether these were present in mothers with sub-clinical schizotypy, and their offspring. Chapter 2 explored sensory gating in infants at 6-months of age. Infants displayed intact sensory gating, and there was no difference between infants of schizotypic and those of control mothers. The mothers of the infants displayed significant differences between Stimulus 1 and Stimulus 2, but also differences as a result of their schizotypy dimensionality; replicating prior literature. Similarly, in Chapter 3, schizotypic mothers displayed reduced oscillatory power towards Stimulus 1 of the paired-tone paradigm, replicating prior literature. In contrast, their infants showed no group differences. This implies that having a mother with schizotypic traits does not influence the sensory gating ability of their 6-month-old infants. Chapter 4 demonstrated that 6-month-old infants differentiated between happy and fearful emotional facial expressions, replicating prior literature. Maternal schizotypy, however, did not influence this ability. When exploring face processing in the maternal sample, schizotypic mothers exhibited greater amplitudes towards both facial expressions when contrasted with non-schizotypic mothers. In Chapter 5 we explored relationships between schizotypy and mother-child interactions in a free play session. We found that oscillatory power shown by infants in their left and right parietal regions was greater when their mother was talking to them, or when they were playing independently with a toy, compared to a baseline. No significant differences were observed between infants of schizotypic, and those of control mothers. Despite a lack of infant group effects, it is important to explore schizotypal expression during adolescence and adulthood as a critical link to childhood risk markers, which confer a role of developmental facilitators on the road to psychosis proneness. This thesis concludes that schizotypy is linked to the schizophrenia-spectrum, as shown consistently by maternal electrophysiological data, but that maternal level of schizotypy did not have an effect on infant markers

Life Sciences Program Tasks and Bibliography for FY 1997

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1997. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive internet web page

Investigation and Quantification of FES Exercise – Isometric Electromechanics and Perceptions of Its Usage as an Exercise Modality for Various Populations

Functional Electrical Stimulation (FES) is the triggering of muscle contraction by use of an electrical current. It can be used to give paralyzed individuals several health benefits, through allowing artificial movement and exercise. Although many FES devices exist, many aspects require innovation to increase usability and home translation. In addition, the effect of changing electrical parameters on limb biomechanics is not entirely understood; in particular with regards to stimulation duty cycle. This thesis has two distinct components. In the first (public health component), interview studies were conducted to understand several issues related to FES technology enhancement, implementation and home translation. In the second (computational biomechanics component), novel signal processing algorithms were designed that can be used to measure mechanical responses of muscles subjected to electrical stimulation. These experiments were performed by changing duty cycle and measuring its effect on quadriceps-generated knee torque. The studies of this thesis have presented several ideas, toolkits and results which have the potential to guide future FES biomechanics studies and the translatability of systems into regular usage for patients. The public health studies have provided conceptual frameworks upon which FES may be used in the home by patients. In addition, they have elucidated a range of issues that need to be addressed should FES technology reach its true potential as a therapy. The computational biomechanics studies have put forward novel data analysis techniques which may be used for understanding how muscle responds to electrical stimulation, as measured via torque. Furthermore, the effect of changing the electrical stimulation duty cycle on torque was successfully described, adding to an understanding of how electrical stimulation parameter modulation can influence joint biomechanics