Implementation of wavelet analysis on thermal images for affective states recognition of children with autism spectrum disorder

Children with Autism Spectrum Disorder are identified as a group of people who has difficulties in socio-emotional interaction. Most of them lack the proper context in producing social response through facial expression and speech. Since emotion is the key for effective social interaction, it is justifiably vital for them to comprehend the correct emotion expressions and recognitions. Emotion is a type of affective states and can be detected through physical reaction and physiological signals. In general, recognition of affective states from physical reaction such as facial expression and speech for autistic children is often unpredictable. Hence, an alternative method of identifying the affective states through physiological signals is proposed. Though considered non-invasive, most of the current recognition methods require sensors to be patched on to the skin body to measure the signals. This would most likely cause discomfort to the children and mask their 'true' affective states. The study proposed the use of thermal imaging modality as a passive medium to analyze the physiological signals associated with the affective states nonobtrusively. The study hypothesized that, the impact of cutaneous temperature changes due to the pulsating blood flow in the blood vessels at the frontal face area measured from the modality could have a direct impact to the different affective states of autistic children. A structured experimental setup was designed to measure thermal imaging data generated from different affective state expressions induced using different sets of audio-video stimuli. A wavelet-based technique for pattern detection in time series was deployed to spot the changes measured from the region of interest. In the study, the affective state model for typical developing children aged between 5 and 9 years old was used as the baseline to evaluate the performance of the affective state classifier for autistic children. The results from the classifier showed the efficacy of the technique and accorded good performance of classification accuracy at 88% in identifying the affective states of autistic children. The results were momentous in distinguishing basic affective states and the information could provide a more effective response towards improving social-emotion interaction amongst the autistic children

Using Polygraph to Detect Passengers Carrying Illegal Items

The present study examined the effectiveness of a Modified-Comparison Questions Technique, used in conjunction with the polygraph, to differentiate between common travelers, drug traffickers, and terrorists at transportation hubs. Two experiments were conducted using a mock crime paradigm. In Experiment 1, we randomly assigned 78 participants to either a drug condition, where they packed and lied about illicit drugs in their luggage, or a control condition, where they did not pack or lie about any illegal items. In Experiment 2, we randomly assigned 164 participants to one of the two conditions in Experiment 1 or an additional bomb condition, where they packed and lied about a bomb in their luggage. For both experiments, we assessed participants’ RR interval, heart rate, peak-to-peak amplitude of Galvanic Skin Response (GSR) and all three combined, using Discriminant Analyses to determine the classification accuracy of participants in each condition. In both experiments, we found decelerated heart rates and increased peak-to-peak amplitude of GSR in guilty participants when lying in response to questions regarding their crime. We also found accurate classifications of participants, in both Experiment 1 (drug vs. control: 84.2% vs. 82.5%) and Experiment 2 (drug vs. control: 82:1% vs. 95.1%; bomb vs. control: 93.2% vs. 95.1%; drug vs. bomb: 92.3% vs. 90.9%), above chance level. These findings indicate that Modified-CQT, combined with a polygraph test, is a viable method for investigating suspects of drug trafficking and terrorism at transportation hubs such as train stations and airports