It has long been reported that children with autism spectrum disorder (ASD) exhibit attention difficulties while learning. They tend to focus on irrelevant information and can easily be distracted. As a result, they are often confined to a one-to-one teaching environment, with fewer distractions and social interactions than would be present in a mainstream educational setting. In recent years, inclusive mainstream schools have been growing in popularity due to government policies on equality rights. Therefore, it is crucial to investigate attentional patterns of children with ASD in mainstream schools. This study aims to explore the attentional behaviors of children with ASD in a virtual reality simulated classroom. We analyzed four eye-gaze behaviors and performance scores of 45 children: children with ASD (ASD n = 20) and typically developing children (TD n = 25) when performing attention tasks. The gaze behaviors included time to first fixate (TTFF), first fixation duration (FFD), average fixation duration (AFD) and the sum of fixation count (SFC) on fourteen areas of interest (AOIs) in the classroom. Our results showed that children with ASD exhibit similar gaze behaviors to TD children, but with significantly lower performance scores and SFC on the target AOI. These findings showed that classroom settings can influence attentional patterns and the academic performance of children with ASD. Further studies are needed on different modalities for supporting the attention of children with ASD in a mainstream setting.

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Cardiovascular diseases (CVD) are the leading cause of death globally. According to the World Health Organization, around 17.3 million people died from CVD in 2008, which is estimated to increase to 23 million annually by 2030. This calls for the exploitation of the latest technology to analyze and detect heart diseases in a timely fashion. Electrocardiogram (ECG) records the electrical activity of heart, and any irregularities in the heart rhythm are used as indicators of possible heart diseases. However, the process of acquiring an ECG and visually inspecting it is laboursome and time consuming. It is in this context that developing a mobile ECG monitoring system based on smart phones and automated diagnostic algorithms prove significant. The ECG acquiring system being developed by our team transmits ECG from the human body to the patient's smart phone in real-time via Bluetooth. This is completed by an application running on the phone. Two approaches are being developed to process the data received. The first is to send the data from the phone to a Webserver where the data will be analyzed and processed and lastly send the results back; this can be done through Wi-fi or a 3G connection. The second approach is to run the algorithm within the application on the smart phone itself. This way, the data can be processed locally, and the results can be displayed in case of no network. Both approaches are being compared for pros and cons. In the first approach, the Webserver can be updated frequently without modifying the application. Moreover, the usage of RAM memory will be efficient, and the code can be preserved from corruption. Nevertheless, this process will not succeed if the server fails or if the network interrupts. The second approach can process the data without network connectivity. However, it is more demanding on the phone, more susceptible to bugs and more difficult to update and debug the C code. The necessary hardware to acquire ECG and transmit it to the smart phone has been developed. Also, a robust diagnostic algorithm and an interactive smart phone application are being built. On integration, the system will be capable of analyzing ECG in real time and providing early warning to patients under cardiac risk saving valuable time and effort in their treatment.