Children with autism spectrum disorder (ASD) frequently encounter challenges in social communication and interaction, which necessitates continuous, comprehensive interventions to enhance their communication skills. Despite the growing interest in digital therapeutics (DTx), research on speech-utilizing interventions for children with ASD remains limited. This study introduces speech-based technologies integrated into a DTx designed to facilitate the development of communicative skills in children with ASD. Initially, we compiled a large speech corpus from children with ASD and typically developing children, which includes clinical scores on social communication severity and speech sound, rated by certified speech and language pathologists. Then three speech-based technologies were developed: automatic speech recognition for verbal interaction with the DTx, automatic assessment of social communication severity to monitor progress, and automatic speech sound assessment to foster speech production skills. The results were promising, demonstrating a syllable error rate of 12.36% in automatic speech recognition for keywords, a 0.71 correlation coefficient for assessing social communication, and a 0.75 correlation coefficient for speech sound assessment. These technologies are expected to improve the accessibility of interventions for children with ASD, overcoming barriers related to space, time, and human resources.

The use of physically passive assistive technologies like electric wheelchairs and electric scooters by people with some walking function runs the risk of de-conditioning the user's physical functionality and their mobile capabilities at a faster rate than if they had used a more physically active assistive technology. This project explores the information necessary to support the combined use of passive and active assistive technology for mobility, aiming to enhance well-being. The study utilized an online survey to gather insights from individuals in Hong Kong using multiple mobility devices. The survey focused on demographic data, user behaviour, mobility device usage, IoT elements, and information requirements. Results from 263 respondents revealed the prevalence of mixed active and passive mobility device usage, shedding light on the use of IoT devices and the essential information needed for commuting, device selection, and overall well-being. The findings provide valuable insights for healthcare professionals, policymakers, and technology developers aiming to improve assistive technology systems and promote well-being in the context of mobility.

Project Presentation as part of the session on eHealth