Space Vision

This paper presents a novel mobile serious game, "Space Vision", which uses a hidden-object mechanic with fiducial marker detection to gamify a clinical test of visual acuity - a key marker of childhood eye disease. For Space Vision to become a credible clinical tool that can facilitate the screening and home-monitoring of children's visual acuity, it must be able to sustain player engagement over the extended durations required to detect vision abnormalities. Hence, we pay particular attention to developing effective game characters - a crucial aspect of children's game design. Using an early prototype with 13 school children (aged 5-6 years), we investigate player experiences through a series of evaluation sessions, involving a single one-to-one observational playtest, semi-structured interview, and ideation activity with each child. Thematic analysis of our session video recordings, written observations, and ideation artefacts, found that future game iterations must: increase resonance between players and game characters by providing aesthetic and behavioural customisation, embrace more anthropomorphic styling, better imbue narratives pertaining to children's life scripts, and feature fantasy character powers as a form of self-expression. Meanwhile, greater physical device support and digital incentivisation of body posture and head position are key to improving the reliability of the visual acuity measurement.

The Design of Augmented Reality Using Unity 3D Image Marker Detection for Smart Bus Transportation

Public bus transportation has become an integral part of society, but the disrup-tion of bus services is one of the major concerns. This project presents the devel-opment of Smart Bus Transportation using Augmented Reality (TRANSPAR) that was developed on a mobile application. One of the major issues with public transportation is on real-time responsiveness. Most bus schedules are presented online but customers still faced many failures. Some bus schedules are not updat-ed when changes happened through time. Some existing bus schedules system is fixed to the bus stations. This research is to identify the bus schedules and its routes characteristics. A 3D AR animation based on identified characteristics was designed using the Unity 3D image marker detection on a mobile Android plat-form. A smartphone application was developed using Vuforia and Google Fire-base. TRANSPAR shows an AR mobile application for acquiring the bus time-tables. The phone camera is applied for marker image detection and scanning the bus station’s images. AR and normal image scanner were designed. Google Fire-base Database is used to retrieve and store each timetable data for every bus sta-tion. Analysis of interactivity and benefits of TRANSPAR shows about 90% agreed on the use of AR and more than 76% agreed on its functionality based on 50 taken samples. This shows a positive impact on the designed TRANSPAR. The research is significant to encourage and experience the public with new tech-nological application for public transportation and it impact the society.

Culture-Free Detection of Antibiotic Resistance Markers from Native Patient Samples by Hybridization Capture Sequencing

The increasing incidence of antimicrobial resistance (AMR) is a major global challenge. Routine techniques for molecular AMR marker detection are largely based on low-plex PCR and detect dozens to hundreds of AMR markers. To allow for comprehensive and sensitive profiling of AMR markers, we developed a capture-based next generation sequencing (NGS) workflow featuring a novel AMR marker panel based on the curated AMR database ARESdb. Our primary objective was to compare the sensitivity of target enrichment-based AMR marker detection to metagenomics sequencing. Therefore, we determined the limit of detection (LOD) in synovial fluid and urine samples across four key pathogens. We further demonstrated proof-of-concept for AMR marker profiling from septic samples using a selection of urine samples with confirmed monoinfection. The results showed that the capture-based workflow is more sensitive and requires lower sequencing depth compared with metagenomics sequencing, allowing for comprehensive AMR marker detection with an LOD of 1000 CFU/mL. Combining the ARESdb AMR panel with 16S rRNA gene sequencing allowed for the culture-free detection of bacterial taxa and AMR markers directly from septic patient samples at an average sensitivity of 99%. Summarizing, the newly developed ARESdb AMR panel may serve as a valuable tool for comprehensive and sensitive AMR marker detection.