Augmented reality (AR) has been extensively applied to health science education. However, a question still remains as to the optimal way to design AR interfaces to enhance users’ understanding of information that is presented in text format. Utilising the context of indoor air pollution, the current study examines the effects of messages about particulate matter (PM) delivered using different modes of AR visualisation (i.e. overlaying AR visuals onto users’ faces [AR-overlay self] vs. overlaying AR visuals onto users’ surroundings [AR-overlay environment] vs. no use of AR [AR control]). A between-subjects online experiment (N = 306) demonstrated that the AR-overlay environment has a pronounced effect on perceived realism toward 3D-rendered PM, which induced powerful effects on increasing both issue involvement and positive attitudes toward the textual information being presented. Compared to the AR control, the AR-overlay environment demonstrated a serial and positive mediation effect on perceived realism, which subsequently influenced issue involvement and message attitudes. However, overlaying 3D-rendered PM onto one’s face turned out to be a less effective means of eliciting perceived realism. Implications are discussed in the context of how to design effective AR interfaces that can best help users understand health science information.

Walking impairment is a debilitating symptom of Multiple Sclerosis (MS), a disease affecting 2.8 million people worldwide. While clinicians’ in-person observational gait assessments are important, research suggests that data from wearable sensors can indicate early onset of gait impairment, track patients’ responses to treatment, and support remote and longitudinal assessment. We present an inquiry into supporting the transition from research to clinical practice. Co-design by HCI, biomedical, neurology and rehabilitation researchers resulted in a data-rich interface prototype for augmented gait analysis based on visualized sensor data. We used this as a prompt in interviews with ten experienced clinicians from a range of MS rehabilitation roles. We find that clinicians value quantitative sensor data within a whole patient narrative, to help track specific rehabilitation goals, but identify a tension between grasping critical information quickly and more detailed understanding. Based on the findings we make design recommendations for data-rich remote rehabilitation interfaces.

Audio annotation is an important step in developing machine-listening systems. It is also a time consuming process, which has motivated investigators to crowdsource audio annotations. However, there are many factors that affect annotations, many of which have not been adequately investigated. In previous work, we investigated the effects of visualization aids and sound scene complexity on the quality of crowdsourced sound-event annotations. In this paper, we extend that work by investigating the effect of sound-event loudness on both sound-event source annotations and sound-event proximity annotations. We find that the sound class, loudness, and annotator bias affect how listeners annotate proximity. We also find that loudness affects recall more than precision and that the strengths of these effects are strongly influenced by the sound class. These findings are not only important for designing effective audio annotation processes, but also for effectively training and evaluating machine-listening systems.

Audio annotation is key to developing machine-listening systems; yet, effective ways to accurately and rapidly obtain crowdsourced audio annotations is understudied. In this work, we seek to quantify the reliability/redundancy trade-off in crowdsourced soundscape annotation, investigate how visualizations affect accuracy and efficiency, and characterize how performance varies as a function of audio characteristics. Using a controlled experiment, we varied sound visualizations and the complexity of soundscapes presented to human annotators. Results show that more complex audio scenes result in lower annotator agreement, and spectrogram visualizations are superior in producing higher quality annotations at lower cost of time and human labor. We also found recall is more affected than precision by soundscape complexity, and mistakes can be often attributed to certain sound event characteristics. These findings have implications not only for how we should design annotation tasks and interfaces for audio data, but also how we train and evaluate machine-listening systems.