With the growing ubiquity of wearable devices, sensed physiological responses provide new means to connect with others. While recent research demonstrates the expressive potential for biosignals, the value of sharing these personal data remains unclear. To understand their role in communication, we created Significant Otter, an Apple Watch/iPhone app that enables romantic partners to share and respond to each other's biosignals in the form of animated otter avatars. In a one-month study with 20 couples, participants used Significant Otter with biosignals sensing OFF and ON. We found that while sensing OFF enabled couples to keep in touch, sensing ON enabled easier and more authentic communication that fostered social connection. However, the addition of biosignals introduced concerns about autonomy and agency over the messages they sent. We discuss design implications and future directions for communication systems that recommend messages based on biosignals. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; Empirical studies in collaborative and social computing; Empirical studies in ubiquitous and mobile computing.

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Rapid diagnostic tests (RDTs) provide point-of-care medical screening without the need for expensive laboratory equipment. RDTs are theoretically straightforward to use, yet their analog colorimetric output leaves room for diagnostic uncertainty and error. Furthermore, RDT results within a community are kept isolated unless they are aggregated by healthcare workers, limiting the potential that RDTs can have in supporting public health efforts. In light of these issues, we present a system called RDTScan for detecting and interpreting lateral flow RDTs with a smartphone. RDTScan provides real-time guidance for clear RDT image capture and automatic interpretation for accurate diagnostic decisions. RDTScan is structured to be quickly configurable to new RDT designs by requiring only a template image and some metadata about how the RDT is supposed to be read, making it easier to extend than a data-driven approach. Through a controlled lab study, we demonstrate that RDTScan's limit-of-detection can match, and even exceed, the performance of expert readers who are interpreting the physical RDTs themselves. We then present two field evaluations of smartphone apps built on the RDTScan system: (1) at-home influenza testing in Australia and (2) malaria testing by community healthcare workers in Kenya. RDTScan achieved 97.5% and 96.3% accuracy compared to RDT interpretation by experts in the Australia Flu Study and the Kenya Malaria Study, respectively.

Conversational agents are rich in content today. However, they are entirely oblivious to users' situational context, limiting their ability to adapt their response and interaction style. To this end, we explore the design space for a context augmented conversational agent, including analysis of input segment dynamics and computational alternatives. Building on these, we propose a solution that redesigns the input segment intelligently for ambient context recognition, achieved in a two-step inference pipeline. We first separate the non-speech segment from acoustic signals and then use a neural network to infer diverse ambient contexts. To build the network, we curated a public audio dataset through crowdsourcing. Our experimental results demonstrate that the proposed network can distinguish between 9 ambient contexts with an average F 1 score of 0.80 with a computational latency of 3 milliseconds. We also build a compressed neural network for on-device processing, optimised for both accuracy and latency. Finally, we present a concrete manifestation of our solution in designing a context-aware conversational agent and demonstrate use cases. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile computing; • Computing methodologies → Neural networks.

IntroductionDiagnostic tests for influenza in Australia are currently only authorised for use in clinical settings. At-home diagnostic testing for influenza could reduce the need for patient contact with healthcare services, which potentially could contribute to symptomatic improvement and reduced spread of influenza. We aim to determine the accuracy of an app-guided nasal self-swab combined with a lateral flow immunoassay for influenza conducted by individuals with influenza-like illness (ILI).Methods and analysisAdults (≥18 years) presenting with ILI will be recruited by general practitioners (GP) participating in Australian Sentinel Practices Research Network. Eligible participants will have a nasal swab obtained by their GP for verification of influenza A/B status using reverse transcription polymerase chain reaction (RT-PCR) test at an accredited laboratory. Participants will receive an influenza test kit and will download an app that collects self-reported symptoms and influenza risk factors, then instructs them in obtaining a low-nasal self-swab, running a QuickVue influenza A+B lateral flow immunoassay (Quidel Corporation) and interpreting the results. Participants will also interpret an enhanced image of the test strip in the app. The primary outcome will be the accuracy of participants’ test interpretation compared with the laboratory RT-PCR reference standard. Secondary analyses will include accuracy of the enhanced test strip image, accuracy of an automatic test strip reader algorithm and validation of prediction rules for influenza based on self-reported symptoms. A post-test survey will be used to obtain participant feedback on self-test procedures.Ethics and disseminationThe study was approved by the Human Research and Ethic Committee (HREC) at the University of Adelaide (H-2019-116). Protocol details and any amendments will be reported to https://www.tga.gov.au/. Results will be published in the peer-reviewed literature, and shared with stakeholders in the primary care and diagnostics communities.Trial registration numberAustralia New Zealand Clinical Trial Registry (U1111-1237-0688).