ABSTRACT
This paper evaluates a robot that distributed hand-sanitizer over an eight month period (October 2020-June 2021) in public places on the Oregon State University campus. During COVID times, many robots have been deployed in public places as social distancing enforcers, food delivery robots, UV-sanitation robots and more, but few studies have assessed the social situations of these robots. Using the context of robot distributing hand sanitizer, this work explores the benefits that social robots may provide to encouraging healthy human activities, as well as ways in which street-performance inspired approaches and a bit of humor might improve the quality and experience of functional human-robot interactions. After gaining human-in-the-loop deployment experience with a customized interface to enable both planned and improvized responses to human bystanders, we run two sub-studies. In the first, we compare the performance of the robot (moving or still) relative to a traditional hand sanitizer dispenser stick (N=2048, 3 week data collection period). In the second, we evaluate how varied utterance strategies further impact the interaction results (N=185, 2 week data collection period). The robot dramatically outperforms the stick dispenser across all tracked behavioral variables, cuing high levels of positive social engagement. This work finds the utterance design is more complex socially, and offer insights to future robot designers about how to integrate helpful and playful speech into service robot interactions. Finally, across both sub-studies, the work shows that people in groups are more likely to engage with the robot and each other, as well as sanitize their hands. © 2022 IEEE.
ABSTRACT
With the advent of the SARS-CoV-2 pandemic, Wastewater-Based Epidemiology (WBE) has been applied to track community infection in cities worldwide and has proven succesful as an early warning system for identification of hotspots and changingprevalence of infections (both symptomatic and asymptomatic) at a city or sub-city level. Wastewater is only one of environmental compartments that requires consideration. In this manuscript, we have critically evaluated the knowledge-base and preparedness for building early warning systems in a rapidly urbanising world, with particular attention to Africa, which experiences rapid population growth and urbanisation. We have proposed a Digital Urban Environment Fingerprinting Platform (DUEF) - a new approach in hazard forecasting and early-warning systems for global health risks and an extension to the existing concept of smart cities. The urban environment (especially wastewater) contains a complex mixture of substances including toxic chemicals, infectious biological agents and human excretion products. DUEF assumes that these specific endo- and exogenous residues, anonymously pooled by communities' wastewater, are indicative of community-wide exposure and the resulting effects. DUEF postulates that the measurement of the substances continuously and anonymously pooled by the receiving environment (sewage, surface water, soils and air), can provide near real-time dynamic information about the quantity and type of physical, biological or chemical stressors to which the surveyed systems are exposed, and can create a risk profile on the potential effects of these exposures. Successful development and utilisation of a DUEF globally requires a tiered approach including: Stage I: network building, capacity building, stakeholder engagement as well as a conceptual model, followed by Stage II: DUEF development, Stage III: implementation, and Stage IV: management and utilization. We have identified four key pillars required for the establishment of a DUEF framework: (1) Environmental fingerprints, (2) Socioeconomic fingerprints, (3) Statistics and modelling and (4) Information systems. This manuscript critically evaluates the current knowledge base within each pillar and provides recommendations for further developments with an aim of laying grounds for successful development of global DUEF platforms.