UC Berkeley

Detailed contact tracing that not only captures the social interaction graph, but also precise interaction distance and duration could prove useful in a wide variety of applications. Most notably, we have seen this play out in the global COVID-19 pandemic, where social distancing and contact tracing have proven critical in efforts to combat disease spread. Traditionally, contact tracing has relied on manual reporting, which provides only coarse grained data and relies on the subjectivity of human memory. These factors have led to a drive for wearable sensor based solutions which can provide objective face-to-face interaction data. Ideally, these sensors would provide precise interaction distances and durations, and would only report these metrics when users are actually facing each other and are not separated by a barrier. Current contact tracing sensors can generally be divided into two camps. First are sensors that can provide precise interaction distances, but require infrastructure to run, making them difficult to deploy in practice. Second are sensors that do not require infrastructure, but only provide a rough sense of proximity, making it difficult to analyze which interactions are significant. The majority of these systems also cannot determine if there is a barrier separating users, or if the users are facing each other.

To address these issues, we present Opo, a wearable sensor which requires no infrastructure to run, provides interaction distance accurate to 5~cm, and only records interaction distances when users are facing each other with no barriers between them. The key problem we identify is that systems that provide precise interaction distances require RF based neighbor discovery protocols to synchronize nodes before performing ranging operations to get interaction distance. Instead, Opo utilizes ultrasonic passive vigilance, to perform neighbor discovery and ranging at the same time, lowering system complexity and power usage.

In addition, while current wearables for contact tracing have largely focused on detecting interactions, in practice this information is greatly enriched by knowledge of health behaviors and symptoms. For example, researchers are often interested in detecting hand-washing behavior due to its importance in combating a wide variety of infectious diseases. Current hand washing sensor systems generally use wrist mounted accelerometers or utilize smart badges and soap-dispenser mounted sensors. Of these two system types, only smart badge plus soap dispenser sensor systems are able to capture if a person uses soap, a key consideration when measuring hand washing behavior. However, smart badge systems only detect when a person washes their hands with soap, and do not sense hand washing duration. The key problem is that current smart badge systems use low-resolution ranging technologies, making it difficult for them to determine when a user approaches leaves a sink. To address this problem, we create a smart badge plus dispenser mounted sensor system by extending Opo with passive vigilance in the accelerometric domain. This extension allows soap dispenser mounted Opos to passively detect when a dispenser is used and provide precise times when a user approaches and leaves a sink. To the best of our knowledge, our hand washing system is the first that can detect and categorize both soaped and un-soaped hand washing events and measure hand washing duration.

Researchers are also often interested in when people first experience symptom onsets. In particular, researchers are often interested in when people begin coughing, due to its prominence as an early symptom in many infectious diseases. Current cough sensing systems focus on counting the number of times a person coughs over a given period of time. These sensors require a user to wear a voice recorder and record all of their audio over the period of time. These systems then identify and count coughs in post-processing. This technique has shown very promising results, but requires a massive invasion of user privacy, making them difficult to deploy in many situations. In addition, our review of prior work on coughing shows that in many applications, simply knowing when a person starts coughing or general trends in a person's cough counts provides significant value. To fill this niche, we create CoughNote, a wearable privacy preserving cough sensor. Instead of constantly recording audio, CoughNote utilizes passive vigilance in the audio domain to capture 1~s snippets of potential coughs, while avoiding recording sensitive vocalized audio such as speech. These potential coughs can then be analyzed in post processing without violating user privacy. Although CoughNote does not capture every cough, it can show general cough trends while preserving usability and being smaller, lighter, and almost three times as long lived as a typical voice recorder.

Overall, our work creates a wearable sensing kit that researchers can use to study face-to-face interactions and important contextual health information. We have conducted two pilot studies using Opos and CoughNote with epidemiologists, and hope that our sensors enable future gains in better understanding and forecasting disease spread. Furthermore, our work shows the power of using passive vigilance to create complex, high-resolution wearables, and we hope that future wearable sensor designers draw inspiration from our designs.