Sensing Contacts, Coughs, and Hand Hygiene
William Huang
EECS Department, University of California, Berkeley
Technical Report No. UCB/EECS-2020-212
December 17, 2020
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-212.pdf
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. This has led to a drive for wearable sensors 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. 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 do not account for barriers or directionality.
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, contact tracing 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. However, current systems cannot sense both soaped and un-soaped hand washing events, determine if soap is used, and detect hand washing duration, which are all key considerations. To address this problem, we create a smart badge plus dispenser mounted sensor system by extending Opo with passive vigilance in the accelerometric domain. 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 begin coughing, due to its prominence as an early symptom in many infectious diseases. Current cough sensing systems require a user to record all of their audio, identifying and counting coughs in post processing. This results in a massive invasion of user privacy, making them difficult to deploy. In addition, in many applications, simply knowing general trends in a person's cough counts provides significant value. To fill this niche, we create CoughNote, a wearable privacy preserving cough sensor. CoughNote utilizes passive vigilance in the audio domain to capture 1~s snippets of potential coughs, avoiding sensitive vocalized audio such as speech. These potential coughs can then be analyzed in post processing without violating user privacy. CoughNote does not capture every cough, but 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. 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.
Advisors: Prabal Dutta
BibTeX citation:
@phdthesis{Huang:EECS-2020-212,
Author= {Huang, William},
Title= {Sensing Contacts, Coughs, and Hand Hygiene},
School= {EECS Department, University of California, Berkeley},
Year= {2020},
Month= {Dec},
Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-212.html},
Number= {UCB/EECS-2020-212},
Abstract= {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. This has led to a drive for wearable sensors 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. 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 do not account for barriers or directionality.
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, contact tracing 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. However, current systems cannot sense both soaped and un-soaped hand washing events, determine if soap is used, and detect hand washing duration, which are all key considerations. To address this problem, we create a smart badge plus dispenser mounted sensor system by extending Opo with passive vigilance in the accelerometric domain. 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 begin coughing, due to its prominence as an early symptom in many infectious diseases. Current cough sensing systems require a user to record all of their audio, identifying and counting coughs in post processing. This results in a massive invasion of user privacy, making them difficult to deploy. In addition, in many applications, simply knowing general trends in a person's cough counts provides significant value. To fill this niche, we create CoughNote, a wearable privacy preserving cough sensor. CoughNote utilizes passive vigilance in the audio domain to capture 1~s snippets of potential coughs, avoiding sensitive vocalized audio such as speech. These potential coughs can then be analyzed in post processing without violating user privacy. CoughNote does not capture every cough, but 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. 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.},
}
EndNote citation:
%0 Thesis %A Huang, William %T Sensing Contacts, Coughs, and Hand Hygiene %I EECS Department, University of California, Berkeley %D 2020 %8 December 17 %@ UCB/EECS-2020-212 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-212.html %F Huang:EECS-2020-212