BlueEye

Ghose, Avik; Bhaumik, Chirabrata; Chakravarty, Tapas

doi:10.1145/2494091.2499771

Cited by 37 publications

(3 citation statements)

References 16 publications

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“…The BlueEye system, developed in Ref. [26], uses Bluetooth received signal strength and relative orientation of mobile phones to gauge the extent of social interactions between people at large gatherings. Proximity detection can be used "to detect and distinguish interactions in the workplace" and how it "can shed light over productivity, team work and on employees' use of space" [27].…”

Section: Related Workmentioning

confidence: 99%

On the feasibility of COVID-19 proximity detection using Bluetooth low energy signals

Moayeri¹

2022

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The Corona VIrus Disease 2019 (COVID-19) pandemic has had a profound effect on the entire world. With the onset of the pandemic in 2020, also started various efforts around the world to automate the contact tracing process to increase its efficacy. Most of these efforts and the smartphone apps that were developed used the Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI) to detect proximity between people. This was facilitated by the development of the Google-Apple Exposure Notification (GAEN) system that made it possible for Android phones and iPhones to exchange standardized BLE messages seamlessly so that RSSI could be measured by both types of phones. In the summer of 2020, we carried out a five-week long BLE RSSI data collection campaign using wearable devices developed at NIST and running on a Raspberry Pi platform. The data collection was comprehensive, because it included a wide variety of operational scenarios including situations where the two devices were not in line-of-site of each other. To the best of our knowledge, there are no publicly reported repositories of BLE RSSI data that includes non-line-of-site (NLOS) scenarios. Such scenarios are important, because they can lead to false alarms in detecting situations where virus transmission between two people is possible. The paper presents a classical analytical framework for proximity detection in the context of electronic contact tracing for COVID-19. We make a distinction between instantaneous and after the fact proximity detection. In the former case, the purpose is to instantaneously warn people that they are violating social distancing rules. In the latter case, the purpose is to notify people after the fact that they had a close contact of certain duration with an individual who has tested positive for COVID-19 and was capable of transmitting the virus at the time of the contact. We evaluate the performance of various methods for instantaneous proximity detection. In after the fact proximity detection, we are interested in identifying periods of time where two people were too close to each other with no barriers between them. We propose a method that applies the Viterbi Algorithm to a time series of BLE RSSI data to solve this problem. The method exploits the fact that humans do not move faster than certain speed. In both cases of instantaneous and after the fact proximity detection, we show that methods based on BLE RSSI data leave a lot of room for improvement. Therefore, the problem of proximity detection for electronic contact tracing and blunting the spread of highly infectious diseases is far from solved. We also know that the next pandemic is not a matter of if but when.

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Section: Related Workmentioning

confidence: 99%

On the feasibility of COVID-19 proximity detection using Bluetooth low energy signals

Moayeri¹

2022

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“…Hivemind's influenceaware authorization is closely related to indoor localization techniques. Extensive prior works have studied room-level localization [12,38,49,74,89,113,121]. As we mentioned in §7, however, those techniques cannot be adopted to actuators with AoI that are easily affected by the environmental changes, such as speakers.…”

Section: Related Workmentioning

confidence: 99%

Hivemind

Kim

Lee

Chang

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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Public spaces are equipped with 'public actuators', e.g., HVAC, lighting fixtures, speakers, or streaming TV channels to ensure their visitors' comfort. However, many public actuators rarely allow the visitors to adjust their operation, limiting their utility and fairness across the visitors. Also, the social bar is often too high to speak up one's preference and attempt to change an actuator's operation. Social control and use of IoT devices is an underexplored new direction of research even with its huge potential and implication, but comes with high complexity and scale. This paper proposes a novel architecture, namely, Social Control-and-Use Architecture for IoT Devices, which provides a systematic view and an effective tool to handle the complication and intricacy in system design. It also proposes Hivemind, a first-of-a-kind system developed, upon the architecture, for sharing IoT-enabled actuators in a public space. It transforms an exclusively-controlled actuator in a public space into a true public actuator, supporting visitors to instantly participate in the democratic collective control. Also, a myriad of off-the-shelf actuators are easily incorporated without modification to their implementation. The field deployment of Hivemind shows its comprehensive service coverage as well as the users' approval on the democratic collective control of public actuators. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing theory, concepts and paradigms; Collaborative and social computing systems and tools; • Computer systems organization → Sensors and actuators.

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“…Bluetooth is capable of providing pairing services, data transfer, device identification, location detection, and others [14]. Android-based applications that run on smartphones using Bluetooth technology have been developed for data communication and object detection [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Development of The Personnel Monitoring System Using Mobile Application and Real-Time Database During the COVID19 Pandemic

Muladi

Aripriharta

Zaeni

et al. 2020

2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

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Coronavirus disease 19 (COVID19) is a disease caused by the new coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This disease has infected almost the entire world with a total of 47.5 million sufferers and a death toll of 1.2 million people so that WHO categorizes it as a global pandemic. The COVID19 case in Indonesia still shows an increasing trend even though various prevention efforts have been made. Proven efforts to reduce the spread of COVID19 include limiting physical interactions between humans or physical distance, maintaining the cleanliness of hands and limbs by washing with soap, and limiting outdoor activities by staying at home. Several government and private agencies have required employees to report their health conditions via web pages. Real-time and accurate mobile applications can help prevent the spread of COVID19. This research will develop a real-time monitoring and command system using mobile applications and cloud computing technology. The application will collect GPS-based location data, the number of people in the vicinity identified via Bluetooth, and the user's body condition in the form of temperature and oxygen levels in the blood. User data is stored and processed in a real time database in cloud computing which can be accessed through an application on the user's smartphone. The database also stores data on Covid19 sufferers and where they live. The application provides alerts when in a crowd and notifies the status of the region the user is in. Advice is given by the app when the recording of the body condition points to the early symptoms of COVID19.

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BlueEye

Cited by 37 publications

References 16 publications

On the feasibility of COVID-19 proximity detection using Bluetooth low energy signals

On the feasibility of COVID-19 proximity detection using Bluetooth low energy signals

Hivemind

Development of The Personnel Monitoring System Using Mobile Application and Real-Time Database During the COVID19 Pandemic

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