Distance Estimation for BLE-based Contact Tracing – A Measurement Study

Etzlinger, Bernhard; Nusbaummuller, Barbara; Peterseil, Philipp; Hummel, Karin Anna

doi:10.1109/wd52248.2021.9508280

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…As discussed in Section II, for the environmental factors, the body shielding and multi-path effect significantly influence the Bluetooth signal. The result of [22] shows that multi-path effect has a positive effect on reducing the signal attenuation caused by shielding and thus increases distance detection accuracy. Therefore, multi-path effect is not considered as a negative environmental factor and hence, is not considered in the algorithm.…”

Section: A Distance Estimation Algorithm Integratedmentioning

confidence: 99%

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

Zhang

Wang

et al. 2022

IEEE J. Select. Areas Commun.

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Since the coronavirus disease 19 (COVID-19) outbreak, the epidemiological analysis has raised a strong requirement for more effective and accurate contact tracing solution. However, the existing contact tracing solutions either lacked the evaluation of tracing proximity or the features used for the tracing proximity evaluation were susceptible to certain negative environmental factors (e.g., body shielding). In this article, we propose a novel distance estimation algorithm based on machine learning for contact tracing: DISTERNING, where we leverage machine learning algorithms including Learning Vector Quantization, Regression, and Deep Feed-forward (DFF) Neural Network, data processing methods, and digital filters to process the Bluetooth signal information collected by the mobile phone for contact distance estimation. A contact tracing scheme based on edge computing is also proposed for algorithm deployment due to the requirements of the computational power. Compared with the existing contact tracing solutions, our algorithm considers the factors that have significant negative influence on the Bluetooth signal for distance estimation in reality. The evaluation results show that when the collected Bluetooth signal is influenced by real-world negative environmental factors, employing our proposed algorithm DISTERNING can keep the accuracy of the estimated distance reliable. The output distance can be combined with some medical models to conduct infection risk assessments.

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Section: A Distance Estimation Algorithm Integratedmentioning

confidence: 99%

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

Zhang

Wang

et al. 2022

IEEE J. Select. Areas Commun.

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“…UWB ToF ranging can obtain reliable dm-level accuracy. UWB systems are also generally resistant to multipath interference [ 32 ], but only a few mobile phones, such as Samsung Galaxy Note 20 or Apple iPhone 11, have UWB modules, which can cause additional energy consumption [ 33 ]. Furthermore, UWB only supports the interaction between the mobile phones and the tags rather than the interaction between phones and phones.…”

Section: Related Workmentioning

confidence: 99%

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal

Jia

Shu

Wang

et al. 2022

Sensors

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With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz–23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.

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“…Whenever the BLE radio receives a new advertisement, it records the Received Signal Strength (RSS). We use the RSS as a rough approximation of the distance between a sender and receiver [7]. Successive measures also produce a simple estimation of a device's motion.…”

Section: Feature Extractionmentioning

confidence: 99%

BlueSeer

Poirot

Harms

Martens

2022

Proceedings of the 59th ACM/IEEE Design Automation Conference

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IoT devices rely on environment detection to trigger specific actions, e.g., for headphones to adapt noise cancellation to the surroundings. While phones feature many sensors, from GNSS to cameras, small wearables must rely on the few energy-efficient components they already incorporate. In this paper, we demonstrate that a Bluetooth radio is the only component required to accurately classify environments and present BlueSeer, an environment-detection system that solely relies on received BLE packets and an embedded neural network. BlueSeer achieves an accuracy of up to 84% differentiating between 7 environments on resource-constrained devices, and requires only 12 ms for inference on a 64 MHz microcontroller-unit.

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Distance Estimation for BLE-based Contact Tracing – A Measurement Study

Cited by 9 publications

References 12 publications

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal

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