Enhanced still presence sensing with supervised learning over segmented ultrasonic reflections

Hammoud, Abbass; Kyritsis, Athanasios I.; Deriaz, Michel; Konstantas, Dimitri

doi:10.1109/ipin.2017.8115920

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The physical distance between two people can be monitored using the Global Positioning System (GPS) when outdoors or indoor positioning systems when indoors. Many technologies can be used for indoor positioning, such as Bluetooth [8], Ultrasound [9], and Ultra-wideband (UWB). Each technology has its advantages and limitations, but in order to achieve a proper distance detection of 1.5-2 meters, the acceptable positioning error should be of a few centimeters.…”

Section: Related Workmentioning

confidence: 99%

A Queue Management Approach for Social Distancing and Contact Tracing

Kyritsis

Deriaz

2020

2020 Third International Conference on Artificial Intelligence for Industries (AI4I)

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Social distancing is necessary to prevent the rapid spread of a highly contagious disease, such as COVID-19, at least until a vaccine is found and mass-produced. By reducing the probability of an uninfected person coming close or in physical contact with an infected one, the disease transmission in the community can be suppressed. Although social distancing is simple to comprehend, it is not always easy to implement, mainly because not all public spaces are designed with this requirement in mind. In this paper, we present a queue management tool that can be used to allow people that wait for a service practice social distancing. In our approach, people are asked to join a virtual queue, in order to avoid crowds in physical waiting rooms or long waiting queues. Machine learning is used to predict the estimated waiting time of queuers, so they are called just in time to get served. We use past data and machine learning to predict how busy a location will be so that customers can pick the best time to visit the service. Finally, we present the method we use to monitor people taking a service at any time and implement contact tracing in a privacy-preserving manner.

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Section: Related Workmentioning

confidence: 99%

A Queue Management Approach for Social Distancing and Contact Tracing

Kyritsis

Deriaz

2020

2020 Third International Conference on Artificial Intelligence for Industries (AI4I)

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“…SVM [17,32]: It first uses an SVM to classify RSSI fingerprints labeled by the locations, and predicts the location when given a target RSSI vector.…”

Section: Localization Factor Analysismentioning

confidence: 99%

An adaptive model recognition and construction method for RSSI fingerprint-based localization

Yang

2019

Meas. Sci. Technol.

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Various methods have been developed for indoor localization. In particular, localization methods based on received signal strength indication (RSSI) fingerprinting can achieve accuracies of a few meters. A drawback that RSSI fingerprinting-based methods still suffer from is, in practice, the RSSI observed by certain devices commonly shows a more complex distribution rather than a single Gaussian distribution. In this paper, we propose an adaptive model recognition and construction (AMRC) method to adjust to the RSSI distribution characteristics for fingerprint-based methods, which improves the positioning accuracy and robustness of the localization system. The different components and factors that influence the performances of the proposed AMRC method are discussed, with experiments conducted in two indoor testbeds. The test results have demonstrated that the proposed AMRC method outperforms traditional localization methods due to more accurate modelling for the non-Gaussian RSSI distribution.

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