Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readings

• URL: http://arxiv.org/abs/2206.06033v1
• Date: Mon, 13 Jun 2022 10:39:03 GMT
• Title: Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readings
• Authors: Suriyadeepan Ramamoorthy, Joyce Mahon, Michael O'Mahony, Jean Francois Itangayenda, Tendai Mukande, Tlamelo Makati
• Abstract summary: We present our solution to the challenge provided by the SFI Centre for Machine Learning (ML-Labs) in which the distance between two phones needs to be estimated. It is a modified version of the NIST Too Close For Too Long (TC4TL) Challenge, as the time aspect is excluded. We propose a feature-based approach based on Bluetooth RSSI and IMU sensory data, that outperforms the previous state of the art by a significant margin, reducing the error down to 0.071.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this report, we present our solution to the challenge provided by the SFI Centre for Machine Learning (ML-Labs) in which the distance between two phones needs to be estimated. It is a modified version of the NIST Too Close For Too Long (TC4TL) Challenge, as the time aspect is excluded. We propose a feature-based approach based on Bluetooth RSSI and IMU sensory data, that outperforms the previous state of the art by a significant margin, reducing the error down to 0.071. We perform an ablation study of our model that reveals interesting insights about the relationship between the distance and the Bluetooth RSSI readings.

Related papers

• RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge [66.33067693672696]
  This paper addresses the critical problem of interference rejection in radio-frequency (RF) signals using a novel, data-driven approach. First, we present an insightful signal model that serves as a foundation for developing and analyzing interference rejection algorithms. Second, we introduce the RF Challenge, a publicly available dataset featuring diverse RF signals along with code templates. Third, we propose novel AI-based rejection algorithms, specifically architectures like UNet and WaveNet, and evaluate their performance across eight different signal mixture types.
  arXiv  Detail & Related papers  (2024-09-13T13:53:41Z)
• BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation [4.831029473163422]
  Concerns about worker privacy and technology protection make it necessary to explore alternative approaches. We propose a non-visual, scalable solution using Bluetooth Low Energy (BLE) and ultrasound coordinates.
  arXiv  Detail & Related papers  (2024-04-24T17:26:17Z)
• Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things [82.15959827765325]
  We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) We address two major shortcomings of standard multimodal approaches, limited area coverage and reduced reliability. Our new framework fuses the concept of modality hallucination with triplet learning to train a model with different modalities to handle missing sensors at inference time.
  arXiv  Detail & Related papers  (2022-07-14T10:04:18Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge [0.0]
  In this paper, we investigate the development of machine learning approaches to determine the distance between two mobile phone devices. We use Bluetooth Low Energy, sensory data and meta data to improve on the existing state of the art.
  arXiv  Detail & Related papers  (2022-03-08T13:42:20Z)
• A Simplistic Machine Learning Approach to Contact Tracing [0.0]
  This report is based on the modified NIST challenge, Too Close For Too Long, provided by the SFI Centre for Machine Learning (ML-Labs) By handcrafting features from phone instrumental data we develop two machine learning models to estimate distance between two phones. Our method is able to outperform the leading NIST challenge by a significant margin.
  arXiv  Detail & Related papers  (2020-12-10T19:34:48Z)
• Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracing [12.070047847431884]
  Social-distancing via efficient contact tracing has emerged as the primary health strategy to dampen the spread of COVID-19. We present a novel system to estimate pair-wise individual proximity, via a joint model of Bluetooth Low Energy (BLE) signals with other on-device sensors.
  arXiv  Detail & Related papers  (2020-09-04T03:01:52Z)
• Delay Minimization for Federated Learning Over Wireless Communication Networks [172.42768672943365]
  The problem of delay computation for federated learning (FL) over wireless communication networks is investigated. A bisection search algorithm is proposed to obtain the optimal solution. Simulation results show that the proposed algorithm can reduce delay by up to 27.3% compared to conventional FL methods.
  arXiv  Detail & Related papers  (2020-07-05T19:00:07Z)
• Coronavirus Contact Tracing: Evaluating The Potential Of Using Bluetooth Received Signal Strength For Proximity Detection [14.749535590735965]
  We report on measurements of Bluetooth Low Energy (LE) received signal strength taken on mobile handsets in a variety of common, real-world settings. We find that the Bluetooth LE received signal strength can vary substantially depending on the relative orientation of handsets. This suggests that the development of accurate methods for proximity detection based on Bluetooth LE received signal strength is likely to be challenging.
  arXiv  Detail & Related papers  (2020-05-19T13:51:23Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)
• Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning [59.19469551774703]
  Drone-based vehicle detection aims at finding the vehicle locations and categories in an aerial image. We construct a large-scale drone-based RGB-Infrared vehicle detection dataset, termed DroneVehicle. Our DroneVehicle collects 28, 439 RGB-Infrared image pairs, covering urban roads, residential areas, parking lots, and other scenarios from day to night.
  arXiv  Detail & Related papers  (2020-03-05T05:29:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.