Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

Baronti, Paolo; Chessa, Stefano; Mavilia, Fabio; Palumbo, Filippo

doi:10.3390/s18124462

Cited by 62 publications

(37 citation statements)

References 50 publications

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“…Many technical solutions are found to be used for indoor localization, which can be classified into five categories: (1) wireless communication, (2) optical/visual, (3) acoustic, (4) electromagnetic, and (5) inertial measurement [9,17]. Despite some technical solutions being competitive in terms of accuracy, such as ultrasound and optical/visual techniques, they still present problems in their adoption due to the cost of additional equipment and difficulties in the deployment and maintenance of dedicated infrastructure [18]. Therefore, the most commonly used techniques for indoor localization are: WiFi, UWB, RFID, and inertial measurement.…”

Section: Indoor Localization Techniques: the State-of-the-artmentioning

confidence: 99%

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Zhang

Gao

et al. 2020

Sensors

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Wearable indoor localization can now find applications in a wide spectrum of fields, including the care of children and the elderly, sports motion analysis, rehabilitation medicine, robotics navigation, etc. Conventional inertial measurement unit (IMU)-based position estimation and radio signal indoor localization methods based on WiFi, Bluetooth, ultra-wide band (UWB), and radio frequency identification (RFID) all have their limitations regarding cost, accuracy, or usability, and a combination of the techniques has been considered a promising way to improve the accuracy. This investigation aims to provide a cost-effective wearable sensing solution with data fusion algorithms for indoor localization and real-time motion analysis. The main contributions of this investigation are: (1) the design of a wireless, battery-powered, and light-weight wearable sensing device integrating a low-cost UWB module-DWM1000 and micro-electromechanical system (MEMS) IMU-MPU9250 for synchronized measurement; (2) the implementation of a Mahony complementary filter for noise cancellation and attitude calculation, and quaternions for frame rotation to obtain the continuous attitude for displacement estimation; (3) the development of a data fusion model integrating the IMU and UWB data to enhance the measurement accuracy using Kalman-filter-based time-domain iterative compensations; and (4) evaluation of the developed sensor module by comparing it with UWB- and IMU-only solutions. The test results demonstrate that the average error of the integrated module reached 7.58 cm for an arbitrary walking path, which outperformed the IMU- and UWB-only localization solutions. The module could recognize lateral roll rotations during normal walking, which could be potentially used for abnormal gait recognition.

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Section: Indoor Localization Techniques: the State-of-the-artmentioning

confidence: 99%

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Zhang

Gao

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…An example of using image processing to allow indoor localisation is provided in [12]. Finally, the research works presented in [13]- [17] study the use of Bluetooth technology to develop IPS.…”

Section: Related Workmentioning

confidence: 99%

Achieving Multi-User Capabilities through an Indoor Positioning System based on BLE Beacons

Quinde

Gimenez-Manuel

Oguego

et al. 2020

2020 16th International Conference on Intelligent Environments (IE)

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“…Many proposed BLE RSSI-based ranging and fingerprinting systems often use RSSI from all three advertising channels together to obtain an aggregate signal [2][3][4]. However, the signals have been shown to exhibit different overall path-loss models and are affected by multipath fading and attenuation differently.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Naghdi

O’Keefe

2020

Sensors

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One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.

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Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

Cited by 62 publications

References 50 publications

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU

Achieving Multi-User Capabilities through an Indoor Positioning System based on BLE Beacons

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

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