Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration
Open accessAbstract
The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user’s position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values.
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AI-enhanced pipelines that fuse BLE RSSI data with vision-based models, such as transformer architectures, classify children's activity patterns and identify spatio-temporal risk behaviors.
“More recent work has introduced AI-enhanced pipelines that fuse BLE RSSI data with vision-based models, such as transformer architectures, to classify children's activity patterns and identify spatio-temporal risk behaviors, moving from simple location tracking toward more cognitive safety management [16].”
From Child Playground Entry/Exit Tracking and Log Visualization System Using BLE Beacons and Smart Devices (2026) · cites this paper as [16]
The Introduction and System Design (pages not supplied) describe combining BLE RSSI, wearable-camera imagery, and artificial neural networks to correct signal obstruction and improve indoor positioning. They do not describe transformer architectures, classify children’s activity patterns, or identify spatio-temporal risk behaviors; the Experimental Setup explicitly distinguishes detecting people in images from tracking them in video. (LLM, conf=0.90)
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- Authors
- Naghdi, S., & O’Keefe, K.
- Affiliations
- University of Calgary (CA)
- Corresponding author
- O’Keefe, Kyle
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-06-07
- Volume
- 22
- Issue
- 12
- Pages
- 4320
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9227766
- OpenAlex
- W4282585228
- Cited by
- 23
- References
- 42
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 8 · 2023: 3 · 2022: 1
- FWCI
- 1.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Libraries and Cultural Resources (University of Calgary) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Libraries and Cultural Resources (University of Calgary) (submittedVersion, cc-by)
- Funders
- Natural Sciences and Engineering Research Council of Canada (514520–17)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Bluetooth and Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Keywords
- Bluetooth Low Energy, indoor positioning, trilateration, received signal strength indicator, wearable cameras, human body shadowing, path loss model, artificial neural networks
- MeSH terms
- Algorithms, Data Collection, Humans, Neural Networks, Computer, Wireless Technology
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