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RoomE and WatchBLoc: A BLE and Smartwatch IMU Algorithm and Dataset for Room-Level Localisation in Privacy-Preserving Environments

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Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2022)

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Abstract

The increasing at-home way of living, which became essential due to the COVID-19 pandemic, yields significant interest in analysing human behaviour at home. Estimating a person’s room-level position can provide essential information to improve situation awareness in smart human-environment interactions. Such information is constrained by two significant challenges: the cost of required infrastructure, and privacy concerns for the monitored household. In this paper, we advocate that ambient bluetooth signals, from IoT devices around the house, and inertial data from a smartwatch can be leveraged to provide room-level tracking information without additional infrastructure. We contribute a comprehensive dataset that combines real-world BLE RSSI data and smartwatch IMU data from two environments, which we use to achieve room-level indoor localisation. We propose an unsupervised, probabilistic framework that combines the two sensor modalities, to achieve robustness against different device placements and effectively track the user around rooms of the house, and examine how different configurations of IoT devices can affect the performance. Over time, through transition-events and stay-events, the model learns to infer the user’s room position, as well as a semantic map of the rooms of the environment. Performance has been evaluated on the collected dataset. Our proposed approach boosts the localisation accuracy from \(67.77\%\) on average in standard BLE RSSI localisation, to \(81.53\%\).

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Notes

  1. 1.

    The dataset is publicly available at: https://doi.org/10.5281/zenodo.7039554.

  2. 2.

    In practice this is not constraining in the presence of multiple IoT devices per room, as they can be aggregated into an e.g., ‘max’ or ‘mean’ BLE signal. Handling rooms with no BLE devices is more challenging and is an open topic for future work.

  3. 3.

    The choice of the window size is related to the \(0.2\;\text {Hz}\) recording frequency of the BLE data, as well as an implementation detail in our approach for the Motion Detection module that eases time synchronisation of the two modalities.

  4. 4.

    This ensures a synchronised 1-to-1 mapping between location and mobility estimates and explains the choice of \(5.12\;\text {s}\) window for the BLE RSSI data.

  5. 5.

    Calculated with the MATLAB’s findpeaks function.

  6. 6.

    Updating the state transitions might also be optional, as the transition matrix has been designed to only indicate allowed or not allowed transitions. However, learning this transition matrix can provide information about room connectivity.

  7. 7.

    We refer the reader to the full set of Baum-Welch equations in [3].

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Authors and Affiliations

  1. Department of Computer Science, University of Oxford, Oxford, UK

    Ada Alevizaki & Niki Trigoni

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  1. Ada Alevizaki
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  2. Niki Trigoni
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Correspondence to Ada Alevizaki .

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  1. University of Pittsburgh, Pittsburgh, PA, USA

    Shangguan Longfei

  2. Microsoft Research, Redmond, WA, USA

    Priyantha Bodhi

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Alevizaki, A., Trigoni, N. (2023). RoomE and WatchBLoc: A BLE and Smartwatch IMU Algorithm and Dataset for Room-Level Localisation in Privacy-Preserving Environments. In: Longfei, S., Bodhi, P. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-34776-4_15

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  • DOI: https://doi.org/10.1007/978-3-031-34776-4_15

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