Mobile Indoor Localization with Bluetooth Beacons in a Pediatric Emergency Department Using Clustering, Rule-Based Classification and High-Level Heuristics

  • Patrice C. RoyEmail author
  • William Van Woensel
  • Andrew Wilcox
  • Syed Sibte Raza Abidi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)


To mitigate anxiety, pain and dehydration in Pediatric Emergency Departments (PED), it is paramount to tailor educational, motivational and self-help content towards the current location inside the PED, since this reflects the current stage in their PED visit. However, accurately identifying the patient’s indoor location in a real-world complex environment, such as a hospital, is still a challenging problem, with interference and attenuation from patients, staff, walls and various electromagnetic sources (e.g., imaging devices). We present an indoor localization methodology that achieve a best-effort localization accuracy given the available sensors, (low-quality) motion data and computational platforms. First, we utilize machine learning methods to find a suitable accuracy/granularity balance and then proceed by training a localization model. Then, we apply a set of heuristics based on motion data to eliminate false location estimates. We validated of our approach in a real-life busy and noisy PED with a 92% accuracy.


Indoor localization Machine learning Mobile health Bluetooth low energy beacons 



This work was funded by an NSERC Discovery Grant.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Patrice C. Roy
    • 1
    Email author
  • William Van Woensel
    • 1
  • Andrew Wilcox
    • 2
  • Syed Sibte Raza Abidi
    • 1
  1. 1.NICHE Research GroupDalhousie UniversityHalifaxCanada
  2. 2.EverAge ConsultingBedfordCanada

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