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DeepIOD: Towards A Context-Aware Indoor-Outdoor Detection Framework Using Smartphone Sensors.

Muhammad Bilal Akram Dastagir1, Omer Tariq1, Dongsoo Han1

  • 1Korea Advanced Institute of Science and Technology-KAIST, Daejeon 34141, Republic of Korea.

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DeepIOD accurately classifies environments as indoor or outdoor using IMU, GPS, and light data. This robust framework significantly improves location-based services with high generalizability.

Keywords:
context awarenessdeep learningindoor–outdoor detectionlocation-based servicesmajority voterssmartphone sensors

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Area of Science:

  • Computer Science
  • Sensor Fusion
  • Machine Learning

Background:

  • Accurate indoor-outdoor detection (IOD) is crucial for location-based services but faces challenges with GPS in indoor environments and IMU data generalizability.
  • Existing methods struggle with reliability and real-time performance in diverse, unseen environments.

Purpose of the Study:

  • To introduce the DeepIOD framework for accurate and robust indoor-outdoor classification.
  • To address the limitations of traditional IOD methods by integrating multiple sensor data sources.

Main Methods:

  • The DeepIOD framework preprocesses IMU, GPS, and light sensor data.
  • It utilizes multiple deep neural network models and an adaptive majority voting mechanism for prediction.
  • The system was evaluated on a smartphone across six unseen environments.

Main Results:

  • DeepIOD achieved significantly higher accuracy compared to IMU-only methods.
  • The system demonstrated remarkable accuracy rates of 98-99%.
  • Transition time was less than 10 ms, indicating real-time applicability.

Conclusions:

  • DeepIOD provides a robust, generalizable, and reliable solution for indoor-outdoor classification.
  • Integrating diverse data sources enhances the precision of location-based services and context-aware applications.