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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Related Experiment Video

Updated: Jun 14, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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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.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

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.