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A Method for Autonomous Multi-Motion Modes Recognition and Navigation Optimization for Indoor Pedestrian.

Zhengchun Wang1,2, Zhi Xiong1,2, Li Xing3

  • 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

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Summary
This summary is machine-generated.

This study introduces a new indoor navigation system using wearable sensors and an artificial neural network. It accurately recognizes multiple pedestrian motion modes, improving navigation accuracy for complex tasks like rescue operations.

Keywords:
heading correctionmulti-motion modes recognitionmulti-node inertial sensor networkpedestrian navigationzero-velocity detection

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Area of Science:

  • Robotics and Automation
  • Sensor Networks
  • Artificial Intelligence

Background:

  • Traditional indoor navigation methods struggle with diverse motion modes like running and stair climbing.
  • Specialized tasks (e.g., rescue, medical) require robust navigation beyond simple walking.
  • Existing zero-velocity update algorithms are less effective during high-speed or non-walking movements.

Purpose of the Study:

  • To develop an accurate multi-motion mode recognition system for indoor pedestrian navigation.
  • To enhance the dynamic performance of indoor navigation algorithms for complex scenarios.
  • To improve the reliability of indoor positioning systems in dynamic environments.

Main Methods:

  • Utilized a wearable multi-node inertial sensor network for data acquisition.
  • Employed gait segmentation and a long short-term memory (LSTM) artificial neural network for motion mode recognition.
  • Developed novel zero-velocity update detection algorithms and integrated navigation methods considering heading changes for fast-speed motions.

Main Results:

  • Achieved an overall multi-motion mode recognition rate of 96.77%.
  • Demonstrated a low navigation error of 1.26% of the total traveled distance.
  • Validated the effectiveness of the proposed method in diverse pedestrian motion scenarios.

Conclusions:

  • The proposed method significantly improves indoor pedestrian navigation accuracy across multiple motion modes.
  • The system shows strong potential for applications in challenging environments like disaster relief and medical search and rescue.
  • Integration of LSTM networks and advanced zero-velocity update algorithms offers a robust solution for wearable indoor navigation.