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Intelligent Localization and Deep Human Activity Recognition through IoT Devices.

Abdulwahab Alazeb1, Usman Azmat2, Naif Al Mudawi1

  • 1Department of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a robust model for human activity recognition and localization using IoT data from smartphones and smartwatches. The system achieves high accuracy in identifying user activities and their locations, enhancing applications in healthcare and personal safety.

Keywords:
IoTactivity recognitiondeep learningdeep neural decision forestgenetic algorithmlocalizationrecursive feature eliminationsmartphonesmartwatch

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Area of Science:

  • Ubiquitous Computing
  • Internet of Things (IoT)
  • Human-Computer Interaction

Background:

  • Ubiquitous computing is a growing research area with applications in healthcare, behavior analysis, and personal safety.
  • Human activity recognition and localization are key applications within ubiquitous computing, often utilizing sensor data from wearable devices.
  • Existing methods require robust models capable of processing complex sensor data for accurate recognition and localization.

Purpose of the Study:

  • To propose a robust model for simultaneous human activity recognition and localization using IoT data.
  • To leverage smartphone and smartwatch sensor data for enhanced user monitoring and context awareness.
  • To improve the accuracy and reliability of activity recognition and localization systems.

Main Methods:

  • A signal denoising technique using a second-order Butterworth filter and a Hamming window for data chunking.
  • Generation of multiple stacked windows for reliable feature extraction in parallel human activity recognition and human localization modules.
  • Application of recursive feature elimination and a genetic algorithm for feature selection and data augmentation, followed by training a deep neural decision forest.

Main Results:

  • The proposed model achieved 88.25% accuracy for activity recognition and 90.63% for localization on the ExtraSensory dataset.
  • On the Sussex-Huawei Locomotion dataset, the system demonstrated 96.00% accuracy for activity recognition and 90.50% for localization.
  • The system outperformed existing state-of-the-art methods in both activity recognition and localization tasks.

Conclusions:

  • The developed model provides a robust and accurate solution for recognizing human activities and classifying locations using wearable sensor data.
  • The parallel processing of activity recognition and localization, combined with advanced feature engineering and deep learning, significantly enhances system performance.
  • This research contributes to the field of ubiquitous computing by offering a high-performing system applicable to various real-world scenarios.