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Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning.

Zhongzheng Fu1, Xinrun He1, Enkai Wang1

  • 1Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|February 2, 2021
PubMed
Summary

This study introduces a new wearable sensor and transfer learning method for accurate human activity recognition (HAR). The approach achieves 93.2% accuracy, improving personalized activity detection using multi-modal data.

Keywords:
air pressure sensorhuman activity recognition (HAR)inertial measurement unit (IMU)transfer learningwearable device

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Area of Science:

  • Wearable sensor technology
  • Human activity recognition (HAR)
  • Machine learning

Background:

  • Personalized human activity recognition (HAR) using wearable devices faces challenges in balancing recognition accuracy and model generalization.
  • Advancements in sensor technology have increased interest in HAR systems.

Purpose of the Study:

  • To design a compact wireless wearable sensor node for multi-modal data collection.
  • To develop a novel transfer learning algorithm for improved personalized HAR.

Main Methods:

  • A sensor node combining an air pressure sensor and inertial measurement unit (IMU) was developed.
  • A joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA) transfer learning algorithm was proposed.
  • Seven daily activities were collected from 7 subjects using the sensor node.

Main Results:

  • Multi-modal data from the sensor node enhanced HAR system accuracy.
  • The proposed IPL-JPDA algorithm demonstrated superior performance compared to other HAR models.
  • An average recognition accuracy of 93.2% across different subjects was achieved.

Conclusions:

  • The developed compact wireless wearable sensor node effectively captures multi-modal data for HAR.
  • The IPL-JPDA transfer learning algorithm significantly improves personalized human activity recognition accuracy.
  • This research offers a promising solution for accurate and generalized personalized HAR.