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A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone.

Wen Qi1, Hang Su2, Chenguang Yang3

  • 1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy.

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|September 1, 2019
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Summary
This summary is machine-generated.

This study introduces a fast and robust deep convolutional neural network (FR-DCNN) for human activity recognition (HAR). The FR-DCNN model achieves high accuracy and fast computation for analyzing human behavior using smartphone sensors.

Keywords:
convolutional neural networkdata compressionhuman activity recognition

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Signal Processing

Background:

  • Human Activity Recognition (HAR) is crucial for healthcare and sports, driving demand for intelligent sensors and wearable devices.
  • Wearable and mobile devices generate vast amounts of heterogeneous, high-dimensional human activity data.
  • Efficient algorithms are needed to analyze this big data from inertial measurement unit (IMU) sensors.

Purpose of the Study:

  • To propose a novel, fast, and robust deep convolutional neural network (FR-DCNN) structure for human activity recognition (HAR).
  • To enhance the analysis of raw IMU sensor data through signal processing and a signal selection module.
  • To enable fast computational methods for building DCNN classifiers via a data compression module.

Main Methods:

  • Developed a Fast and Robust Deep Convolutional Neural Network (FR-DCNN) model.
  • Integrated signal processing algorithms and a signal selection module to enhance raw IMU data.
  • Incorporated a data compression module for efficient DCNN classifier construction.

Main Results:

  • The FR-DCNN model demonstrated superior performance in fast computation and high accuracy for HAR.
  • Online activity prediction achieved 95.27% accuracy in just 0.0029 seconds.
  • DCNN classifier establishment on compressed data averaged 88 seconds with minimal precision loss (94.18%).

Conclusions:

  • The proposed FR-DCNN is an effective method for fast and accurate human activity recognition using smartphone IMU data.
  • The model offers a significant advancement in real-time HAR applications.
  • FR-DCNN provides a computationally efficient approach to HAR with high precision.