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Multi-Sensor Data Fusion and CNN-LSTM Model for Human Activity Recognition System.

Haiyang Zhou¹, Yixin Zhao¹, Yanzhong Liu¹

¹Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.

Sensors (Basel, Switzerland)

|July 11, 2023

Summary

This study introduces a novel human activity recognition (HAR) system using combined camera and millimeter wave radar data. The fusion approach significantly boosts accuracy in low-light conditions, outperforming camera-only systems.

Keywords:

CNN-LSTM fusion algorithm human activity recognition multi-sensor data fusion

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

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Area of Science:

Computer Science
Artificial Intelligence
Sensor Fusion

Background:

Human activity recognition (HAR) is crucial for elder care, but camera-based systems struggle in low light.
Existing HAR systems face accuracy limitations, particularly in challenging environmental conditions.

Purpose of the Study:

To develop an improved HAR system that overcomes low-light limitations.
To enhance HAR accuracy and reduce misclassification rates through sensor fusion.

Main Methods:

A hybrid system combining camera and millimeter wave radar sensors was designed.
An improved Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model was used for feature extraction.
Three data fusion algorithms (data-level, feature-level, decision-level) were investigated.

Main Results:

The fused sensor data significantly improved HAR accuracy by 19.87%–26.68% compared to camera-only systems in low light.
The data-level fusion algorithm reduced the misclassification rate to 2%–6%.

Conclusions:

The proposed multi-sensor fusion system effectively enhances HAR accuracy in low-light environments.
This approach shows potential for reliable monitoring and reduced activity misclassification in smart living spaces.