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Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and
Summary
This study introduces a new recurrent neural network and data augmentation for Human Activity Recognition (HAR). The approach enhances model robustness, even with missing sensor data, for disease prevention applications.
Area of Science:
- Biomedical Engineering
- Machine Learning
- Wearable Technology
Background:
- Human Activity Recognition (HAR) is crucial for disease treatment and prevention.
- Ubiquitous sensors (accelerometers, gyroscopes) enable HAR model training.
- Advanced technology facilitates sophisticated HAR applications.
Purpose of the Study:
- Propose a recurrent neural network architecture for robust HAR.
- Introduce a data augmentation technique to handle missing sensor data.
- Investigate sensor placement effects on activity recognition accuracy.
Main Methods:
- Developed and compared Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models.
- Implemented a novel data augmentation strategy for sensor-dropout scenarios.
- Evaluated models on the GOTOv dataset with 35 participants and 16 activities.
Main Results:
- The proposed recurrent neural network architecture achieved robust HAR.
- Data augmentation improved model performance with missing sensor inputs.
- Identified correlations between sensor location and activity type recognition.
Conclusions:
- The developed HAR models demonstrate high accuracy and robustness.
- The data augmentation technique effectively addresses sensor data variability.
- Findings contribute to improved HAR systems for health monitoring.
