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IoT powered RNN for improved human activity recognition with enhanced localization and classification
Naif Al Mudawi1, Usman Azmat2, Abdulwahab Alazeb1
1School Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, 55461, Saudi Arabia.
Scientific Reports
|March 26, 2025
Summary
This study introduces a robust system for human activity recognition (HAR) and localization using noisy sensor data. The novel approach achieves high accuracy in identifying activities and locations, outperforming existing methods.
Area of Science:
- Computer Science
- Signal Processing
- Machine Learning
Background:
- Human Activity Recognition (HAR) and localization are critical research areas driven by smart devices.
- Sensor data from smart devices often contains significant noise, necessitating robust system design.
Purpose of the Study:
- To develop a noise-impervious and efficient system for human activity recognition and localization.
- To leverage multiple algorithms for enhanced performance in HAR and localization tasks.
Main Methods:
- Signal denoising using a Chebyshev type-I filter, followed by windowing.
- Parallel feature extraction for activity and location, utilizing the Boruta algorithm for feature selection.
- Data optimization with Particle Swarm Optimization (PSO) and training parallel Recurrent Neural Networks (RNNs) for HAR and localization.
Main Results:
- The system demonstrated exceptional performance on the Extrasensory and Sussex Huawei Locomotion (SHL) datasets.
- Achieved accuracies of 89.25% and 90.50% for HAR on Extrasensory, and 95.75% for HAR on SHL.
- Achieved accuracies of 95.75% and 91.50% for localization on Extrasensory and SHL, respectively, outperforming state-of-the-art methods.
Conclusions:
- The proposed system effectively handles noisy sensor data for accurate human activity recognition and localization.
- The integrated approach of filtering, feature selection, optimization, and parallel RNNs provides a robust solution.
- The system's superior performance on benchmark datasets validates its efficacy and potential for real-world applications.
