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Interview with Zahra Ghorrati: developing frameworks for human activity recognition using wearable sensors - Robohub

  Interview with Zahra Ghorrati: developing frameworks for human activity recognition using wearable sensors - Robohub
Source: robohub
Published: 10/8/2025

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In this interview, Zahra Ghorrati, a PhD candidate at Purdue University, discusses her research on developing scalable and adaptive deep learning frameworks for human activity recognition (HAR) using wearable sensors. Her work addresses the challenges posed by noisy, inconsistent, and uncertain data from wearable devices, aiming to create models that are computationally efficient, interpretable, and robust enough for real-world applications outside controlled lab environments. Unlike video-based recognition systems, wearable sensors offer privacy advantages and continuous monitoring capabilities, making them highly suitable for healthcare and long-term activity tracking. Ghorrati’s research has focused on a hierarchical fuzzy deep neural network that adapts to diverse HAR datasets by detecting simpler activities at lower levels and more complex ones at higher levels. By integrating fuzzy logic into deep learning, her model effectively handles uncertainty in sensor data, improving both robustness and interpretability. This approach also maintains low computational costs, enabling real-time recognition on wearable devices. Evaluations on multiple benchmark datasets show that her framework achieves competitive accuracy

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robotwearable-sensorshuman-activity-recognitiondeep-learningIoThealthcare-technologysensor-data-analysis