Human Activity Recognition via Time Series Imaging of Wearable Sensors and Fusion of Multiple Deep CNN Models

Human Activity Recognition (HAR) is essential in healthcare, smart homes, and security applications. Recent advancements in sensor technology and machine learning have enhanced HAR's ability to address real-world challenges, including continuous monitoring of elderly individuals. This study presents a HAR system developed using the publicly available GOTOV dataset, which contains accelerometer data collected from the ankle, chest, and wrist. The time-series signals were converted into images using the Gramian Angular Field (GAF) technique, allowing Convolutional Neural Network (CNN) models to be trained for each sensor modality. To improve classification performance, a fusion strategy was employed to integrate the predictions from all sensor-specific models. The proposed system achieved an F1-score exceeding 93% across 16 activity classes, demonstrating its effectiveness in multi-class human activity recognition.

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