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Enhancement of Human Complex Activity Recognition using Wearable Sensors Data with InceptionTime Network

Effective human activity recognition can be incredibly beneficial in big data applications like ambient healthcare-supported living. Deep learning (DL) techniques have considerably advanced research in human activity recognition (HAR). These deep learning algorithms outperform conventional machine learning methods in terms of automatic feature extraction. Many deep learning models have recently been demonstrated as state-of-the-art approaches to efficiently classify simple and complex human behaviors to address the HAR. This work proposes a new sensor-based HAR to classify complex activities with high performance using a DL model called InceptTime network. The proposed architecture is evaluated on a public benchmark complex activity dataset named PAMAP2. The experimental outcomes show that the proposed InceptTime model is significantly better than other baseline D L models on the same dataset with the highest accuracy of 88 %.