Improving the Performance of Closet-Set Classification in Human Activity Recognition by Applying a Residual Neural Network Architecture

Daniel Rodriguez Gonzalez; Yanzhen Qu

doi:10.24018/ejece.2022.6.2.433

Daniel Rodriguez Gonzalez

Yanzhen Qu

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The Time Series Classification Residual Network (TSCResNet) deep learning model is introduced in this study to improve the classification performance in the human activity recognition (HAR) problem. Specifically in the context of closed-set classification where all labels or classes are present during the model training phase. This contrasts with open-set classification where new, unseen activities are introduced to the HAR system after the training phase. The proposed TSCResNet model is evaluated with the benchmark PAMAP2 Physical Activity Monitoring Dataset. By using the same quantitative methods and data preprocessing protocols as previous research in the field of closed-set HAR, results show that the TSCResNet model architecture is able to achieve improved classification results across accuracy, the weighted F1-score, and mean F1-score.

Keywords: Closed-set classification, deep learning, human activity recognition, residual neural networks

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How to Cite

[1]

Rodriguez Gonzalez, D. and Qu, Y. 2022. Improving the Performance of Closet-Set Classification in Human Activity Recognition by Applying a Residual Neural Network Architecture. European Journal of Electrical Engineering and Computer Science. 6, 2 (Apr. 2022), 59–65. DOI:https://doi.org/10.24018/ejece.2022.6.2.433.