ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL

Nevzat Olgun; Mücahit Çalışan; Caner Balım

doi:10.51477/mejs.1667055

ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL

Abstract

In this study, a model based on Hybrid CNN-NCP and Self-Attention is proposed to recognize human activities from sensor data. The proposed model aims to improve the accuracy of human activity recognition (HAR) systems by learning spatial and temporal dependencies more effectively. The CNN layer, one of the main components of the model, extracts low-level spatial patterns from sensor data. These features are then transferred to the NCP layer, where long-term dependencies in time series data are learnt using dynamic feedback mechanisms. In the final stage, the Self-Attention mechanism improves the decision-making process of the model by focusing on critical information in different time periods. In this study, accelerometer and gyroscope sensor data are used to classify 12 different movement activities that are frequently performed in daily life. The performance of the model was evaluated by Group K-Fold cross-validation method and an average accuracy rate of 90.38% was obtained. The proposed hybrid model provides an innovative solution in HAR systems by providing higher performance compared to traditional deep learning approaches.

Keywords

Ethical Statement

The authors declare that this study does not require ethics committee approval.

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Details

Primary Language

English

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Authors

Nevzat Olgun ^*
0000-0003-2461-4923
Türkiye

Mücahit Çalışan
0000-0003-2651-5937
Türkiye

Caner Balım
0000-0002-1010-129X
Türkiye

Publication Date

December 30, 2025

Submission Date

March 27, 2025

Acceptance Date

September 19, 2025

Published in Issue

Year 2025 Volume: 11 Number: 2

DOI

https://doi.org/10.51477/mejs.1667055

IZ

https://izlik.org/JA23GW68NT

Cite

RIS / Bibtex

APA

Olgun, N., Çalışan, M., & Balım, C. (2025). ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL. Middle East Journal of Science, 11(2), 290-305. https://doi.org/10.51477/mejs.1667055

AMA

1.Olgun N, Çalışan M, Balım C. ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL. MEJS. 2025;11(2):290-305. doi:10.51477/mejs.1667055

Chicago

Olgun, Nevzat, Mücahit Çalışan, and Caner Balım. 2025. “ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL”. Middle East Journal of Science 11 (2): 290-305. https://doi.org/10.51477/mejs.1667055.

EndNote

Olgun N, Çalışan M, Balım C (December 1, 2025) ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL. Middle East Journal of Science 11 2 290–305.

IEEE

[1]N. Olgun, M. Çalışan, and C. Balım, “ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL”, MEJS, vol. 11, no. 2, pp. 290–305, Dec. 2025, doi: 10.51477/mejs.1667055.

ISNAD

Olgun, Nevzat - Çalışan, Mücahit - Balım, Caner. “ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL”. Middle East Journal of Science 11/2 (December 1, 2025): 290-305. https://doi.org/10.51477/mejs.1667055.

JAMA

1.Olgun N, Çalışan M, Balım C. ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL. MEJS. 2025;11:290–305.

MLA

Olgun, Nevzat, et al. “ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL”. Middle East Journal of Science, vol. 11, no. 2, Dec. 2025, pp. 290-05, doi:10.51477/mejs.1667055.

Vancouver

1.Nevzat Olgun, Mücahit Çalışan, Caner Balım. ACTIVITY RECOGNITION WITH A HYBRID CNN–NCP AND SELF-ATTENTION-BASED MODEL. MEJS. 2025 Dec. 1;11(2):290-305. doi:10.51477/mejs.1667055