An Efficient Human Activity Recognition (HAR) Model Based on Convolutional Neural Networks for Computing Devices Aiming to Reduce Latency and Tackle the Inactivity of Gadgets
Received: 30 May 2025 | Revised: 18 June 2025 and 1 July 2025 | Accepted: 3 July 2025 | Online: 6 October 2025
Corresponding author: Hamayun Khan
Abstract
Human Activity Recognition (HAR) is examined using a High Resolution Convolutional Neural Network (HR-CNN) and is compared with Machine Learning (ML) baselines, such as Decision Tree (DT) and Support Vector Machine (SVM), and with Deep Learning (DL) models, including Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a standard Convolutional Neural Network (CNN). Using a six-class dataset (10,299 samples), HR-CNN attains 99.2% accuracy and 0.88 macro-F1, outperforming all evaluated baselines. A supplementary analysis models latency as a regression target, and reports Mean Squared Error (MSE) and Coefficient of Determination (R²) across configurations. MSE reaches 0.008 compared with 0.031 in alternatives, with competitive R² values noted for both DT and CNN in distinct runs. Overall, HR-CNN delivers a state-of-the-art classification performance for HAR, while the regression study indicates favorable system-level behavior.
Keywords:
human activity recognition, machine learning, data analysis, model evaluation, convolutional neural network (CNN), latency reduction, mean square errorDownloads
References
H. Ma, W. Li, X. Zhang, S. Gao, and S. Lu, "AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition," in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Macao, China, Aug. 2019, pp. 3109–3115. DOI: https://doi.org/10.24963/ijcai.2019/431
X. Wang, L. Gao, J. Song, and H. Shen, "Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition," IEEE Signal Processing Letters, vol. 24, no. 4, pp. 510–514, Apr. 2017. DOI: https://doi.org/10.1109/LSP.2016.2611485
Y.-C. Fan, Y.-H. Tseng, and C.-Y. Wen, "A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors," Sensors, vol. 22, no. 21, Jan. 2022, Art. no. 8507,https://doi.org/10.3390/s22218507. DOI: https://doi.org/10.3390/s22218507
P. Zhang, Z. Zhang, and H.-C. Chao, "A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory," Sensors, vol. 20, no. 14, Jan. 2020, Art. no. 4016. DOI: https://doi.org/10.3390/s20144016
N. Y. Hammerla, S. Halloran, and T. Ploetz, "Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables." arXiv, Apr. 29, 2016.
A. Kelotra and P. Pandey, "Stock Market Prediction Using Optimized Deep-ConvLSTM Model," Big Data, vol. 8, no. 1, pp. 5–24, Feb. 2020. DOI: https://doi.org/10.1089/big.2018.0143
M. M. Hassan, Md. Z. Uddin, A. Mohamed, and A. Almogren, "A robust human activity recognition system using smartphone sensors and deep learning," Future Generation Computer Systems, vol. 81, pp. 307–313, Apr. 2018. DOI: https://doi.org/10.1016/j.future.2017.11.029
E. Carter, P. Adam, D. Tsakis, S. Shaw, R. Watson, and P. Ryan, "Enhancing pedestrian mobility in Smart Cities using Big Data," Journal of Management Analytics, vol. 7, no. 2, pp. 173–188, Apr. 2020. DOI: https://doi.org/10.1080/23270012.2020.1741039
W. N. Ismail, M. M. Hassan, and H. A. Alsalamah, "Context-Enriched Regular Human Behavioral Pattern Detection From Body Sensors Data," IEEE Access, vol. 7, pp. 33834–33850, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2904122
S. Zhang et al., "Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances," Sensors, vol. 22, no. 4, Jan. 2022, Art. no. 1476. DOI: https://doi.org/10.3390/s22041476
V. Bianchi, M. Bassoli, G. Lombardo, P. Fornacciari, M. Mordonini, and I. De Munari, "IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment," IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8553–8562, July 2019. DOI: https://doi.org/10.1109/JIOT.2019.2920283
G. Nain, K. K. Pattanaik, and G. K. Sharma, "Towards edge computing in intelligent manufacturing: Past, present and future," Journal of Manufacturing Systems, vol. 62, pp. 588–611, Jan. 2022. DOI: https://doi.org/10.1016/j.jmsy.2022.01.010
J. Kim, J. Moon, E. Hwang, and P. Kang, "Recurrent inception convolution neural network for multi short-term load forecasting," Energy and Buildings, vol. 194, pp. 328–341, July 2019. DOI: https://doi.org/10.1016/j.enbuild.2019.04.034
F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, July 2017, pp. 1800–1807. DOI: https://doi.org/10.1109/CVPR.2017.195
S. A. Alshammari and N. S. Albalawi, "Enhancing Healthcare Monitoring: A Deep Learning Approach to Human Activity Recognition using Wearable Sensors," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18843–18848, Dec. 2024. DOI: https://doi.org/10.48084/etasr.9255
J. R. Foerster, X. Costa-Perez, and R. V. Prasad, "Communications for IoT: Connectivity and Networking," IEEE Internet of Things Magazine, vol. 3, no. 1, pp. 6–7, Mar. 2020. DOI: https://doi.org/10.1109/MIOT.2020.9063399
D. Anguita, J. Reyes-Ortiz, A. Ghio, L. Oneto, and X. Parra, "Human Activity Recognition Using Smartphones." UCI Machine Learning Repository, 2013.
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Copyright (c) 2025 Hamayun Khan, Nasir Ayub, Muhammad Uzair, Irfanud Din, Arshad Ali, Farrukh Yuldashev, Samariddin Makhmudov
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