Comparative Prediction of Physical Fatigue Patterns in Bandung, Indonesia Workers using CNN and ANN

 (*)Muhammad Fikri Raihan Ardiansyah Mail (Telkom Univeristy, Bandung, Indonesia)
 Rifki Wijaya (Telkom Univeristy, Bandung, Indonesia)
 Gia Septiana Wulandari (Telkom Univeristy, Bandung, Indonesia)

(*) Corresponding Author

Submitted: January 5, 2024; Published: January 29, 2024


This research explores the impact of physical fatigue on task performance and evaluates the effectiveness of Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) in predicting fatigue levels. Physical fatigue, as a critical factor influencing performance and safety, serves as a signal for the body's need for rest. Utilizing a smartwatch with heart rate sensors, this study applies ANN for subjective fatigue assessments and CNN for time series analysis. With a structured approach encompassing data collection, preprocessing, and model training, a confusion matrix evaluates the model's performance. Results indicate an accuracy of 92.4% for the ANN model with an RMSE of 0.275, while the CNN model achieves an accuracy of 85.46% with an RMSE of 0.381. These findings affirm the effectiveness of both models in predicting fatigue, providing valuable insights for future research and emphasizing the importance of comprehensive data analysis for a nuanced understanding of individual performance (Number of data: 149,796 from 6 subjects).


Physical Fatigue; Artificial Neural Network (ANN); Convolutional Neural Network (CNN) ; Time Series; Heart Rate

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S. Rahimian Aghdam, S. S. Alizadeh, Y. Rasoulzadeh, and A. Safaiyan, “Fatigue Assessment Scales: A comprehensive literature review,” Arch. Hyg. Sci., vol. 8, no. 3, pp. 145–153, 2019, doi: 10.29252/archhygsci.8.3.145.

P. Yin, L. Yang, C. Wang, and S. Qu, “Effects of wearable power assist device on low back fatigue during repetitive lifting tasks,” Clin. Biomech., vol. 70, pp. 59–65, 2019, doi: 10.1016/j.clinbiomech.2019.07.023.

S. Park, S. Seong, Y. Ahn, and H. Kim, “Real-Time Fatigue Evaluation Using Ecological Momentary Assessment and Smartwatch Data: An Observational Field Study on Construction Workers,” J. Manag. Eng., vol. 39, no. 3, pp. 1–14, 2023, doi: 10.1061/jmenea.meeng-4953.

U. Techera, M. Hallowell, and R. Littlejohn, “Worker Fatigue in Electrical-Transmission and Distribution-Line Construction,” J. Constr. Eng. Manag., vol. 145, no. 1, pp. 1–9, 2019, doi: 10.1061/(asce)co.1943-7862.0001580.

F. K.-W. Wong, Y.-H. Chiang, F. A. Abidoye, and S. Liang, “Interrelation between Human Factor–Related Accidents and Work Patterns in Construction Industry,” J. Constr. Eng. Manag., vol. 145, no. 5, pp. 1–8, 2019, doi: 10.1061/(asce)co.1943-7862.0001642.

M. Z. Liu, X. Xu, J. Hu, and Q. N. Jiang, “Real time detection of driver fatigue based on CNN-LSTM,” IET Image Process., vol. 16, no. 2, pp. 576–595, 2022, doi: 10.1049/ipr2.12373.

M. Moshawrab, M. Adda, A. Bouzouane, H. Ibrahim, and A. Raad, “Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review,” Sensors, vol. 23, no. 2, pp. 1–25, 2023, doi: 10.3390/s23020828.

IMO, “MSC.1/Circ.1598 Guidelines on fatigue,” Imo-Msc, vol. 44, no. June 2001, 2019, [Online]. Available:

T. Theodoridis and J. Kraemer, “Evaluation of physiological metrics as a real-time measurement of physical fatigue in construction workers: State-of-the-Art Reviews,” vol. 147, no. 5, 2021, doi:

O. Bamisile et al., “Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals,” Sci. Rep., vol. 12, no. 1, pp. 1–26, 2022, doi: 10.1038/s41598-022-13652-w.

A. Rai, A. Shrivastava, and K. C. Jana, “A CNN-BiLSTM based deep learning model for mid-term solar radiation prediction,” Int. Trans. Electr. Energy Syst., vol. 31, no. 9, pp. 1–13, 2021, doi: 10.1002/2050-7038.12664.

S. Mehtab and J. Sen, “Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries,” 2020, doi: 10.36227/techrxiv.15088734.v1.

Z. Ahmad, M. N. Jamaludin, and K. Soeed, “Prediction of exhaustion threshold based on ECG features using the artificial neural network model,” 2018 IEEE EMBS Conf. Biomed. Eng. Sci. IECBES 2018 - Proc., pp. 523–528, 2019, doi: 10.1109/IECBES.2018.8626605.

N. Juliana, I. F. Abu, N. A. Roslan, N. I. Mohd Fahmi Teng, A. R. Hayati, and S. Azmani, Muscle Strength in Male Youth that Play Archery During Leisure Time Activity. 2020.

K. Wang et al., “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing, vol. 360, pp. 107–119, 2019, doi: 10.1016/j.neucom.2019.05.023.

G. Bilquise, S. Abdallah, and T. Kobbaey, Predicting Student Retention Among a Homogeneous Population Using Data Mining, vol. 77. 2021.

Y. Zhang, M. Safdar, J. Xie, J. Li, M. Sage, and Y. F. Zhao, A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management, vol. 34, no. 8. Springer US, 2023.

P. C. Rodrigues, O. O. Awe, J. S. Pimentel, and R. Mahmoudvand, “Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks,” Stats, vol. 3, no. 2, pp. 137–157, 2020, doi: 10.3390/stats3020012.

M. Ayitey Junior, P. Appiahene, and O. Appiah, “Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis,” J. Electr. Syst. Inf. Technol., vol. 9, no. 1, pp. 1–24, 2022, doi: 10.1186/s43067-022-00054-1.

M. Lepot, J. B. Aubin, and F. H. L. R. Clemens, “Interpolation in time series: An introductive overview of existing methods, their performance criteria and uncertainty assessment,” Water (Switzerland), vol. 9, no. 10, 2017, doi: 10.3390/w9100796.

Ahmad Harmain, P. Paiman, H. Kurniawan, K. Kusrini, and Dina Maulina, “Normalisasi Data Untuk Efisiensi K-Means Pada Pengelompokan Wilayah Berpotensi Kebakaran Hutan Dan Lahan Berdasarkan Sebaran Titik Panas,” Tek. Teknol. Inf. dan Multimed., vol. 2, no. 2, pp. 83–89, 2022, doi: 10.46764/teknimedia.v2i2.49.

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