Comparative Analysis of ARIMA and LSTM Models for Predicting Physical Fatigue in Bandung Workers

Kiki Dwi Prasetyo, Rifki Wijaya, Gia Septiana Wulandari

Abstract


In today's era of rapid economic growth, there is an increasing demand for workers to increase productivity by working longer and harder. However, these demands often lead to irregular and excessive working hours, which can potentially lead to negative consequences, such as physical fatigue-a state in which the body feels tired after physical activity. Factors that influence this fatigue include age, gender, health conditions, workload and work environment. Physical fatigue poses a significant challenge in ensuring workplace safety, especially in the transportation and industrial sectors, as it can reduce overall performance, productivity and quality of work. In addition, physical fatigue also increases the likelihood of decision-making errors and workplace accidents. Predicting physical fatigue is crucial to addressing these challenges. Heart rate serves as a parameter to measure fatigue, given its proven efficacy as a marker to predict physical fatigue, which is derived from the electrocardiogram and regulated by the autonomic nervous system. This research utilizes two machine learning algorithms - ARIMA and LSTM - with heart rate (bpm) and number of steps as variables. Performance evaluation, using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), showed that the LSTM model outperformed the ARIMA model. The LSTM model showed better performance, with MSE of 0.1108 and RMSE of 0.3329, compared to the ARIMA model with MSE of 0.2397 and RMSE of 0.4895.

Keywords


Autoregressive Integrated Moving Average; Long Short Term Memory; Heart Rate; Time Series; Physical Fatigue

Full Text:

PDF

References


G. P. Humairoh, R. Dani, and E. Putra, “Analisis Faktor-Faktor Yang Berhubungan Dengan Kelelahan Fisik Karyawan (Studi Kasus PT. X),†Serambi Engineering, vol. 5, no. 3, pp. 1177–1187, 2020.

S. R. Aghdam, S. Alizadeh, Y. Rasoulzadeh, and A. Safaiyan, “Fatigue Assessment Scales: A Comprehensive Literature Review Background,†Journal Homepage: Rahimian Aghdam S, Alizadeh SS, Rasoulzadeh Y, Safaiyan A. Fatigue Assessment Scales: A Comprehensive Literature Review. Arch Hyg Sci, vol. 8, no. 3, pp. 145–153, 2019.

J. Fan and A. P. Smith, “The impact of workload and fatigue on performance,†Communications in Computer and Information Science, vol. 726, pp. 90–105, 2017, doi: 10.1007/978-3-319-61061-0_6.

H. M. Abd-Elfattah, F. H. Abdelazeim, and S. Elshennawy, “Physical and cognitive consequences of fatigue: A review,†J Adv Res, vol. 6, no. 3, pp. 351–358, May 2015, doi: 10.1016/j.jare.2015.01.011.

S. A. Useche, V. G. Ortiz, and B. E. Cendales, “Stress-related psychosocial factors at work, fatigue, and risky driving behavior in bus rapid transport (BRT) drivers,†Accid Anal Prev, vol. 104, pp. 106–114, Jul. 2017, doi: 10.1016/j.aap.2017.04.023.

L. A. Ratna Winanda, T. Wahyu Adi, and N. Anwar, “MODEL PREDIKSI KELELAHAN PEKERJA KONSTRUKSI DI LOKASI PROYEK,†A SCIENTIFIC JOURNAL OF CIVIL ENGINEERING, vol. 21, no. 2, pp. 99–109, 2017.

Z. Ni, F. Sun, and Y. Li, “Heart Rate Variability-Based Subjective Physical Fatigue Assessment,†Sensors, vol. 22, no. 9, May 2022, doi: 10.3390/s22093199.

M. Oyeleye, T. Chen, S. Titarenko, and G. Antoniou, “A Predictive Analysis of Heart Rates Using Machine Learning Techniques,†Int J Environ Res Public Health, vol. 19, no. 4, Feb. 2022, doi: 10.3390/ijerph19042417.

P. T. Yamak, L. Yujian, and P. K. Gadosey, “A comparison between ARIMA, LSTM, and GRU for time series forecasting,†ACM International Conference Proceeding Series, pp. 49–55, Dec. 2019, doi: 10.1145/3377713.3377722.

D. Utomo, T.-H. Yang, D. Thi Thanh, and P.-A. Hsiung, “Driver Fatigue Prediction Using Different Sensor Data with Deep Learning,†2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), pp. 242–247, 2019, doi: 10.1109/ICPHYS.2019.8780225.

G. Abdoli, M. Mehrara, and M. E. Ardalani, “COMPARING THE PREDICTION ACCURACY OF LSTM AND ARIMA MODELS FOR TIME-SERIES WITH PERMANENT FLUCTUATION,†vol. 9, no. 2, pp. 314–339, 2020.

A. Harmain, Paiman, H. Kurniawan, kusrini, and D. Maulina, “DATA NORMALIZATION FOR K-MEANS EFFICIENCY ON GROUPS OF AREAS WITH POTENTIAL FORES AND /LAND FIRE BASED ON HEAT SPOTS DISTRIBUTION,†TEKNIMEDIA, vol. 2, no. 2, pp. 83–89, 2021.

S. Khan and H. Alghulaiakh, “ARIMA Model for Accurate Time Series Stocks Forecasting,†IJACSA) International Journal of Advanced Computer Science and Applications, vol. 11, no. 7, pp. 524–528, 2020.

A. A. Adebiyi, A. O. Adewumi, and C. K. Ayo, “Stock price prediction using the ARIMA model,†in Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014, Institute of Electrical and Electronics Engineers Inc., 2014, pp. 106–112. doi: 10.1109/UKSim.2014.67.

B. P. Yafitra, Indwiarti, and A. R. Atiqi, “PERBANDINGAN PREDIKSI HARGA SAHAM DENGAN MODEL ARIMA DAN ARTIFICIAL NEURAL NETWORK,†Ind. Journal on Computing, vol. 4, no. 2, pp. 189–198, 2019, doi: 10.21108/indojc.2019.4.2.344.

G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,†Artif Intell Rev, vol. 53, no. 8, pp. 5929–5955, Dec. 2020, doi: 10.1007/s10462-020-09838-1.

F. Shahid, A. Zameer, and M. Muneeb, “Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM,†Chaos Solitons Fractals, vol. 140, Nov. 2020, doi: 10.1016/j.chaos.2020.110212.

G. Bathla, “Stock price prediction using LSTM and SVR,†in PDGC 2020 - 2020 6th International Conference on Parallel, Distributed and Grid Computing, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, pp. 211–214. doi: 10.1109/PDGC50313.2020.9315800.

Y. Hua, Z. Zhao, R. Li, X. Chen, Z. Liu, and H. Zhang, “Deep Learning with Long Short-Term Memory for Time Series Prediction,†IEEE Communications Magazine, vol. 57, no. 6, pp. 114–119, Oct. 2018, doi: 10.1109/MCOM.2019.1800155.

A. Khumaidi, R. Raafi, I. Permana Solihin, and J. Rs Fatmawati, “Pengujian Algoritma Long Short Term Memory untuk Prediksi Kualitas Udara dan Suhu Kota Bandung,†Jurnal Telematika, vol. 15, no. 1, 2019.

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,†Journal of Electrical Systems and Information Technology, vol. 9, no. 1, Dec. 2022, doi: 10.1186/s43067-022-00054-1.




DOI: https://doi.org/10.30865/mib.v8i1.7282

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


JURNAL MEDIA INFORMATIKA BUDIDARMA
Universitas Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.