Optimized Photoplethysmography-Based Classification of Calf Muscle Fatigue Using Particle Swarm Optimization with Logistic Regression

Sigit Dani Perkasa, Fadli Ama, Prisma Megantoro

Abstract


This study investigates photoplethysmography (PPG) as a non-invasive, cost-effective alternative for real-time muscle fatigue monitoring, addressing limitations inherent to conventional methods like electromyography (EMG) and blood lactate testing. A PPG-based system was developed to classify fatigued versus non-fatigued states of the calf muscle using a DFRobot SEN0203 sensor at a 1000 Hz sampling rate. The raw PPG signals were segmented into 1-second intervals and processed to compute first and second derivatives—yielding vascular (VPG) and arterial (APG) photoplethysmograms—which enabled extraction of key features including heart rate (HR), heart rate variability (HRV), peak systolic and diastolic voltages, maximum systolic slope (u), minimum diastolic slope (v), and arterial stiffness indicators (b–a and c–a ratios). A Particle Swarm Optimization (PSO) algorithm was employed to optimize both feature selection and hyperparameters within a Logistic Regression (LR) model, achieving perfect classification accuracy (1.0) with training and prediction times of 0.0053 s and 0.0016 s, respectively. Notably, HRV and the minimum diastolic slope—reflecting autonomic regulation and vascular compliance—emerged as the most influential features with weights of 12.3747 and 23.9367. Comparative analyses revealed that although LightGBM matched the PSO-LR accuracy, neural network approaches performed poorly (0.50 accuracy), likely due to overfitting and limited training data. These findings underscore the viability of PPG for muscle fatigue monitoring, with promising applications in sports science, rehabilitation, and occupational health.

Keywords


Photoplethysmography (PPG), Muscle Fatigue Monitoring, Particle Swarm Optimization, Logistic Regression

Full Text:

PDF

References


T. T. de Beukelaar and D. Mantini, “Monitoring Resistance Training in Real Time with Wearable Technology: Current Applications and Future Directions,” Bioengineering, vol. 10, no. 9, p. 1085, 2023, https://doi.org/10.3390/bioengineering10091085.

M. Meina et al., “Heart Rate Variability and Accelerometry as Classification Tools for Monitoring Perceived Stress Levels—A Pilot Study on Firefighters,” Sensors, vol. 20, no. 10, p. 2834, 2020, https://doi.org/10.3390/s20102834.

Z. Liu, V. Cascioli, and P. W. McCarthy, “Healthcare Monitoring Using Low-Cost Sensors to Supplement and Replace Human Sensation: Does It Have Potential to Increase Independent Living and Prevent Disease?,” Sensors, vol. 23, no. 4, p. 2139, 2023, https://doi.org/10.3390/s23042139.

M. H. Medagedara, A. Ranasinghe, T. D. Lalitharatne, R. A. R. C. Gopura, and G. K. Nandasiri, “Advancements in Textile-Based sEMG Sensors for Muscle Fatigue Detection: A Journey from Material Evolution to Technological Integration,” ACS Sensors, vol. 9, no. 9, pp. 4380–4401, 2024, https://doi.org/10.1021/acssensors.4c00604.

W. Sun et al., “A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors,” Sensors, vol. 22, no. 20, p. 7784, 2022, https://doi.org/10.3390/s22207784.

I. Martínez-González-Moro, I. Albertus Cámara, and M.-J. Paredes Ruiz, “Influences of Intense Physical Effort on the Activity of the Autonomous Nervous System and Stress, as Measured with Photoplethysmography,” International Journal of Environmental Research and Public Health, vol. 19, no. 23, p. 16066, 2022, https://doi.org/10.3390/ijerph192316066.

D. Kim, Y. Lee, J. Jeong, and S. Kim, “Stimulation method and individual health index study for real-time cardiovascular and autonomic nervous system reactivity analysis using PPG signal,” Biomedical Signal Processing and Control, vol. 76, p. 103714, 2022, https://doi.org/10.1016/j.bspc.2022.103714.

M. Metshein et al., “Sensor-Location-Specific Joint Acquisition of Peripheral Artery Bioimpedance and Photoplethysmogram for Wearable Applications,” Sensors, vol. 23, no. 16, p. 7111, 2023, https://doi.org/10.3390/s23167111.

J. Liu, M. Liu, Y. Bai, J. Zhang, H. Liu, W. Zhu, “Recent Progress in Flexible Wearable Sensors for Vital Sign Monitoring,” Sensors, vol. 20, no. 14, p. 4009, 2020, https://doi.org/10.3390/s20144009.

S. K. Longmore, G. Y. Lui, G. Naik, P. P. Breen, B. Jalaludin, and G. D. Gargiulo, “A Comparison of Reflective Photoplethysmography for Detection of Heart Rate, Blood Oxygen Saturation, and Respiration Rate at Various Anatomical Locations,” Sensors, vol. 19, no. 8, p. 1874, 2019, https://doi.org/10.3390/s19081874.

C. K. Vinay, V. Vazhiyal and M. Rao, "Design of a non-invasive pulse rate controlled deep vein thrombosis prophylaxis lower limb device," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5407-5410, 2019, https://doi.org/10.1109/EMBC.2019.8856441.

W. Feng et al., “Predicting physical fatigue in athletes in rope skipping training using ECG signals,” Biomedical Signal Processing and Control, vol. 83, p. 104663, 2023, https://doi.org/10.1016/j.bspc.2023.104663.

N. R. Adão Martins, S. Annaheim, C. M. Spengler, and R. M. Rossi, “Fatigue Monitoring Through Wearables: A State-of-the-Art Review,” Frontiers in Physiology, vol. 12, 2021, https://doi.org/10.3389/fphys.2021.790292.

P. Liu, Y. Song, X. Yang, D. Li, and M. Khosravi, “Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities,” Scientific Reports, vol. 14, no. 1, p. 16149, 2024, https://doi.org/10.1038/s41598-024-66839-8.

P. Faragó, L. Grama, M.-A. Farago, and S. Hintea, “A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics,” Applied Sciences, vol. 11, no. 1, p. 268, 2020, https://doi.org/10.3390/app11010268.

K. Vinay, K. Nagaraj, H. R. Arvinda, V. Vikas and M. Rao, "Design of a Device for Lower Limb Prophylaxis and Exercise," IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1-7, 2021, https://doi.org/10.1109/JTEHM.2020.3037018.

L. M. S. do Nascimento, L. V. Bonfati, M. L. B. Freitas, J. J. A. Mendes Junior, H. V. Siqueira, and S. L. Stevan, “Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review,” Sensors, vol. 20, no. 15, p. 4063, 2020, https://doi.org/10.3390/s20154063.

M. Tabbakha et al., "PPG Segmentation Using Deep Learning," 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC), pp. 1-5, 2023, https://doi.org/10.1109/ICMNWC60182.2023.10435787.

T. Zhao, Y. Wang, J. Liu, Y. Chen, J. Cheng and J. Yu, "TrueHeart: Continuous Authentication on Wrist-worn Wearables Using PPG-based Biometrics," IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, pp. 30-39, 2020, https://doi.org/10.1109/INFOCOM41043.2020.9155526.

H. Shin, S. Sun, J. Lee and H. C. Kim, "Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network," IEEE Access, vol. 9, pp. 70639-70649, 2021, https://doi.org/10.1109/ACCESS.2021.3078534.

S.-H. Liu, R.-X. Li, J.-J. Wang, W. Chen, and C.-H. Su, “Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume,” Applied Sciences, vol. 10, no. 13, p. 4612, 2020, https://doi.org/10.3390/app10134612.

M. Elgendi, Y. Liang, and R. Ward, “Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms,” Diseases, vol. 6, no. 1, p. 20, 2018, https://doi.org/10.3390/diseases6010020.

L.-P. Yao and Z. Pan, “Cuff-less blood pressure estimation from photoplethysmography signal and electrocardiogram,” Physical and Engineering Sciences in Medicine, vol. 44, no. 2, pp. 397–408, 2021, https://doi.org/10.1007/s13246-021-00989-1.

B. N. Krupa, K. Bharathi, M. Gaonkar, S. Karun, S. Nath, and M. A. M. Ali, “Multiclass Classification of APG Signals using ELM for CVD Risk Identification: A Real-Time Application,” The 16th International Conference on Biomedical Engineering, pp. 32–37, 2017, https://doi.org/10.1007/978-981-10-4220-1_7.

E. J. Argüello-Prada and C. D. Castaño Mosquera, “A Machine Learning Approach to Predict Blood Pressure Risk Levels Using Only Maxima and Minima of Photoplethysmogram (Ppg) First and Second Derivatives,” SSRN, 2024, https://dx.doi.org/10.2139/ssrn.4797403.

H. Sharma, “Heart rate extraction from PPG signals using variational mode decomposition,” Biocybernetics and Biomedical Engineering, vol. 39, no. 1, pp. 75–86, 2019, https://doi.org/10.1016/j.bbe.2018.11.001.

M. Bolanos, H. Nazeran and E. Haltiwanger, "Comparison of Heart Rate Variability Signal Features Derived from Electrocardiography and Photoplethysmography in Healthy Individuals," 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4289-4294, 2006, https://doi.org/10.1109/IEMBS.2006.260607.

F. Esgalhado, A. Batista, V. Vassilenko, S. Russo, and M. Ortigueira, “Peak Detection and HRV Feature Evaluation on ECG and PPG Signals,” Symmetry, vol. 14, no. 6, p. 1139, 2022, https://doi.org/10.3390/sym14061139.

M. Z. Suboh, R. Jaafar, N. A. Nayan, N. H. Harun, and M. S. F. Mohamad, “Analysis on Four Derivative Waveforms of Photoplethysmogram (PPG) for Fiducial Point Detection,” Frontiers in Public Health, vol. 10, 2022, https://doi.org/10.3389/fpubh.2022.920946.

G. Thambiraj, U. Gandhi, U. Mangalanathan, V. J. M. Jose, and M. Anand, “Investigation on the effect of Womersley number, ECG and PPG features for cuff less blood pressure estimation using machine learning,” Biomedical Signal Processing and Control, vol. 60, p. 101942, 2020, https://doi.org/10.1016/j.bspc.2020.101942.

M. Ghamari, “A review on wearable photoplethysmography sensors and their potential future applications in health care,” International Journal of Biosensors & Bioelectronics, vol. 4, no. 4, p. 195, 2018, https://doi.org/10.15406/ijbsbe.2018.04.00125.

P. C. -P. Chao, C. -C. Wu, D. H. Nguyen, B. -S. Nguyen, P. -C. Huang and V. -H. Le, "The Machine Learnings Leading the Cuffless PPG Blood Pressure Sensors Into the Next Stage," IEEE Sensors Journal, vol. 21, no. 11, pp. 12498-12510, 2021, https://doi.org/10.1109/JSEN.2021.3073850.

J. Phillips, E. Cripps, J. W. Lau, and M. R. Hodkiewicz, “Classifying machinery condition using oil samples and binary logistic regression,” Mechanical Systems and Signal Processing, vol. 60–61, pp. 316–325, 2015, https://doi.org/10.1016/j.ymssp.2014.12.020.

G. Charizanos, H. Demirhan, and D. İçen, “Binary classification with fuzzy logistic regression under class imbalance and complete separation in clinical studies,” BMC Medical Research Methodology, vol. 24, no. 1, p. 145, 2024, https://doi.org/10.1186/s12874-024-02270-x.

X. Cui et al., “Adaptive LASSO logistic regression based on particle swarm optimization for Alzheimer’s disease early diagnosis,” Chemometrics and Intelligent Laboratory Systems, vol. 215, p. 104316, 2021, https://doi.org/10.1016/j.chemolab.2021.104316.

S. Alqadhi et al., “Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping,” Geocarto International, vol. 37, no. 25, pp. 9518–9543, 2022, https://doi.org/10.1080/10106049.2021.2022009.




DOI: https://doi.org/10.59247/csol.v3i1.182

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Sigit Dani Perkasa, Fadli Ama

 
Control Systems and Optimization Letters
ISSN: 2985-6116
Website: https://ejournal.csol.or.id/index.php/csol
Email: alfian_maarif@ieee.org
Publisher: Peneliti Teknologi Teknik Indonesia
Address: Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia