Optimizing Emotion Recognition with Wearable Sensor Data: Unveiling Patterns in Body Movements and Heart Rate through Random Forest Hyperparameter Tuning
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C. L. Park et al., “Emotional Well-Being: What It Is and Why It Matters,†Affect Sci, vol. 4, no. 1, pp. 10–20, Mar. 2023, doi: 10.1007/s42761-022-00163-0.
R. Sun et al., “Emotional experiences and psychological well-being in 51 countries during the COVID-19 pandemic.,†Emotion, vol. 24, no. 2, pp. 397–411, Mar. 2024, doi: 10.1037/emo0001235.
A. López-Alcarria, A. Olivares-Vicente, and F. Poza-Vilches, “A Systematic Review of the Use of Agile Methodologies in Education to Foster Sustainability Competencies,†Sustainability, vol. 11, no. 10, p. 2915, May 2019, doi: 10.3390/su11102915.
S. Shajari, K. Kuruvinashetti, A. Komeili, and U. Sundararaj, “The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review,†Sensors, vol. 23, no. 23, p. 9498, Nov. 2023, doi: 10.3390/s23239498.
M. Guo, X. Zhang, Z. Niu, and Z. Gao, “Wearable Devices for Emotion Visualization: State of the Art, Benefits, and Challenges,†in Proceedings of the 11th International Conference on Digital and Interactive Arts, New York, NY, USA: ACM, Nov. 2023, pp. 1–10. doi: 10.1145/3632776.3632794.
S. Kumar, P. Tiwari, and M. Zymbler, “Internet of Things is a revolutionary approach for future technology enhancement: a review,†J Big Data, vol. 6, no. 1, pp. 1–21, Dec. 2019, doi: 10.1186/S40537-019-0268-2/FIGURES/9.
T. Wang and H. Zhang, “Using Wearable Devices for Emotion Recognition in Mobile Human- Computer Interaction: A Review,†Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13519 LNCS, pp. 205–227, 2022, doi: 10.1007/978-3-031-17618-0_16/FIGURES/6.
S. Saganowski et al., “Emotion Recognition Using Wearables: A Systematic Literature Review Work in progress,†Dec. 2019.
S. Pal, S. Mukhopadhyay, and N. Suryadevara, “Development and Progress in Sensors and Technologies for Human Emotion Recognition,†Sensors, vol. 21, no. 16, p. 5554, Aug. 2021, doi: 10.3390/s21165554.
J. C. Quiroz, E. Geangu, and M. H. Yong, “Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study,†JMIR Ment Health, vol. 5, no. 3, p. e10153, Aug. 2018, doi: 10.2196/10153.
A. V. Geetha, T. Mala, D. Priyanka, and E. Uma, “Multimodal Emotion Recognition with Deep Learning: Advancements, challenges, and future directions,†Information Fusion, vol. 105, p. 102218, May 2024, doi: 10.1016/J.INFFUS.2023.102218.
I. Brdar, “Positive and Negative Affect Schedule (PANAS),†Encyclopedia of Quality of Life and Well-Being Research, pp. 5310–5313, 2023, doi: 10.1007/978-3-031-17299-1_2212.
D. L. Shanthi and N. Chethan, “Genetic Algorithm Based Hyper-Parameter Tuning to Improve the Performance of Machine Learning Models,†SN Comput Sci, vol. 4, no. 2, pp. 1–8, Mar. 2023, doi: 10.1007/S42979-022-01537-8/TABLES/1.
S. González, S. GarcÃa, J. Del Ser, L. Rokach, and F. Herrera, “A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities,†Information Fusion, vol. 64, pp. 205–237, Dec. 2020, doi: 10.1016/J.INFFUS.2020.07.007.
P. Probst, “Hyperparameters, tuning and meta-learning for random forest and other machine learning algorithms,†Jul. 2019, Ludwig-Maximilians-Universität München. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bvb:19-245579
M. Dhilsath Fathima and S. J. Samuel, “Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease,†Computational Intelligence and Healthcare Informatics, pp. 139–158, Oct. 2021, doi: 10.1002/9781119818717.CH8.
K. Pal and B. V. Patel, “Emotion classification with reduced feature set sgdclassifier, random forest and performance tuning,†Communications in Computer and Information Science, vol. 1235 CCIS, pp. 95–108, 2020, doi: 10.1007/978-981-15-6648-6_8/FIGURES/8.
P. Probst, M. N. Wright, and A. Boulesteix, “Hyperparameters and tuning strategies for random forest,†WIREs Data Mining and Knowledge Discovery, vol. 9, no. 3, May 2019, doi: 10.1002/widm.1301.
P. J. Bota, C. Wang, A. L. N. Fred, and H. Placido Da Silva, “A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals,†IEEE Access, vol. 7, pp. 140990–141020, 2019, doi: 10.1109/ACCESS.2019.2944001.
S. Koelstra et al., “DEAP: A database for emotion analysis; Using physiological signals,†IEEE Trans Affect Comput, vol. 3, no. 1, pp. 18–31, Jan. 2012, doi: 10.1109/T-AFFC.2011.15.
R. H. Jhaveri, A. Revathi, K. Ramana, R. Raut, and R. K. Dhanaraj, “A Review on Machine Learning Strategies for Real-World Engineering Applications,†Mobile Information Systems, vol. 2022, pp. 1–26, Aug. 2022, doi: 10.1155/2022/1833507.
M. K. Suryadi, R. Herteno, S. W. Saputro, M. R. Faisal, and R. A. Nugroho, “Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction,†Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 6, no. 2, pp. 137–147, Mar. 2024, doi: 10.35882/jeeemi.v6i2.375.
A. Saidi, S. Ben Othman, M. Dhouibi, and S. Ben Saoud, “FPGA-based implementation of classification techniques: A survey,†Integration, vol. 81, pp. 280–299, Nov. 2021, doi: 10.1016/j.vlsi.2021.08.004.
R. Genuer, J.-M. Poggi, R. Genuer, and J.-M. Poggi, Random forests. Springer, 2020.
P. Probst, A.-L. Boulesteix, and B. Bischl, “Tunability: Importance of hyperparameters of machine learning algorithms,†Journal of Machine Learning Research, vol. 20, no. 53, pp. 1–32, 2019.
DOI: https://doi.org/10.30865/mib.v8i3.7761
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