Optimizing Emotion Recognition with Wearable Sensor Data: Unveiling Patterns in Body Movements and Heart Rate through Random Forest Hyperparameter Tuning
<|reference_start|>Optimizing Emotion Recognition with Wearable Sensor Data: Unveiling Patterns in Body Movements and Heart Rate through Random Forest Hyperparameter Tuning: This research delves into the utilization of smartwatch sensor data and heart rate monitoring to discern individual emotions based on body movement and heart rate. Emotions play a pivotal role in human life, influencing mental well-being, quality of life, and even physical and physiological responses. The data were sourced from prior research by Juan C. Quiroz, PhD. The study enlisted 50 participants who donned smartwatches and heart rate monitors while completing a 250-meter walk. Emotions were induced through both audio-visual and audio stimuli, with participants' emotional states evaluated using the PANAS questionnaire. The study scrutinized three scenarios: viewing a movie before walking, listening to music before walking, and listening to music while walking. Personal baselines were established using DummyClassifier with the 'most_frequent' strategy from the sklearn library, and various models, including Logistic Regression and Random Forest, were employed to gauge the impacts of these activities. Notably, a novel approach was undertaken by incorporating hyperparameter tuning to the Random Forest model using RandomizedSearchCV. The outcomes showcased substantial enhancements with hyperparameter tuning in the Random Forest model, yielding mean accuracies of 86.63% for happy vs. sad and 76.33% for happy vs. neutral vs. sad.<|reference_end|>
@article{nur2024optimizing,
    title={Optimizing Emotion Recognition with Wearable Sensor Data: Unveiling
  Patterns in Body Movements and Heart Rate through Random Forest
  Hyperparameter Tuning},
    author={Zikri Kholifah Nur, Rifki Wijaya and Gia Septiana Wulandari},
    journal={Jurnal Media Informatika Budidarma, Vol. 8, No. 3, pp. 1472, 2024},
    year={2024},
    doi={10.30865/mib.v8i3.7761},
    archivePrefix={arXiv},
    eprint={2408.03958},
    primaryClass={cs.HC cs.LG}
}