  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 Linear 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.
