Utilizing wearable technology, such as smart bracelets or watches equipped with heart rate monitors, can aid in predicting mood fluctuations. These devices gather physiological signals like heart rate variations that mirror shifts in emotional states including happiness, sadness, or neutrality. The gathered data can then be processed through machine learning models to accurately forecast changes in mood. Studies have demonstrated the efficacy of these wearable devices in monitoring emotional states and even foreseeing conditions like depression and suicidal tendencies. By amalgamating diverse data sources from wearables, such as heart rate, neurocognitive sampling, and lifestyle information, personalized machine learning models have been constructed to predict depressive moods over time for individuals. This method presents a hopeful pathway for harnessing wearable technology to augment mental health surveillance and treatment.
