Evolutionary Computing in Promoting Sustainable Health Through Fitness Tracker Data Analytics
摘要
In the pursuit of healthier lifestyles, wearable fitness trackers have emerged as valuable tools for monitoring physical activity and vital signs. This study leverages data from a popular fitness tracker to explore patterns in user activity, sleep, and heart rate metrics. We dissected information from more than 1,000 clients north of a 6-month time frame to survey the effect of day to day advances, dynamic minutes, and rest quality on generally speaking wellbeing results. Our discoveries show that clients who accomplished a normal of 10,000 stages each day encountered a 15% improvement in cardiovascular wellness, as estimated by resting pulse decrease. Moreover, clients with something like 7 h of rest every night showed a 12% higher recovery rate after exceptional proactive undertakings diverged from those with less rest. Simulated intelligence models were applied to predict prosperity redesigns considering individual development plans, with an accuracy speed of 85%. These results feature the ability of health trackers to give critical pieces of information to clients proposing to deal with their prosperity and thriving. The concentrate also includes open entryways for tweaked prosperity recommendations through state of the art examination.