Wearable devices focused on fitness, safety, and data monitoring have
become increasingly common. It is not surprising, given that a sedentary lifestyle can
lead to various health issues such as weight gain, chronic and acute illnesses, and
reduced productivity in daily life. Researchers from both business and academic
settings have delved deeply into monitoring fitness, examining aspects like sleep
patterns, cardiac health, activity levels, overall well-being, and recovery from illness.
They employ a range of techniques including deep learning, machine learning, and
statistical methods to analyze the data collected by these wearables. This study aims to
gather data from Fitbit Inspire HR and Fitbit Versa using the Fitbit API and to
investigate any anomalies, trends, and correlations within the collected healthcare data.
The data is classified using the K-means algorithm based on various parameters of the
Fitbit healthcare data.
Keywords: Activity monitoring, Fitbit data, Healthcare analytics, Health behaviors, K-means, Machine learning.
