Human Activity Recognition

With advancement in the field of health and medicine, new and better sensors are being developed which help monitor various aspects of an individual’s health. To provide the users with effective analysis related to their health using these sensors, an optimized and accurate solution is required. Due to the increase in health awareness and user-friendliness, growth in the fitness tracker market is expected to grow in the coming years. The main objective of this study is to analyze various machine learning algorithms which will use the data provided by these sensors and extrapolate information from this data which will be useful in tracking the physical activities of a user. Different research papers already published on this have been studied in order to understand their methodology and obtained results are improved. A comparative study of different algorithms to find the most accurate one has been implemented and performed.