Playing sports provides long-term health benefits for people of all ages, backgrounds, and abilities. During exercising, fitness trackers are an option that help keep people moving, increase their motivations through feedback and help them gain fitness and lose weight.
In the past few years, there had been several attempts to track people’s fitness progress such as calorie counter, step counter, distance tracker, speed and heart rate monitor. However, very few attempts had been made on tracking fitness progress on different fitness machines, such as doing exercises on chest press, by using only one fitness tracker. And only a small number of equipment in the gym support providing real-time exercise data to users, but there is no suitable application that could take advantage of such useful data.
In the past few years, there had been several attempts to track people’s fitness progress such as calorie counter, step counter, distance tracker, speed and heart rate monitor. However, very few attempts had been made on tracking fitness progress on different fitness machines, such as doing exercises on chest press, by using only one fitness tracker. And only a small number of equipment in the gym support providing real-time exercise data to users, but there is no suitable application that could take advantage of such useful data.
Therefore, we intended to develop a fitness tracker, which can be attached on every fitness machine involving reciprocating motion (e.g. abdominal machine, etc.), to help people track their fitness progress by utilizing acceleration and gyroscope data generated by every backward and forward movement of the machine.
Moreover, few fitness tracking devices were designed to recommend fitness exercises to users based on their former fitness progress. Therefore, we needed to analyze fitness data using machine learning algorithms.
Finally, we desired to build a social platform for users to share their progress with friends online. It can be achieved by community web application development based on Flask.
Moreover, few fitness tracking devices were designed to recommend fitness exercises to users based on their former fitness progress. Therefore, we needed to analyze fitness data using machine learning algorithms.
Finally, we desired to build a social platform for users to share their progress with friends online. It can be achieved by community web application development based on Flask.