In conclusion, we achieved accurate real-time fitness data capturing, and we correctly recognized each reciprocal movement period from acceleration data using DSP methods. From the perspective of the whole system, we developed an Android application and a website that can be used to collect, process, transmit data and provide a variety of interactive services. We stored user’s fitness data on the web server, which can be used to perform machine learning algorithms and make recommendations to our users. Besides these, we have created a fitness community, in which people can share their exercise experience and find new friends. By using our IoT solution, users can not only cultivate good exercise habits, but also expand their social circle through the fitness community.
In the future, we will improve the accuracy of data collection by combining machine learning and DSP techniques. In addition, we will add more features to the CF algorithm, such as gender, height, weight, location, age, hobbies, etc. to further increase the recommendation diversity.
In the future, we will improve the accuracy of data collection by combining machine learning and DSP techniques. In addition, we will add more features to the CF algorithm, such as gender, height, weight, location, age, hobbies, etc. to further increase the recommendation diversity.