Design and Implement a Mobile Fitness Application based on Realtime Image Detection

With the promotion of fitness, the number of people participating in sports has increased year by year. It also has increased the competition, attraction, and the development of fitness industries, in which businesses constantly evolve and refine their competency sets and solve problems and challenges effectively. At present, the mobile APP is the most extensive tool that is close to users. If it is fitted into the fitness industry, it draws the attention of consumers with reasonable and intelligent services and meets their needs. It is noted that the target customers of current fitness APPs on the market mostly are beginners and zero-based customers. The app specially designed for people with fitness foundations and professional fitness coaches is still missing. This research aims to solve the problem and design an application service for advanced users. With the involvement of the Python module, machine learning is involved in the application, which is built for Android phones and coded in the Java native code. The phone camera is used to identify images and human bones to determine whether the exercise is accurate. The exercise record is kept, and recommendation is provided. Afterward, a study to understand users' motivation and attitude is conducted. The purpose of the study is to design and develop a fitness app for advanced users with exclusive planning for effective workout control. Preliminary result shows the conceptual model and prototype of a mobile app for fitness industry. The app is in line with the concept of niche marketing and is able to apply to the fitness industries for commercial usage, such as student data management and industry competitiveness.