A Novel Fitness Tracker Using Edge Machine Learning


Several characteristics of the human body turn into postural behavior, recognizable also during sport activities. The presence of differences between body types could lead to different behavior of wearable and fitness-devote products. A new wearable based on machine learning techniques for the exercise detection and repetitions count is described in this work. A proper dataset has been obtained in order to offline train the network. Eventually, the machine learning algorithm has been implemented inside an edge device for real-time test e verification.



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL