A proposal for service design based on user’s action history using machine learning
Xinyue Wang, Nobutada Fujii, Toshiya Kaihara, Daisuke Kokuryo · 2019
With the development of IoT techniques, it become easier to collect users' action data. By analyzing and using those data, consumers and producers will mutually exchange their intelligence and better customize product development processes. This study examines a proposed system using sensor shoes with several sensor devices embedded in the insoles, collecting action data of users, extracting their action features, and then issuing some advice to help users train more efficiently. As described herein, a service model uses a backpropagation (BP) network to distinguish users' actions and to extract their action features using using Self-organization Map from the presented sensing data. Finally, we review their performance via experimentation.