Behavioral Estimation for Multiple Possession Positions Using Smartphone Accelerometers

Abstract

With the widespread use of smartphones and wearable devices, various research has been conducted using built-in sensors. For example, height estimation and road condi-tion estimation have been performed. In addition, behavioral estimation of the smartphone holder, possession position estimation, and person estimation has also been conducted. However, most of the measurement data is taken by fixing the possession position at a single location and not considering it in actuality when estimating behavior. In this research, we aim to estimate a person’s behavior by considering multiple possession positions. It is necessary to estimate a person’s behavior by considering various possession positions when using behavior estimation as a system. In addition, by treat-ing the time series data acquired by the 3-axis acceleration sensor as a 2-dimensional image using the GAF algorithm, (1) class classification by machine learning is performed.