Conventional recommendation systems such as for books or movies are primarily based on purchase and browsing history, which do not reflect user evaluation. This study focuses on analyzing invisible and feeble physiological tremors to reflect user evaluation. While physiological tremors are generally measured by fixing joints and wearing a light accelerometer, considering implementation, we measured them without fixing joints. This study aims to examine whether it is possible to acquire acceleration data including physiological tremors with no limitation on the posture and extract features reflecting the user interest. In the experiment, subjects read comic books on a smartphone and the acceleration data were collected using the 3D accelerometer of a smartphone. Assuming the actual use environment, subjects read comic books in both sitting and standing with no instruction on their posture. As a result of performing fast Fourier transforms, it was suggested that the acceleration data included physiological tremors and could estimate user interest while reading.
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