In this study, we propose an effective method for accurately detecting the number of walking steps and estimating the step length adaptively using the set of inertial sensors of a smartphone. The proposed walking behavior recognition method can be used as an important functional block in a pedestrian dead reckoning system. We develop a method for classifying the four main holding styles while walking, i.e., holding a phone in the hand while watching it, holding a phone while calling, swinging it, and putting it in a pocket. The four main holding styles are divided into 34 sub-styles, which encompass the various free styles of holding a smartphone during daily activities. Using this holding style classification, we obtain better performance when counting the walking steps and estimating the step length, although we only employ a set of feature values that are easily calculated without any complex data processing techniques. Based on numerous experiments, we demonstrate the excellent performance of the proposed method for step counting and step length estimation for various holding styles.