A novel slip detection algorithm is proposed using the innovation term of the Unscented Kalman Filter (UKF). An intentional modeling error was introduced in the dynamic model of a block resting on a slope, including tilt angle and angular velocity. The model was formulated with an assumption of no translations in x- and y- directions. This model was implemented in the UKF based on gyro and accelerometer measurements. When the block slid, the UKF innovation increased considerably due to unmodeled dynamics (i.e., translation). The smoothed innovation was used to detect slip of the block, instead of using the metrics of estimation/measurement of the translational acceleration. As proof of concept, drag-sled stick-slip experiments were conducted under dry and wet surface conditions for level and inclined surfaces. Results indicate versatility of the proposed algorithm for slip detection using boosted innovation. Because accurate metrics of estimation/measurements were not required, parameter tuning was simple, and inexpensive MEMS-based sensors provided satisfactory data quality for slip detection without further error correction.