Human activity recognition by matching curve shapes

Abstract

In this paper, we present a new method for Human Activity Recognition (HAR) from body-worn accelerometers or inertial sensors using comparison of curve shapes. Simple motion activities have characteristic patterns that are visible in the time series representations of the sensor data. These time series representations, such as the 3D accelerations or the Euler angles (roll, pitch and yaw), can be treated as curves and activities can be recognized by matching patterns (shapes) in the curves using curve comparison and alignment techniques. We transform the sensor signals into cubic B-splines and parametrize the curves with respect to arc length for comparison. We tested our algorithm on the accelerometer data collected at Cleveland State University [1]. The 3D acceleration signals were segmented at high-level and subjectdependent ‘representative’ curves for the activities were constructed with which test curves were compared and labeled with an overall accuracy rate of 88.46% by our algorithm.