Exploration of interactions detectable by wearable IMU sensors

Abstract

Context aware systems like smart homes and offices will benefit from determining human-object and human-human interactions. In this paper, we explore interaction detection methods using only wearable Inertial Measurement Units (IMUs). The interactions we explore involve two actors - the primary person and a secondary object or person. We explore how several commonly used time domain signal processing operators can be utilized to detect the similar movements in the interactions and thus the interactions themselves. We also utilize a well-known boosting algorithm to potentially increase the accuracy of the operator results. The techniques operate on the magnitudes of the acceleration and gyroscope readings to keep the analysis independent of the orientation of the sensors. The detection accuracy for six interactions using the approach presented in the paper range from 84.2% to 69.6%.