Pedestrian activity classification to improve human tracking and localization

The advent of miniaturized sensing technology (MEMS: Micro-Electro-Mechanical Systems) gave the possibility to collect data on different aspects of human activities. E.g. it is possible to recognize activities using hip-mounted sensing. Furthermore a dynamic changing filter model could be developed for the use in existing pedestrian localization systems (fusion algorithm) depending on the human activity to improve the estimated position. Nine degrees of freedom (DOF) sensors (acceleration, angular rate and magnetic field), such as those integrated in almost every smartphone, have been taken as the basis. With the presented algorithm it is possible to distinguish seven activities: standing, walking, running, lying, cycling, throwing and entering or leaving a car. Each activity is identified by using a linear classifier and a decision tree approach. The linear classifier parameters are set by using the k-means algorithm of the clustered features. The features for classification process include quantities like variance, frequency component, mean and the absolute value. Finally, when no periodical movement pattern is detected, the system successfully discriminates between standing and lying by estimating the projection of the gravitational field vector. Measurement data was collected with twenty subjects performing the seven different activities. Using the algorithm, activities were classified correctly with an accuracy of 91 %.An advantage of the presented method is the simple implementation, because of the low complexity of the algorithm. Furthermore, the algorithm works with low-cost sensors and is independent of any infrastructure. Possible applications are a indoor navigation scenarios in combination with Wi-Fi localization or protection of vulnerable road users in traffic situations by communicating the data to the vehicle.