User-independent activity recognition for industrial assembly lines-feature vs. instance selection

This study concentrated on real-time monitoring of a worker using wearable-sensor-based activity recognition. An inertial measurement unit was attached to both wrists of the worker and, by using acceleration and angle speed information, the activities performed by the worker were recognized. Online recognition was done using the sliding window method to divide the data into two-second intervals, and the activity performed in each window was recognized using a knn classifier. Moreover, especially the real-time aspect of the system was considered by studying ways to decrease the amount of training data needed in knn recognition. The studied methods for decreasing the data set included feature and instance selection methods. The results show that instance selection is the best solution for data reduction, while the overall recognition accuracy of the system is high enough for use on industrial assembly lines. When an independent test set is used, the recognition rates using an instance selection method are on average 79.2% which is six percentage units better than the best result using feature selection methods.