Using Acceleration Data for Detecting Temporary Cognitive Overload in Health Care Exemplified Shown in a Pill Sorting Task

In this paper we propose a new approach for detecting temporary cognitive overload. Due to the raising propagation of wearable devices with various integrated sensors, the idea is to detect such overload situations based on acceleration data out of these sensors at task relevant body parts. We executed an experiment in order to investigate the performance differences of people in a relaxed state and under cognitive load. The loaded state was simulated in a dual-task test. Additionally, we analyzed changes in the participants' motion behaviors at their hips and both of their wrists. We could show, that dual-task measuring is a suitable way for generating ground truth data for cognitive load. For this reason we used the study's data also as ground truth for the subsequent developed classification system. After investigating different features from the data we could discriminate the two states ("relaxed" and "loaded") with an accuracy of 90% and an MCC of 0.7986, which indicates a high correlation between ground truth and classified data. That outperforms other ACC based systems and approaches the performance of vital parameter based ones. Moreover, it could be shown that the dominant hand's data have greater influence to the results than the recessive one's. However, using data from both hands leads to further improvements.