@conference {1384,
	title = {Feature Based Random Forest Nurse Care Activity Recognition Using Accelerometer Data},
	booktitle = {Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (UbiComp/ISWC {\textquoteright}20 Adjunct)},
	year = {2020},
	month = {2020},
	abstract = {The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data addresses the important issue about care and the need for assistance systems in the nursing profession like automatic documentation systems. Data of 12 different care activities were recorded with an accelerometer attached to the right arm of the nurses. Both, laboratory and field data were taken into account. The task was to classify each activity based on the accelerometer data. We participated as team Gudetama in the challenge. We trained a Random Forest classifier and achieved an accuracy of 61.11\% on our internal test set.},
	keywords = {activity recognition, classification, imu, nurse activity recognition challenge, random forest, supervised learning, UNIAMT},
	isbn = {9781450380768},
	doi = {10.1145/3410530.3414340},
	author = {L{\"u}bbe, Carolin and Friedrich, Bj{\"o}rn and Fudickar, Sebastian and Hellmers, Sandra and Hein, Andreas}
}
