Agnes Grünerbl, Gernot Bahle, Paul Lukowicz


This paper investigates the problem of recognizing
activities and dynamic ad-hoc collaboration involving multiple
users. Thus, we consider people performing various predominantly
physical, compound activities in a smart environment
(which includes personal/wearable devices). In this case, being
“compound” means that the activity can be decomposed into
primitive (atomic) actions that are executed by individual users.
We investigate how noisy recognition of the atomic actions of
individual users can be used to identify instances of cooperation
at the level of the compound activities. To this end, we first
introduce a hierarchical tree plan library model for activity
representation. Using this new model we developed an algorithm,
which allows detecting of ad-hoc team interaction without any
further knowledge about roles or preliminary designed tasks.
We evaluate the model and algorithm ”post-mortem” with data
extracted from video footage of a real nurse-emergency-training
session and with increasing difficulties by artificially adding


@inproceedings {Grünerbl:Detecting:2017:8999,
	number = {}, 
	month = {}, 
	year = {2017}, 
	title = {Detecting spontaneous collaboration in dynamic group activities from noisy individual activity data.}, 
	journal = {}, 
	volume = {}, 
	pages = {}, 
	publisher = {IEEE}, 
	author = {Agnes Grünerbl, Gernot Bahle, Paul Lukowicz}, 
	keywords = {}