A preliminary experimental outline to train machine learning models for the unobtrusive, real-time detection of acute physiological stress levels during training exercises

Jeworutzki, André; Schwarzer, Jan; von Luck, Kai; Draheim, Susanne; Wang, Qi

doi:10.1145/3453892.3461833

Verlagslink DOI:	10.1145/3453892.3461833
Titel:	A preliminary experimental outline to train machine learning models for the unobtrusive, real-time detection of acute physiological stress levels during training exercises
Sprache:	Englisch
Autorenschaft:	Jeworutzki, André Schwarzer, Jan von Luck, Kai Draheim, Susanne Wang, Qi
Schlagwörter:	activity recognition; inertial measurement units; physiological sensors; stress determination; supervised machine
Erscheinungsdatum:	29-Jun-2021
Verlag:	Association for Computing Machinery
Buchtitel:	The 14th PErvasive Technologies Related to Assistive Environments Conference
Teil der Schriftenreihe:	ACM Other conferences
Anfangsseite:	575
Endseite:	584
Konferenz:	International Conference on PErvasive Technologies Related to Assistive Environments 2021
URI:	http://hdl.handle.net/20.500.12738/13075
ISBN:	9781450387927
Einrichtung:	Department Informatik Fakultät Technik und Informatik
Dokumenttyp:	Konferenzveröffentlichung
Enthalten in den Sammlungen:	Publications without full text