title: Context-aware mobile health monitoring: evaluation of different pattern recognition methods for classification of physical activity.
abstract: There are various applications of physical activity monitoring for medical purposes, such as therapeutic rehabilitation, fitness enhancement or the use of physical activity as context information for evaluation of other vital data. Physical activity can be estimated using acceleration sensor-systems fixed on a person's body. By means of pattern recognition methods, it is possible to identify with certain accuracy which movement is being performed. This work presents a comparison of different methods for recognition of daily-life activities, which will serve as basis for the development of an online activity monitoring system.
authors.affiliation: Institute for Information Processing Technology, Karlsruhe, D-76128 Germany.
authors.forename: Luciana C
authors.initials: LC
authors.lastname: Jatobá
authors.forename: Ulrich
authors.initials: U
authors.lastname: Grossmann
authors.forename: Chistophe
authors.initials: C
authors.lastname: Kunze
authors.forename: Jörg
authors.initials: J
authors.lastname: Ottenbacher
authors.forename: Wilhelm
authors.initials: W
authors.lastname: Stork
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods*; D009043:Motor Activity / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019114:Remote Consultation / Q000379:methods*; D013686:Telemetry / Q000379:methods*