C4.5: programs for machine learning
C4.5: programs for machine learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Using a complex multi-modal on-body sensor system for activity spotting
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Performance metrics for activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
IEEE Transactions on Information Technology in Biomedicine
Introducing a New Benchmarked Dataset for Activity Monitoring
ISWC '12 Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC)
Centinela: A human activity recognition system based on acceleration and vital sign data
Pervasive and Mobile Computing
Creating and benchmarking a new dataset for physical activity monitoring
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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The monitoring of physical activities under realistic, everyday life conditions --- thus while an individual follows his regular daily routine --- is usually neglected or even completely ignored. Therefore, this paper investigates the development and evaluation of robust methods for everyday life scenarios, with focus on the task of aerobic activity recognition. Two important aspects of robustness are investigated: dealing with various (unknown) other activities and subject independency. Methods to handle these issues are proposed and compared, a thorough evaluation simulates usual everyday scenarios of the usage of activity recognition applications. Moreover, a new evaluation technique is introduced (leave-one-other-activity-out) to simulate when an activity recognition system is used while performing a previously unknown activity. Through applying the proposed methods it is possible to design a robust physical activity recognition system with the desired generalization characteristic.