Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
IEEE Transactions on Information Technology in Biomedicine
Performance metrics for activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Towards global aerobic activity monitoring
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
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)
Confidence-based multiclass AdaBoost for physical activity monitoring
Proceedings of the 2013 International Symposium on Wearable Computers
Personalized mobile physical activity recognition
Proceedings of the 2013 International Symposium on Wearable Computers
Towards robust activity recognition for everyday life: methods and evaluation
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
QuEval: beyond high-dimensional indexing à la carte
Proceedings of the VLDB Endowment
Hi-index | 0.00 |
Physical activity monitoring has recently become an important field in wearable computing research. However, there is a lack of a commonly used, standard dataset and established benchmarking problems. In this work, a new dataset for physical activity monitoring --- recorded from 9 subjects, wearing 3 inertial measurement units and a heart rate monitor, and performing 18 different activities --- is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a standard data processing chain and 5 different classifiers. The benchmark shows the difficulty of the classification tasks and exposes some challenges, defined by e.g. a high number of activities and personalization.