| 1. |
- Reiss, Attila, et al.
(författare)
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A Competitive Approach for Human Activity Recognition on Smartphones
- 2013
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Ingår i: ESANN 2013. - : ESANN. - 9782874190810 ; , s. 455-460
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Konferensbidrag (refereegranskat)abstract
- This paper describes a competitive approach developed for an activity recognition challenge. The competition was defined on a new and publicly available dataset of human activities, recorded with smartphone sensors. This work investigates different feature sets for the activity recognition task of the competition. Moreover, the focus is also on the introduction of a new, confidence-based boosting algorithm called ConfAda- Boost.M1. Results show that the new classification method outperforms commonly used classifiers, such as decision trees or AdaBoost.M1.
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| 2. |
- Reiss, Attila, et al.
(författare)
-
Confidence-based multiclass AdaBoost for physical activity monitoring
- 2013
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Ingår i: ISWC '13: Proceedings of the 2013 International Symposium on Wearable Computers. - New York, NY, USA : ACM. - 9781450321273 ; , s. 13-20
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Konferensbidrag (refereegranskat)abstract
- Physical activity monitoring has recently become an important topic in wearable computing, motivated by e.g. healthcare applications. However, new benchmark results show that the difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. The proposed algorithm is a variant of the AdaBoost.M1 that incorporates well established ideas for confidence based boosting. The method is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository and it is also evaluated on an activity recognition and an intensity estimation problem, including a large number of physical activities from the recently released PAMAP2 dataset. The presented results indicate that the proposed ConfAdaBoost.M1 algorithm significantly improves the classification performance on most of the evaluated datasets, especially for larger and more complex classification tasks.
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| 3. |
- Reiss, Attila, et al.
(författare)
-
Towards Robust Activity Recognition for Everyday Life : Methods and Evaluation
- 2013
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Ingår i: 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2013. - : IEEE. - 9781479902965 ; , s. 25-32
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Konferensbidrag (refereegranskat)abstract
- 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.
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