Ensuring ubiquity, robustness and continuity of monitoring
is of key importance in activity recognition. To that end, multiple sensor congurations and fusion techniques are ever more used. In this paper we present a multi-sensor meta-classier that aggregates the knowledge of several sensor-based decision entities to provide a unique and reliable activity classication. This model introduces a new weighting scheme which improves the rating of the impact that each entity has on the decision fusion process. Sensitivity and specicity are particularly considered as insertion and rejection weighting metrics instead of the overall accuracy classication performance proposed in a previous work. For the sake of comparison, both new and previous weighting models together with feature fusion models are tested on an extensive activity recognition
benchmark dataset. The results demonstrate that the new weighting scheme enhances the decision aggregation thus leading to an improved recognition system.
This presentation illustrates part of the work described in the following articles:
* Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B. & Valenzuela, O.
Human activity recognition based on a sensor weighting hierarchical classifier.
Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer Berlin / Heidelberg, vol. 17, pp. 333-343 (2013)
* Banos, O., Damas, M., Pomares, H., Rojas, I.: Activity recognition based on a multi-sensor meta-classifier. In: Proceedings of the 2013 International Work Conference on Neural Networks (IWANN 2013), Tenerife, Spain, June 12-14, (2013)
Activity recognition based on a multi-sensor meta-classifier
1. Activity recognition based on
a multi-sensor hierarchical-
classifier
IWANN 2013, 12-14 June, Tenerife (Spain)
Oresti Baños, Miguel Damas, Héctor Pomares and Ignacio Rojas
Department of Computer Architecture and Computer Technology, CITIC-UGR,
University of Granada, SPAIN
DG-Research Grant #228398
2. Introduction
• Activity recognition concept
– “Recognize the actions and goals of one or more agents from a series of
observations on the agents' actions and the environmental conditions”
• Applications (among others)
– eHealth (AAL, telerehabilation)
– Sports (performance improvement, injury-free pose)
– Industrial (assembly tasks, avoidance of risk situations)
– Gaming (Kinect, Wii Mote, PlayStationMove)
• Categorization by sensor modality
– Ambient
– On-body
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24. Experimental setup: dataset
• Fitness benchmark dataset
• Up to 33 activities
• 9 IMUs (XSENS) ACC, GYR, MAG
• 17 subjects
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Baños, O., Toth M. A., Damas, M., Pomares, H., Rojas, I., Amft, O.: A benchmark dataset to evaluate sensor displacement in activity recognition.
In: 14th International Conference on Ubiquitous Computing (Ubicomp 2012), Pittsburgh, USA, September 5-8, (2012)
26. Conclusions
• We propose a multi-sensor hierarchical classifier that allows data
fusion of multiple sensors
– Its assymetric decision weighting (SEinsertions/SPrejections)
leverages the potential of the classifiers either for
classification/rejection or both
– Specially suited for complex scenarios
• Feature Fusion and MSHC are quite in line in terms of performance
however
– Our method outperforms the former when a more informative
feature set is used
– Particularly notable for complex recognition scenarios
• Our model is expected to be particularly suited to deal with sensor
anomalies (work-in-progress)
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27. On-going work…
• Our model is expected to be particularly suited to deal with
sensor anomalies (work-in-progress)
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FEAT-FUSION MSHC
0
20
40
60
80
100
Accuracy(%)
Ideal Self Induced
28. Thank you for your attention.
Questions?
Oresti Baños Legrán
Dep. Computer Architecture & Computer Technology
Faculty of Computer & Electrical Engineering (ETSIIT)
University of Granada, Granada (SPAIN)
Email: oresti@atc.ugr.es
Phone: +34 958 241 516
Fax: +34 958 248 993
Work supported in part by the HPC-Europa2 project funded by the European Commission - DG Research in the Seventh Framework Programme
under grant agreement no. 228398, the Spanish CICYT Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476 and the FPU
Spanish grant AP2009-2244.
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