Abstract
Activity recognition has recently gained a lot of interest and appears to be a promising approach to help the elderly population pursue an independent living. There already exist several methods to detect human activities based either on wearable sensors or on cameras but few of them combine the two modalities. This paper presents a strategy to enhance the robustness of indoor human activity recognition by combining wearable and depth sensors. To exploit the data captured by those sensors, we used an ensemble of binary one-vs-all neural network classifiers. Each activity-specific model was configured to maximize its performance. The performance of the complete system is comparable to lazy learning methods (k-NN) that require the whole dataset.
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References
Amft, O., Tröster, G.: Recognition of dietary activity events using on-body sensors. Artificial Intelligence in Medicine 42(2), 121–136 (2008)
Bishop, C.: Neural networks for pattern recognition. OUP, USA (1995)
Browne, G., et al.: Sensecam improves memory for recent events and quality of life in a patient with memory retrieval difficulties. Memory 19(7), 713–722 (2011)
Garcia-Pedrajas, N., Ortiz-Boyer, D.: An empirical study of binary classifier fusion methods for multiclass classification. Information Fusion 12(2), 111–130 (2011)
Hondori, H., et al.: Monitoring intake gestures using sensor fusion (microsoft kinect and inertial sensors) for smart home tele-rehab setting. In: 2012 1st Annual IEEE Healthcare Innovation Conference (2012)
Kepski, M., Kwolek, B.: Fall detection on embedded platform using kinect and wireless accelerometer. In: Miesenberger, K., Karshmer, A., Penaz, P., Zagler, W. (eds.) ICCHP 2012, Part II. LNCS, vol. 7383, pp. 407–414. Springer, Heidelberg (2012)
Lara, O.D., et al.: Centinela: A human activity recognition system based on acceleration and vital sign data. Pervasive and Mobile Computing 8(5), 717–729 (2012)
Rijsbergen, C.J.V.: Information Retrieval, 2nd edn. Butterworth-Heinemann, Newton (1979)
Roggen, D., et al.: Collecting complex activity data sets in highly rich networked sensor environments. In: Proceedings of the Seventh International Conference on Networked Sensing Systems (INSS), pp. 233–240. IEEE CSP (2010)
Sagha, H., et al.: Benchmarking classification techniques using the Opportunity human activity dataset. In: IEEE International Conference on Systems, Man, and Cybernetics (2011)
Satizábal M, H.F., Pérez-Uribe, A.: Relevance metrics to reduce input dimensions in artificial neural networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 39–48. Springer, Heidelberg (2007)
Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. In: Computer Vision and Pattern Recognition (CVPR), pp. 1297–1304. IEEE (2011)
Stiefmeier, T., et al.: Wearable activity tracking in car manufacturing. IEEE Pervasive Computing 7(2), 42–50 (2008)
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Delachaux, B., Rebetez, J., Perez-Uribe, A., Satizábal Mejia, H.F. (2013). Indoor Activity Recognition by Combining One-vs.-All Neural Network Classifiers Exploiting Wearable and Depth Sensors. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_25
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DOI: https://doi.org/10.1007/978-3-642-38682-4_25
Publisher Name: Springer, Berlin, Heidelberg
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