Abstract
Recognizing Activities of Daily Living (ADLs) has a large number of health applications, such as characterize lifestyle for habit improvement, nursing and rehabilitation services. Wearable cameras can daily gather large amounts of image data that provide rich visual information about ADLs than using other wearable sensors. In this paper, we explore the classification of ADLs from images captured by low temporal resolution wearable camera (2 fpm) by using a Convolutional Neural Networks (CNN) approach. We show that the classification accuracy of a CNN largely improves when its output is combined, through a random decision forest, with contextual information from a fully connected layer. The proposed method was tested on a subset of the NTCIR-12 egocentric dataset, consisting of 18,674 images and achieved an overall accuracy of 86% activity recognition on 21 classes.
Keywords
A. Cartas and J. Marín—Equally contributed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Castro, D., Hickson, S., Bettadapura, V., Thomaz, E., Abowd, G., Christensen, H., Essa, I.: Predicting daily activities from egocentric images using deep learning. In: Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 75–82. ACM (2015)
Fathi, A., Farhadi, A., Rehg, J.M.: Understanding egocentric activities. In: 2011 International Conference on Computer Vision, pp. 407–414. IEEE (2011)
Fathi, A., Li, Y., Rehg, J.M.: Learning to recognize daily actions using gaze. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 314–327. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33718-5_23
Gurrin, C., Joho, H., Hopfgartner, F., Zhou, L., Albatal, R.: Overview of NTCIR-12 lifelog task. In: NTCIR-12, National Institute of Informatics (NII)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding (2014). arXiv preprint arXiv:1408.5093
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates Inc. (2012)
Ma, M., Fan, H., Kitani, K.M.: Going deeper into first-person activity recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Martin-Lesende, I., Vrotsou, K., Vergara, I., Bueno, A., Diez, A., et al.: Design and validation of the vida questionnaire, for assessing instrumental activities of daily living in elderly people. J Gerontol. Geriat. Res. 4(214), 2 (2015)
Nguyen, T.-H.-C., Nebel, J.-C., Florez-Revuelta, F.: Recognition of activities of daily living with egocentric vision: a review. Sensors (Basel), 16(1), 72 (2016). sensors-16-00072[PII]
Pirsiavash, H., Ramanan, D.: Detecting activities of daily living in first-person camera views. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2847–2854. IEEE (2012)
Schüssler-Fiorenza Rose, S.M., Stineman, M.G., Pan, Q., Bogner, H., Kurichi, J.E., Streim, J.E.: Potentially avoidable hospitalizations among people at different activity of daily living limitation stages. Health Serv. Res. 52(1), 132–155 (2016)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition (CVPR) (2015)
Acknowledgments
A.C. was supported by a doctoral fellowship from the Mexican Council of Science and Technology (CONACYT) (grant-no. 366596). This work was partially founded by TIN2015-66951-C2, SGR 1219, CERCA, ICREA Academia’2014 and 20141510 (Marató TV3). The funders had no role in the study design, data collection, analysis, and preparation of the manuscript. M.D. is grateful to the NVIDIA donation program for its support with GPU card.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Cartas, A., Marín, J., Radeva, P., Dimiccoli, M. (2017). Recognizing Activities of Daily Living from Egocentric Images. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-58838-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58837-7
Online ISBN: 978-3-319-58838-4
eBook Packages: Computer ScienceComputer Science (R0)