Abstract
The ability to recognize and model human Activities of Daily Living (ADL) and to detect possible deviations from regular patterns, or anomalies, constitutes an enabling technology for developing effective Socially Assistive Robots. Traditional approaches aim at recognizing an anomaly behavior by means of machine-learning techniques trained on anomalies’ dataset, like subject’s falls. The main problem with these approaches lies in the difficulty to generate these dataset. In this work, we present a two-step framework implementing a new strategy for the detection of ADL anomalies. Indeed, rather than detecting anomaly behaviors, we aim at identifying those that are divergent from normal ones. This is achieved by a first step, where a deep learning technique determine the most probable ADL class related to the action performed by the subject. In a second step, a Gaussian Mixture Model is used to compute the likelihood that the action is normal or not, within that class. We performed an experimental validation of the proposed framework on a public dataset. Results are very close to the best traditional approaches, while at the same time offering the significant advantage that it is much easier to create dataset of normal ADL.
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Notes
- 1.
This work has been partially supported by MIUR within the PRIN2015 research project UPA4SAR.
References
Albert, M.V., Kording, K., Herrmann, M., Jayaraman, A.: Fall classification by machine learning using mobile phones. PLOS ONE 7(5), 1–6 (2012)
Bishop, C.M.: Novelty detection and neural network validation. In: Gielen, S., Kappen, B. (eds.) ICANN 1993, pp. 789–794. Springer, London (1993). https://doi.org/10.1007/978-1-4471-2063-6_225
Clifton, L., Clifton, D.A., Watkinson, P.J., Tarassenko, L.: Identification of patient deterioration in vital-sign data using one-class support vector machines. In: FedCSIS, pp. 125–131 (2011)
Ercolano, G., Rossi, S.: Two deep approaches for ADL recognition: a multi-scale LSTM and a CNN-LSTM with a 3D matrix skeleton representation. In: 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 877–882 (2017)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Lisowska, A., Wheeler, G., Inza, V.C., Poole, I.: An evaluation of supervised, novelty-based and hybrid approaches to fall detection using silmee accelerometer data. In: IEEE ICCVW, pp. 402–408, December 2015
Magnanimo, V., Saveriano, M., Rossi, S., Lee, D.: A Bayesian approach for task recognition and future human activity prediction. In: Robot and Human Interactive Communication, RO-MAN, pp. 726–731, August 2014
Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: Proceedings of ESANN, pp. 89–94. Presses universitaires de Louvain (2015)
Markou, M., Singh, S.: Novelty detection: a review’s - part 2: neural network based approaches. Signal Process. 83(12), 2499–2521 (2003)
Medrano, C., Igual, R., García-Magariño, I., Plaza, I., Azuara, G.: Combining novelty detectors to improve accelerometer-based fall detection. Med. Biol. Eng. Comput. 55, 1849–1858 (2017)
Medrano, C., Igual, R., Plaza, I., Castro, M.: Detecting falls as novelties in acceleration patterns acquired with smartphones. PLOS ONE 9(4), 1–9 (2014)
Meng, L., Miao, C., Leung, C.: Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing. Multimed. Tools Appl. 76, 1–21 (2016)
Micucci, D., Mobilio, M., Napoletano, P.: UniMiB SHAR: a dataset for human activity recognition using acceleration data from smartphones. Appl. Sci. 7(10), 1101 (2017)
Miljković, D.: Review of novelty detection methods. In: Mipro, 2010 Proceedings of the 33rd International Convention, pp. 593–598. IEEE (2010)
Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Signal Process. 99, 215–249 (2014)
Rossi, S., Ferland, F., Tapus, A.: User profiling and behavioral adaptation for HRI: a survey. Pattern Recognit. Lett. 99(Supplement C), 3–12 (2017)
Sabokrou, M., Fayyaz, M., Fathy, M., Klette, R.: Deep-cascade: cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Trans. Image Process. 26(4), 1992–2004 (2017)
Staffa, M., Gregorio, M.D., Giordano, M., Rossi, S.: Can you follow that guy? In: 22th European Symposium on Artificial Neural Networks, ESANN, pp. 511–516 (2014)
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Rossi, S., Bove, L., Di Martino, S., Ercolano, G. (2018). A Two-Step Framework for Novelty Detection in Activities of Daily Living. In: Ge, S., et al. Social Robotics. ICSR 2018. Lecture Notes in Computer Science(), vol 11357. Springer, Cham. https://doi.org/10.1007/978-3-030-05204-1_32
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DOI: https://doi.org/10.1007/978-3-030-05204-1_32
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