Abstract
In this work, we address the problem of Human Activity Recognition (HAR), applied to service robot. In addition, a real time HRI system able to understand some common interactive human activities is designed. To classify activities into different classes, a combination of three supervised machine-learning algorithms: Support Vector Machine (SVM), Decision Tree (DT) and Artificial Neural Network (ANN) is used, based on the idea that a set of classifiers improve machine learning results. Our approach uses as input a view invariant 3D data of skeleton joints, which are rich body movement information recorded from a single Microsoft Kinect camera to create specific dataset of six interactive activities. The algorithm was able to successfully classify and recognize activities being performed in front of the camera. The system framework is realized on the Robot Operating System (ROS), and real-life activity interaction between our service robot and the user was conducted to demonstrate the effectiveness of the developed HRI system.
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References
Fiaz MK, Ijaz B (2010) Vision based human activity tracking using artificial neural networks. In: Proceedings of IEEE international conference on intelligent and advanced systems (ICIAS). Malaysia, pp 1–5
Foroughi H, Naseri A, Saberi A, Yazdi HS (2008) An eigenspace-based approach for human fall detection using integrated time motion image and neural network. In: Proceeding of IEEE 9th international conference on signal processing (ICSP). Beijing, China, pp 1499–1503
Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In Proceedings of the 17th ieee international conference on pattern recognition (ICPR), vol 3. Cambridge, UK, pp 32–36
Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22:747–757
Ribeiro PC, Santos-Victor J (2005) Human activity recognition from video: modeling, feature selection and classification architecture. In: Proceedings of the international workshop on human activity recognition and modelling (HAREM), vol 1. Oxford, UK, pp 61–70
Shotton J, Fitzgibbon A, Cook M, Sharp T, Finocchio M, Moore R, Kipman A, Blake A (2011) Real-time human pose recognition in parts from single depth images. In: IEEE conference on computer vision and pattern recognition
Chrungoo A, Manimaran SS, Ravindran B (2014) Activity recognition for natural human robot interaction. In: Social robotics. Springer, Berlin, pp 84–94
Chen H, Wang G, Xue JH, He L (2016) A novel hierarchical framework for human action recognition. Pattern Recogn 55:148–159
Zhou Z-H (2012) Ensemble methods: foundations and algorithms. CRC Press
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Kahlouche, S., Belhocine, M. (2021). Human Activity Recognition Based on Ensemble Classifier Model. In: Bououden, S., Chadli, M., Ziani, S., Zelinka, I. (eds) Proceedings of the 4th International Conference on Electrical Engineering and Control Applications. ICEECA 2019. Lecture Notes in Electrical Engineering, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-15-6403-1_78
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DOI: https://doi.org/10.1007/978-981-15-6403-1_78
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