Abstract
Human activity recognition (HAR) has become an active research topic in the various fields. Depth sensor-based HAR recognizes human activities using features from depth human silhouettes via classifiers such as Hidden Markov Model (HMM), Conditional Random Fields Model etc. In this paper, we propose a new HAR system via Convolutional Neural Network (CNN), one of deep learning algorithms. We extract joint angles from multiple body joints changing in time and create a spatiotemporal feature matrix (i.e., multiple body joint angles in time). With these derived features, we train and test our CNN for HAR. In order to evaluate our system, we have compared the performance of our CNN-based HAR against the HMM- and Deep Belief Network (DBN)-based HAR using a database of Microsoft Research Cambridge-12 (MSRC-12). Our test results show that the proposed CNN-based HAR is able to recognize twelve human activities reliably and it outperforms the HMM- and DBN-based systems. We have achieved the average recognition accuracy of 98.59% for the activities. The results are 6.1% more accurate than that of the HMM-based HAR and 1.05% more accurate than that of the DBN-based HAR.
Access provided by CONRICYT-eBooks. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Nam SB, Park SU, Park JH, Uddin MZ, Kim T-S (2015) Accurate 3D human pose recognition via fusion of depth and motion sensors. Int Conf Comput Commun Devices 4(5):336–340
Iengo S, Rossi S, Staffa M, Finzi A (2014) Continuous gesture recognition for flexible human-robot interaction. IEEE Trans ICRA 2014 4863–4868
Piyathilaka L, Kodagoda S (2013) Gaussian mixture based HMM for human daily activity recognition using 3d skeleton features. IEEE 8th Conf ICIEA 2013 567–572
Jalal A, Sarif N, Kim JT, Kim T-S (2013) Human activity recognition via recognized body parts of human depth silhouettes for residents monitoring services at smart home. Indoor Built Environ 22:271–279
Nam SB, Park SU, Park JH, Kim T-S (2015) A single depth sensor based human activity recognition via deep belief network. World Conf Appl Sci 2015 015–019
Hinton GE, Osindero S, Tes Y (2006) A fast learning algorithm for deep belief nets. Neural Computation 1527–1554
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. NIPS 2012 1097–1105
LeCun Y, Bengio Y (1995) Convolutional networks for images, speech, and time-series. Handb Brain Theory Neural Networks 3361(10):1995
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2015, pp 1–9
Duffner S, Berlemont S, Lefebvre G, Garcia C (2014) 3D gesture classification with convolutional neural networks. In: ICASSP 2014, pp 5432–5436
Fothergill S, Mentis H, Kohli P, Nowozin S (2012) Instructing people for training gestural interactive systems. ACM Conference on Human Factors in Computing Systems 2012, pp. 1737–1746
Linde Y, Buzo A, Gray RM (1980) An algorithm for vector quantizer design. IEEE Trans Commun 702–710
Minsky M, Papert S (1969) Perceptrons. An introduction to computational geometry. M.I.T Press, Cambridge, vol 165, No. 3895, pp 780–782
Hecht-Nielsen R (1989) Theory of the backpropagation neural network, In: IJCNN 1989, pp 593–605
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (2014R1A2A2A09052449).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Park, J.H., Park, S.U., Zia Uddin, M., Al-antari, M.A., Al-masni, M.A., Kim, TS. (2018). A Single Depth Sensor Based Human Activity Recognition via Convolutional Neural Network. In: Vo Van, T., Nguyen Le, T., Nguyen Duc, T. (eds) 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6) . BME 2017. IFMBE Proceedings, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-10-4361-1_92
Download citation
DOI: https://doi.org/10.1007/978-981-10-4361-1_92
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4360-4
Online ISBN: 978-981-10-4361-1
eBook Packages: EngineeringEngineering (R0)