Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human Activity Recognition

IEEE Internet of Things Journal

Volumn 3, Issue 6, 2016, Pages 1124-1134

  Tao, Dapeng ^a   Wen, Yonggang ^b   Hong, Richang ^c  

^a YUNNAN UNIVERSITY   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c HEFEI UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Accelerometers; bidirectional long short Term memory (BLSTM); human activity recognition; mobile service

Indexed keywords

ACCELEROMETERS; BRAIN; PATTERN RECOGNITION; SEMANTICS;

ACCELERATION SIGNALS; ACTIVITY RECOGNITION; ENSEMBLE CLASSIFIERS; HUMAN ACTIVITY RECOGNITION; HUMAN ACTIVITY RECOGNITION SYSTEMS; INCREMENTAL LEARNING; LONG SHORT TERM MEMORY; MOBILE SERVICE;

BEHAVIORAL RESEARCH;

EID: 85010058679     PISSN: None     EISSN: 23274662     Source Type: Journal    
DOI: 10.1109/JIOT.2016.2561962     Document Type: Article

References (51)

1. L. Bao and S. S. Intille, "Activity recognition from user-Annotated acceleration data, " in Proc. 2nd Int. Conf. Pervasive Comput., Vienna, Austria, 2004, pp. 1-17.
2. O. Banos, M. Damas, H. Pomares, A. Prieto, and I. Rojas, "Daily living activity recognition based on statistical feature quality group selection, " Expert Syst. Appl., vol. 39, no. 9, pp. 8013-8021, 2012.
3. M. Benocci et al., "Wearable assistant for load monitoring: Recognition of on-Body load placement from gait alterations, " in Proc. Int. Conf. Pervasive Comput. Technol. Healthcare, Munich, Germany, 2010, pp. 1-8.
4. D. Ciresan, U. Meier, and J. Schmidhuber, "Multi-column deep neural networks for image classification, " Comput. Vis. Pattern Recognit., pp. 3642-3649, Feb. 2012.
5. S. Dernbach, B. Das, N. C. Krishnan, B. L. Thomas, and D. J. Cook, "Simple and complex activity recognition through smart phones, " in Proc. Int. Conf. Intell. Environ., 2012, pp. 214-221.
6. M. Ermers, J. Pärkkä, J. Mäntyjärvi, and I. Korhonen, "Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions, " IEEE Trans. Inf. Technol. Biomed., vol. 12, no. 1, pp. 20-26, Jan. 2008.
7. J. Farringdon, A. J. Moore, N. Tilbury, J. Church, and P. D. Biemond, "Wearable sensor badge and sensor jacket for context awareness, " in Proc. 3rd Int. Symp. Wearable Comput., San Francisco, CA, USA, 1999, pp. 107-113.
8. D. Graham, G. Simmons, D. T. Nguyen, and G. Zhou, "A softwarebased sonar ranging sensor for smart phones, " IEEE Internet Things J., vol. 2, no. 6, pp. 479-489, Dec. 2015.
9. A. Graves and J. Schmidhuber, "Offline handwriting recognition with multidimensional recurrent neural networks, " in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 545-552.
10. A. Graves et al., "A novel connectionist system for unconstrained handwriting recognition, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 5, pp. 855-868, May 2009.
11. J. B. Gomes, S. Krishnaswamy, M. M. Gaber, P. A. C. Sousa, and E. Menasalvas, Mobile Activity Recognition Using Ubiquitous Data Stream Mining. Heidelberg, Germany: Springer, 2012, pp. 130-141.
12. T. Guerreiro, R. Gamboa, and J. Jorge, "Mnemonical body shortcuts: Improving mobile interaction, " in Proc. 15th Eur. Conf. Cognitive Ergonom. Ergonom. Cool Interact., 2008, pp. 11-18.
13. N. Gyorbiro, A. Fabian, and G. Homanyi, "An activity recognition system for mobile phones, " Mobile Netw. Appl., vol. 14, no. 1, pp. 82-91, 2009.
14. B. Hammer, "On the approximation capability of recurrent neural networks, " Neurocomputing, vol. 31, nos. 1-4, pp. 107-123, 2000.
15. Z. He, Z. Liu, L. Jin, L.-X. Zhen, and J.-C. Huang, "Weightlessness feature-A novel feature for single tri-Axial accelerometer based activity recognition, " in Proc. ICPR, Tampa, FL, USA, 2008, pp. 1-4.
16. J. J. C. Ho, "Interruptions: Using activity transitions to trigger proactive messages, " Ph.D. dissertation, Dept. Elect. Eng. Comput. Sci., Massachusetts Inst. Technol., Cambridge, MA, USA, 2004.
17. S. Hochreiter and J. Schmidhuber, "Long short-Term memory, " Neural Comput., vol. 9, no. 8, pp. 1735-1780, 1997.
18. R. Jain, "Toward social life networks, " IEEE Comput., vol. 47, no. 11, pp. 86-88, Nov. 2014.
19. R. Jain, L. Jalali, S. Pongpaichet, and A. Gupta, "Building social life networks, " IEEE Data Eng. Bull., vol. 36, no. 3, pp. 91-98, Sep. 2013.
20. N. Kern, B. Schiele, and A. Schmidt, "Recognizing context for annotating a live life recording, " Pers. Ubiquitous Comput., vol. 11, no. 4, pp. 251-263, 2007.
21. J. Lester, T. Choudhury, and G. Borriello, "A practical approach to recognizing physical activities, " in Pervasive Computing. Heidelberg, Germany: Springer, 2006, pp. 1-16.
22. D. Liang, Z. Zhang, and M. Peng, "Access point reselection and adaptive cluster splitting-based indoor localization in wireless local area networks, " IEEE Internet Things J., vol. 2, no. 6, pp. 573-585, Dec. 2015.
23. L. Lin, C. Chen, M.-L. Shyu, and S.-C. Chen, "Weighted subspace filtering and ranking algorithms for video concept retrieval, " IEEE Multimedia, vol. 18, no. 3, pp. 32-43, Mar. 2011.
24. T. Liu and D. Tao, "Classification with noisy labels by importance reweighting, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 3, pp. 447-461, Mar. 2016.
25. A. Lombardo, C. Panarello, and G. Schembra, "A model-Assisted crosslayer design of an energy-efficient mobile video cloud, " IEEE Trans. Multimedia, vol. 16, no. 8, pp. 2307-2322, Dec. 2014.
26. A. Mannini, S. S. Intille, M. Rosenberger, A. M. Sabatini, and W. Haskell, "Activity recognition using a single accelerometer placed at the wrist or ankle, " Med. Sci. Sports Exerc. vol. 45, no. 11, pp. 2193-2203, 2013.
27. U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher, "Activity recognition and monitoring using multiple sensors on different body positions, " in Proc. Int. Workshop Wearable Implantable Body Sensor Netw., Cambridge, MA, USA, 2006, pp. 113-116.
28. C. McCall, K. Reddy, and M. Shah, "Macro-class selection for hierarchical k-NN classification of inertial sensor data, " in Proc. Int. Conf. Pervasive Embedded Comput. Commun. Syst., Rome, Italy, 2012, pp. 106-114.
29. B. Nham, K. Siangliulue, and S. Yeung, "Predicting mode of transport from iPhone accelerometer data, " Mach. Learn. Final Projects, Stanford Univ., Stanford, CA, USA 2008.
30. S. Pirttikangas, K. Fujinami, and T. Nakajima, "Feature selection and activity recognition from wearable sensors, " in Ubiquitous Computing Systems. Heidelberg, Germany: Springer, 2006, pp. 516-527.
31. N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman, "Activity recognition from accelerometer data, " in Proc. Nat. Conf. Artif. Intell., 2005, pp. 1541-1546.
32. S. Samanta and B. Chanda, "Space-Time facet model for human activity classification, " IEEE Trans. Multimedia, vol. 16, no. 6, pp. 1525-1535, Oct. 2014.
33. H. Si, Y. Kawahara, H. Kurasawa, H. Morikawa, and T. Aoyama, "A context-Aware collaborative filtering algorithm for real world oriented content delivery service, " in Proc. ubiPCMM, 2005.
34. J. Suutala, S. Pirttikangas, and J. Roning, "Discriminative temporal smoothing for activity recognition from wearable sensors, " in Proc. Ubiquitous Comput. Syst., Tokyo, Japan, 2007, pp. 182-195.
35. D. Tao, L. Jin, Y. Yuan, and Y. Xue, "Ensemble manifold rank preserving for acceleration-based human activity recognition, " IEEE Trans. Neural Netw. Learn. Syst., to be published, doi: 10.1109/TNNLS.2014.2357794.
36. D. Tao, L. Liang, L. Jin, and Y. Gao, "Similar handwritten Chinese character recognition by kernel discriminative locality alignment, " Pattern Recognit. Lett., vol. 35, pp. 186-194, Jan. 2014.
37. M. Thejaswini, P. Rajalakshmi, and U. B. Desai, "Novel sampling algorithm for human mobility-based mobile phone sensing, " IEEE Internet Things J., vol. 2, no. 3, pp. 210-220, Jun. 2015.
38. W. Ugulino et al., "Wearable computing: Accelerometers data classification of body postures and movements, " in Advances in Artificial Intelligence-SBIA. Heidelberg, Germany: Springer, 2012, pp. 52-61.
39. C. Wang, Z. Liu, and S.-C. Chan, "Superpixel-based hand gesture recognition with kinect depth camera, " IEEE Trans. Multimedia, vol. 17, no. 1, pp. 29-39, Jan. 2015.
40. M. Weber, G. Bleser, M. Liwicki, and D. Stricker, "Unsupervised motion pattern learning for motion segmentation, " in Proc. ICPR, Tsukuba, Japan, 2012, pp. 202-205.
41. J. Xiao, Z. Zeng, and A. Wu, "New criteria for exponential stability of delayed recurrent neural networks, " Neurocomputing, vol. 134, pp. 182-188, Jun. 2014.
42. C. Xu, D. Tao, and C. Xu, "Large-margin multi-view information bottleneck, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 8, pp. 1559-1572, Aug. 2014.
43. Y. Yang, H.-Y. Ha, F. C. Fleites, and S.-C. Chen, "A multimedia semantic retrieval mobile system based on HCFGs, " IEEE Multimedia, vol. 21, no. 1, pp. 36-46, Jan./Mar. 2014.
44. Z. Yuan, J. Sang, C. Xu, and Y. Liu, "A unified framework of latent feature learning in social media, " IEEE Trans. Multimedia, vol. 16, no. 6, pp. 1624-1635, Oct. 2014.
45. O. Yurur, C. H. Liu, and W. Moreno, "Light-weight online unsupervised posture detection by smartphone accelerometer, " IEEE Internet Things J., vol. 2, no. 4, pp. 329-339, Aug. 2015.
46. X. Zhang, Y. Tian, T. Huang, S. Dong, and W. Gao, "Optimizing the hierarchical prediction and coding in HEVC for surveillance and conference videos with background modeling, " IEEE Trans. Image Process., vol. 23, no. 10, pp. 4511-4526, Oct. 2014.
47. X. Zhang, T. Huang, Y. Tian, and W. Gao, "Background-modeling-based adaptive prediction for surveillance video coding, " IEEE Trans. Image Process., vol. 23, no. 2, pp. 769-784, Feb. 2014.
48. L. Zhou and H. Wang, "Toward blind scheduling in mobile media cloud: Fairness, simplicity, and asymptotic optimality, " IEEE Trans. Multimedia, vol. 15, no. 4, pp. 735-746, Jun. 2013.
49. L. Zhou, H. Wang, and M. Guizani, "How mobility impacts video streaming over multi-hop wireless networks?" IEEE Trans. Commun., vol. 60, no. 7, pp. 2017-2028, Jul. 2012.
50. Z. Zeng, T. Huang, and W. X. Zheng, "Multistability of recurrent neural networks with time-varying delays and the piecewise linear activation function, " IEEE Trans. Neural Netw., vol. 21, no. 8, pp. 1371-1377, Aug. 2010.
51. Y. Zheng, W.-K. Wong, X. Guan, and S. Trost, "Physical activity recognition from accelerometer data using a multi-scale ensemble method, " in Proc. 25th Conf. Innov. Appl. Artif. Intell., Jul. 2013, pp. 14-18.