Abstract：This paper proposed a novel neighboring robust collaborative representation algorithm for wireless body area networks (WBANs)-based activity classification. Based on the similarity of multi-sensor action data structure, our proposed technique found out a few neighboring classes and samples associated with test sample according to the nearest neighbor principle. This allowed to construct the new training set for collaboratively representing action patterns. And then, the augmented lagrange multiplier algorithm was adopted to solve the representation coefficients and representation residuals of test sample, in order to significantly improve the classification performance. The multi-sensor action data are selected from an open wearable action recognition database (WARD) of University of California, Berkeley, in order to validate the effectiveness of our proposed technique. The results showed that our proposed method could capture more valuable correlation information associated with human action. The best accuracy was increased by 2% and the running time only spends 6.5 s, which suggested that our proposed technique was obviously superior to the sparse representation-based action classification algorithms. It is very helpful to offer a new powerful tool for recognizing action pattern in clinical application.
[1] Fortino G, Parisi D, Pirrone V. BodyCloud: A SaaS approach for community body sensor networks[J]. Future Generation Computer Systems, 2014, 35: 62-79. [2] Fortino G, Galzarano S, Gravina R, et al. A froamework for collaborative computing and multi-sensor data fusion in body sensor networks[J]. Information Fusion, 2014, 22(2015): 50-70. [3] Zhao HW, Xu RZ, Shu ML, et al. Physiological-signal-based key negotiation protocols for body sensor networks: A survey[J]. Simulation Modelling Practice and Theory, 2016, 65:32-44. [4] Korel BT, Koo GM. A survey oncontext-aware sensing for body sensor networks[J]. Wireless Sensor Network, 2010, 2 (8):571-583. [5] Gravina R, Alinia P, Ghasemzadeh H, et al. Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges[J]. Information Fusion, 2016, 35: 68-80. [6] Randell C, Muller H. Context awareness by analyzing accelerometer data[C]// Proceedings of the 4th International Symposium on Wearable Computers. Washington: IEEE Computer Society, 2000: 175-176. [7] Wei HX, He J, Tan JD. Layered hidden Markov models for real-time daily activity monitoring using body sensor networks[J], International Summer School and Symposium on Medical Devices and Biosensors, 2008,29 (2):479-494. [8] Lee YS, Cho SB. Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer.[C]// Proceedings of the 6th International Conference on Hybrid Artificial Intelligence Systems. Wroclaw:Springer-Verlag, 2011: 460-467. [9] Casale P, Pujol O, Radeva P. Human activity recognition form accelerometer data using a wearable device[C]// Iberian Conference on Pattern Recognition and Image Analysis, Berlin: Springer-Verlag, 2011: 289-296. [10] Maurer U, Smailagic A, Siewiorek DP, et al. Activity recognition and monitoring using multiple sensors on different body positions[C]// International Workshop on Wearable and Implantable Body Sensor Networks. Massachusetts: IEEE Computer Society, 2006:113-116. [11] Jatoba LC, Grobmann U, Kunze C, et al. Context-Aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity[C]// Proceedings of the 30th Annual International Conference of IEEE Engineering in Medicine and Biology Society. Vancouver: IEEE, 2008: 5250-5253. [12] Wright J, Yang AY, Ganesh A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 1-18. [13] Yang AY, Jafari R, Sastry SS, et al. Distributed recognition of human actions using wearable motion sensor networks[J]. Journal of Ambient Intelligence and Smart Environments, 2009, 1(2): 103-115. [14] Zhang M, Sawchuk AA. Human daily activity recognition with sparse representation using wearable sensors[J]. IEEE Journal of Biomedical and Health Informatics, 2013, 17(3): 553-560. [15] 肖玲,李仁发,罗娟. 体域网中一种基于压缩感知的人体动作识别方法[J]. 电子与信息学报,2013, 35(1): 119-125. [16] Huang JB, Yang MH. Fast sparse representation with prototypes[J]. Computer Vision and Pattern Recognition, 2010, 26 (2):3618-3625 [17] 陈才扣,喻以明,史俊. 一种快速的基于稀疏表示分类器[J]. 南京大学学报(自然科学版), 2012, 48(1): 70-76. [18] 吴建宁,徐海东,王佳境,等. 基于随机投影的快速稀疏表示人体动作识别方法[J]. 中国生物医学工程学报, 2016, 35(1): 38-46. [19] Zhang L, Yang M, Feng XC. Sparse representation or collaborative representation: which helps face recognition?[C]// Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona: IEEE Computer Society, 2011:471-178. [20] Zhang L, Yang M, Feng XC, et al. Collaborative representation based classification for face recognition [EB/OL]. https://arxiv.org/abs/1204.2358. [21] Zhu PF, Zuo WM, Zhang L, et al. Image set based collaborative representation for face recognition[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(7): 1120-1132. [22] Cai SJ, Zhang L, Zuo WM, et al. A probabilistic collaborative representation based approach for pattern classification[C]// Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. Nevada-Las Vegas: IEEE, 2016:2950-2959. [23] Li W, Du Q. Joint within-class collaborative representation for hyperspectral image classification[J]. IEEE Journal of selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2200-2208. [24] Bertsekas DP. Nonlinear Programming, Athena Scientific[M] (3rd Edition). Belmont: Athena Scientific, 2003:115-187. [25] Lin Z, Chen M, Wu L, et al. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices[R]. UIUC Technical Report UILU-ENG-09-2215, 2009.
