Computer Science ›› 2016, Vol. 43 ›› Issue (7): 285-289.doi: 10.11896/j.issn.1002-137X.2016.07.052

Previous Articles     Next Articles

Gait Recognition Algorithm Based on Hidden Markov Model

ZHANG Xiang-gang, TANG Hai, FU Chang-jun and SHI Yu-liang   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Human gait is the pattern of walking of human being.Recently,gait recognition has been a hot research point in many research fields.Specially,the distinction of gait segmentation plays a key role on gait recognition.The use of HMMs in this paper aimed at recognizing different gait segmentation by an encoder on knee-joint and a leg-mounted accelerometer.Firstly,the methods of data pre-processing and feature extraction were used.Secondly,a model based on HMM was presented for recognizing gait segmentation,including model structure,parameters training,gait recognition.Thirdly,performance evaluation of the gait recognition was conducted,obtaining total accuracy of 91.06%,proving that HMM can accurately recognize gait segmentation and has good performance.

Key words: Hidden markov model,Gait segmentation,Gait recognition

[1] Jiang Ming,Wang Zhe-long,Liu Xiao-bo,et al.Research on human daily activity Recognition method based on BSN and CHMMs[J].Journal of Dalian University of Technology,2013,53(1):121-126(in Chinese) 姜鸣,王哲龙,刘晓博,等.基于BSN和CHMMs的人体日常动作识别方法研究[J].大连理工大学学报,2013,53(1):121-126
[2] Ugulino W,Cardador D,Vega K,et al.Wearable computing:accelerometers’ data classification of body postures and movements[M]∥Advances in Artificial Intelligence-SBIA 2012.Springer Berlin Heidelberg,2012:52-61
[3] Yang A Y,Jafari R,Sastry S S,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
[4] Meng Ming,She Qing-shan,Luo Zhi-zeng.The application ofHMM in gait recognition using lower limb SEMG[J].J.Huazhong Univ.of Tech.Sci.(Natural Science Edition),2011,39 (2):176-179(in Chinese) 孟明,佘青山,罗志增.HMM在下肢表面肌电信号步态识别中的应用[J].华中科技大学学报(自然科学版),2011,39(2):176-179
[5] Gao Yun-yuan,Meng Ming,Luo Zhi-zeng,et al.Multi-Mode and Gait Phase Recognition of Lower Limb Prosthesis Based on Multi-Source Motion Information[J].Chinese Journal of Sensors and Actuators,2012,24(11):1574-1578(in Chinese) 高云园,孟明,罗志增,等.利用多源运动信息的下肢假肢多模式多步态识别研究[J].传感技术学报,2012,24(11):1574-1578
[6] Zhang Jin-yu,Wang Lan,Zhang Li-xun.Research on real-time gait phase measuring based on multi-sensor[J].Harbin Engineering University,2007,8(2):218-221(in Chinese) 张今瑜,王岚,张立勋.基于多传感器的实时步态检测研究[J].哈尔滨工程大学学报,2007,28(2):218-221
[7] Novak D,Reberek P,De Rossi S M M,et al.Automated detection of gait initiation and termination using wearable sensors[J].Medical Engineering & Physics,2013,35(12):1713-1720
[8] Alaqtash M,Yu H,Brower R,et al.Application of wearable sen- sors for human gait analysis using fuzzy computational algorithm[J].Engineering Applications of Artificial Intelligence,2011,24(6):1018-1025
[9] De Rossi S M M,Crea S,Donati M,et al.Gait segmentation using bipedal foot pressure patterns[C]∥2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).IEEE,2012:361-366
[10] Mannini A,Sabatini A M.A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope[C]∥2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.2011:4369-4373
[11] Rabiner L.A tutorial on hidden Markov models and selected applications in speech recognition[J].Proceedings of the IEEE,1989,77(2):257-286
[12] Wilson A D,Bobick A F.Parametric hidden markov models for gesture recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(9):884-900
[13] Bao L,Intille S S.Activity recognition from user-annotated ac-celeration data[M]∥Pervasive Computing.Springer Berlin Heidelberg,2004:1-17
[14] Fawcett T.An introduction to ROC analysis[J].Pattern recognition letters,2006,27(8):861-874

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!