Human action and road condition recognition based on the inertial information_Journal of Biomedical Engineering

Authors：

 WANG Yongxiong ^1,2 , CHEN Han ¹ , YIN Zhong ¹ , YU Hongliu ^2,3 , MENG Qiaolin ^2,3

1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093. P.R.China;
2. Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093. P.R.China;
3. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093. P.R.China;

Corresponding?author：

WANG Yongxiong, Email: wyxiong@usst.edu.cn

Keywords：

human action and road condition recognition; Gaussian mixture model; hidden Markov model; intelligent prosthetic; inertial information

DOI：

10.7507/1001-5515.201712081

Video：

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Abstract Full text Figures/Tables Video References Cited by

Rapid and accurate recognition of human action and road condition is a foundation and precondition of implementing self-control of intelligent prosthesis. In this paper, a Gaussian mixture model and hidden Markov model are used to recognize the road condition and human motion modes based on the inertial sensor in artificial limb (lower limb). Firstly, the inertial sensor is used to collect the acceleration, angle and angular velocity signals in the direction of x , y and z axes of lower limbs. Then we intercept the signal segment with the time window and eliminate the noise by wavelet packet transform, and the fast Fourier transform is used to extract the features of motion. Then the principal component analysis (PCA) is carried out to remove redundant information of the features. Finally, Gaussian mixture model and hidden Markov model are used to identify the human motion modes and road condition. The experimental results show that the recognition rate of routine movement (walking, running, riding, uphill, downhill, up stairs and down stairs) is 96.25%, 92.5%, 96.25%, 91.25%, 93.75%, 88.75% and 90% respectively. Compared with the support vector machine (SVM) method, the results show that the recognition rate of our proposed method is obviously higher, and it can provide a new way for the monitoring and control of the intelligent prosthesis in the future.

Citation： WANG Yongxiong, CHEN Han, YIN Zhong, YU Hongliu, MENG Qiaolin. Human action and road condition recognition based on the inertial information. Journal of Biomedical Engineering, 2018, 35(4): 621-630. doi: 10.7507/1001-5515.201712081 Copy

1.	Sup F, Bohara A, Goldfarb M. Design and control of a powered transfemoral prosthesis. Int J Rob Res, 2008, 27(2): 263-273.
2.	Tucker M R, Olivier J, PAGEL Anna, et al. Control strategies for active lower extremity prosthetics and orthotics: a review. J Neuroeng Rehabil, 2015, 12(1): 1-29.
3.	張騰宇, 蘭陟, 樊瑜波. 智能膝關節假肢的技術發展與趨勢分析. 中國康復醫學雜志, 2017, 32(4): 451-453.
4.	楊鵬, 劉作軍, 耿艷利, 等. 智能下肢假肢關鍵技術研究進展. 河北工業大學學報, 2013, 42(1): 76-80.
5.	Powers C M, Boyd L A, Torburn L, et al. Stair ambulation in persons with transtibial amputation: an analysis of the Seattle LightFoot. J Rehabil Res Dev, 1997, 34(1): 9-18.
6.	Headon R, Curwen R. Recognizing movements from the ground reaction force, CiteSeer, 2001:1-8.
7.	Jeong J, Cho W, Kim Y, et al. Recognition of lower limb muscle EMG patterns by using neural networks during the postural balance control//3rd Kuala Lumpur International Conference On Biomedical Engineering 2006, Berlin: Springer, 2007: 82-85.
8.	劉磊, 楊鵬, 劉作軍, 等. 采用核主成分分析和相關向量機的人體運動意圖識別. 機器人, 2017, 39(5): 661-669.
9.	王振平, 喻洪流, 杜妍辰, 等. 假肢智能膝關節的研究現狀和發展趨勢. 生物醫學工程學進展, 2015, 36(3): 159-163.
10.	Wang Y, Shi Y, Wei G. A novel local feature descriptor based on energy information for human activity recognition. Elsevier Science Publishers B. V, 2017: 19-28.
11.	尹燕芳, 孫農亮, 劉明, 等. 基于BSCPs-RF的人體關節點行為識別與預測. 機器人, 2017, 39(6): 795-802.
12.	Jiménez-Fabián R, Verlinden O. Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Med Eng Phys, 2012, 34(4): 397-408.
13.	Kim S K, Hong S, Kim D. A walking motion imitation framework of a humanoid robot by human walking recognition from IMU motion data//IEEE-Ras International Conference on Humanoid Robots, IEEE, 2009:343-348.
14.	張向剛, 唐海, 付常君, 等. 一種基于隱馬爾科夫模型的步態識別算法. 計算機科學, 2016, 43(7): 285-289, 302.
15.	Preece S J, Goulermas J Y, Kenney L P, et al. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng, 2009, 56(3): 871-879.
16.	Kwapisz J R, Weiss G M, Moore S A. Activity recognition using cell phone accelerometers. Acm Sigkdd Explorati, 2011, 12(2): 74-82.
17.	Abhayasinghe N, Murray I. Human activity recognition using thigh angle derived from single thigh mounted IMU data//2014 International Conference On Indoor Positioning And Indoor Navigation (IPIN), IEEE, 2014: 111-115.
18.	Shi G Y, Zou Y X, Li W J, et al. Towards multi-classification of human motions using micro IMU and SVM training process. Adv Mat Res, 2009, 60-61: 189-193.
19.	Dehzangi O, Taherisadr M, Changalvala R. IMU-based gait recognition using convolutional neural networks and multi-sensor fusion. Sensors (Basel), 2017, 17(12): 2735-2757.
20.	Panahandeh G, Mohammadiha N, Leijon A A. Continuous hidden markov model for pedestrian activity classification and gait analysis. IEEE Trans Instrum Meas, 2013, 62(5, SI): 1073-1083.
21.	You Y, Qian Y, He T, et al. An investigation on DNN-derived bottleneck features for GMM-HMM based robust speech recognition//IEEE China Summit and International Conference on Signal and Information Processing, IEEE, 2015: 30-34.
22.	Oskoei M A, Hu Huosheng. Myoelectric control systems-A survey. Biomed Signal Process Control, 2007, 2(4): 275-294.
23.	Zhou X, Zhou C, Stewart B G. Comparisons of discrete wavelet transform, wavelet packet transform and stationary wavelet transform in denoising PD measurement data//Conference Record of the 2006 IEEE International Symposium on Electrical Insulation, IEEE, 2006: 237-240.

1. Sup F, Bohara A, Goldfarb M. Design and control of a powered transfemoral prosthesis. Int J Rob Res, 2008, 27(2): 263-273.
2. Tucker M R, Olivier J, PAGEL Anna, et al. Control strategies for active lower extremity prosthetics and orthotics: a review. J Neuroeng Rehabil, 2015, 12(1): 1-29.
3. 張騰宇, 蘭陟, 樊瑜波. 智能膝關節假肢的技術發展與趨勢分析. 中國康復醫學雜志, 2017, 32(4): 451-453.
4. 楊鵬, 劉作軍, 耿艷利, 等. 智能下肢假肢關鍵技術研究進展. 河北工業大學學報, 2013, 42(1): 76-80.
5. Powers C M, Boyd L A, Torburn L, et al. Stair ambulation in persons with transtibial amputation: an analysis of the Seattle LightFoot. J Rehabil Res Dev, 1997, 34(1): 9-18.
6. Headon R, Curwen R. Recognizing movements from the ground reaction force, CiteSeer, 2001:1-8.
7. Jeong J, Cho W, Kim Y, et al. Recognition of lower limb muscle EMG patterns by using neural networks during the postural balance control//3rd Kuala Lumpur International Conference On Biomedical Engineering 2006, Berlin: Springer, 2007: 82-85.
8. 劉磊, 楊鵬, 劉作軍, 等. 采用核主成分分析和相關向量機的人體運動意圖識別. 機器人, 2017, 39(5): 661-669.
9. 王振平, 喻洪流, 杜妍辰, 等. 假肢智能膝關節的研究現狀和發展趨勢. 生物醫學工程學進展, 2015, 36(3): 159-163.
10. Wang Y, Shi Y, Wei G. A novel local feature descriptor based on energy information for human activity recognition. Elsevier Science Publishers B. V, 2017: 19-28.
11. 尹燕芳, 孫農亮, 劉明, 等. 基于BSCPs-RF的人體關節點行為識別與預測. 機器人, 2017, 39(6): 795-802.
12. Jiménez-Fabián R, Verlinden O. Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Med Eng Phys, 2012, 34(4): 397-408.
13. Kim S K, Hong S, Kim D. A walking motion imitation framework of a humanoid robot by human walking recognition from IMU motion data//IEEE-Ras International Conference on Humanoid Robots, IEEE, 2009:343-348.
14. 張向剛, 唐海, 付常君, 等. 一種基于隱馬爾科夫模型的步態識別算法. 計算機科學, 2016, 43(7): 285-289, 302.
15. Preece S J, Goulermas J Y, Kenney L P, et al. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng, 2009, 56(3): 871-879.
16. Kwapisz J R, Weiss G M, Moore S A. Activity recognition using cell phone accelerometers. Acm Sigkdd Explorati, 2011, 12(2): 74-82.
17. Abhayasinghe N, Murray I. Human activity recognition using thigh angle derived from single thigh mounted IMU data//2014 International Conference On Indoor Positioning And Indoor Navigation (IPIN), IEEE, 2014: 111-115.
18. Shi G Y, Zou Y X, Li W J, et al. Towards multi-classification of human motions using micro IMU and SVM training process. Adv Mat Res, 2009, 60-61: 189-193.
19. Dehzangi O, Taherisadr M, Changalvala R. IMU-based gait recognition using convolutional neural networks and multi-sensor fusion. Sensors (Basel), 2017, 17(12): 2735-2757.
20. Panahandeh G, Mohammadiha N, Leijon A A. Continuous hidden markov model for pedestrian activity classification and gait analysis. IEEE Trans Instrum Meas, 2013, 62(5, SI): 1073-1083.
21. You Y, Qian Y, He T, et al. An investigation on DNN-derived bottleneck features for GMM-HMM based robust speech recognition//IEEE China Summit and International Conference on Signal and Information Processing, IEEE, 2015: 30-34.
22. Oskoei M A, Hu Huosheng. Myoelectric control systems-A survey. Biomed Signal Process Control, 2007, 2(4): 275-294.
23. Zhou X, Zhou C, Stewart B G. Comparisons of discrete wavelet transform, wavelet packet transform and stationary wavelet transform in denoising PD measurement data//Conference Record of the 2006 IEEE International Symposium on Electrical Insulation, IEEE, 2006: 237-240.

Journal of Biomedical Engineering

Human action and road condition recognition based on the inertial information

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