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Personalized gait trajectory generation based on anthropometric features using Random Forest

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Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Using lower limb rehabilitation robots (LLRRs) to help stroke patients recover their walking ability is attracting more and more attention presently. Previous studies have shown that gait rehabilitation training with natural gait pattern can improve the therapeutic outputs. However, how to generate the personalized gait trajectory has not been well researched. In this paper, a personalized gait generation method based anthropometric features is proposed. Firstly, gait trajectories are fitted and simplified into Fourier coefficient vectors, which are used to represent gait trajectories. Secondly, fourteen body features are used to generate the personalized gait trajectories and the feature set is further optimized based on the minimal redundancy maximal relevance criterion for easy application on the LLRR. Then, the relationship between the optimized feature set and gait trajectories is modeled by using the RF algorithm. Finally, the performance of the proposed method is demonstrated by several comparison experiments.

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References

  • Amirabdollahian F, Loureiro R, Gradwell E, Collin C, Harwin W, Johnson G (2007) Multivariate analysis of the fugl-meyer outcome measures assessing the effectiveness of gentle/s robot-mediated stroke therapy. J Neuroeng Rehabil 4(1):4

    Article  Google Scholar 

  • Aoyagi D, Ichinose WE, Harkema SJ, Reinkensmeyer DJ, Bobrow JE (2007) A robot and control algorithm that can synchronously assist in naturalistic motion during body-weight-supported gait training following neurologic injury. IEEE Trans Neural Syst Rehabil Eng 15(3):387–400

    Article  Google Scholar 

  • Barbeau H, Wainberg M, Finch L (1987) Description and application of a system for locomotor rehabilitation. Med Biol Eng Comput 25(3):341–344

    Article  Google Scholar 

  • Bernhardt J, Dewey H, Thrift A, Collier J, Donnan G (2008) A very early rehabilitation trial for stroke (avert): phase ii safety and feasibility. Stroke 39(2):390–396

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Calabro RS, Cacciola A, Berte F, Manuli A, Leo A, Bramanti A, Naro A, Milardi D, Bramanti P (2016) Robotic gait rehabilitation and substitution devices in neurological disorders: where are we now? Neurol Sci 37(4):503–514

    Article  Google Scholar 

  • Chandler R, Clauser CE, McConville JT, Reynolds H, Young JW (1975) Investigation of inertial properties of the human body. Technical report, AIR FORCE AEROSPACE MEDICAL RESEARCH LAB WRIGHT-PATTERSON AFB OH

  • Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27

    Article  Google Scholar 

  • Chen G, Chan CK, Guo Z, Yu H (2013) A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Crit Rev Biomed Eng 41(4–5):343

    Article  Google Scholar 

  • Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, KDD ’16, pp 785–794

  • Colombo G, Joerg M, Schreier R, Dietz V (2000) Treadmill training of paraplegic patients using a robotic orthosis. J Rehabil Res Dev 37(6):693–700

    Google Scholar 

  • Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3(02):185–205

    Article  Google Scholar 

  • Feng Z, Qian J, Zhang Y, Shen L, Zhang Z, Wang Q (2008) Dynamic walking planning for gait rehabilitation robot. In: 2008 2nd international conference on bioinformatics and biomedical engineering. IEEE, pp 1280–1283

  • Hogan N, Krebs HI, Rohrer B, Palazzolo JJ, Dipietro L, Fasoli SE, Stein J, Hughes R, Frontera WR, Lynch D et al (2006) Motions or muscles? some behavioral factors underlying robotic assistance of motor recovery. J Rehabil Res Dev 43(5):605–618

    Article  Google Scholar 

  • Hu MH, Hsu SS, Yip PK, Jeng JS, Wang YH (2010) Early and intensive rehabilitation predicts good functional outcomes in patients admitted to the stroke intensive care unit. Disabil Rehabil 32(15):1251–1259

    Article  Google Scholar 

  • Kohavi R et al (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, Montreal, Canada, vol 14, pp 1137–1145

  • Koopman B, van Asseldonk EH, van der Kooij H (2014) Speed-dependent reference joint trajectory generation for robotic gait support. J Biomech 47(6):1447–1458

    Article  Google Scholar 

  • Kwakkel G, Kollen BJ, Krebs HI (2008) Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabil Neural Repair 22(2):111–121

    Article  Google Scholar 

  • Liaw A, Wiener M et al (2002) Classification and regression by randomforest. R News 2(3):18–22

    Google Scholar 

  • Liu DX, Du W, Wu X, Wang C, Qiao Y (2016) Deep rehabilitation gait learning for modeling knee joints of lower-limb exoskeleton. In: 2016 IEEE international conference on robotics and biomimetics (ROBIO). IEEE, pp 1058–1063

  • Luu TP, Lim HB, Qu X, Low K (2010) Subject tailored gait pattern planning for robotic gait rehabilitation. In: 2010 IEEE international conference on robotics and biomimetics (ROBIO). IEEE, pp 259–264

  • Luu TP, Lim H, Qu X, Hoon K, Low K (2011) Subject-specific lower limb waveforms planning via artificial neural network. In: 2011 IEEE international conference on rehabilitation robotics (ICORR). IEEE, pp 1–6

  • Malekipirbazari M, Aksakalli V (2015) Risk assessment in social lending via random forests. Expert Syst Appl 42(10):4621–4631

    Article  Google Scholar 

  • Meng W, Liu Q, Zhou Z, Ai Q, Sheng B, Xie SS (2015) Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics 31:132–145

    Article  Google Scholar 

  • Ming D, Jiang SL, Wang ZP, Qi HZ, Wan BK (2017) Review of walk assistant exoskeleton technology: human–machine interaction. Zidonghua Xuebao/Acta Automatica Sinica 43(7):1089–1100

    Google Scholar 

  • Murray MP (1967) Gait as a total pattern of movement: Including a bibliography on gait. Am J Phys Med Rehabil 46(1):290–333

    Google Scholar 

  • Niu X, Varoqui D, Kindig M, Mirbagheri MM (2014) Prediction of gait recovery in spinal cord injured individuals trained with robotic gait orthosis. J Neuroeng Rehabil 11(1):42

    Article  Google Scholar 

  • Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  • Song WK (2016) Trends in rehabilitation robots and their translational research in National Rehabilitation Center, Korea. Biomed Eng Lett 6(1):1–9

    Article  Google Scholar 

  • Tolstov GP (2012) Fourier series. Courier Corporation, Chelmsford

    Google Scholar 

  • Vallery H, Van Asseldonk EH, Buss M, Van Der Kooij H (2009) Reference trajectory generation for rehabilitation robots: complementary limb motion estimation. IEEE Trans Neural Syst Rehabil Eng 17(1):23–30

    Article  Google Scholar 

  • Wager S, Athey S (2017) Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc (just-accepted)

  • Wang CYE, Bobrow JE, Reinkensmeyer DJ (2005) Dynamic motion planning for the design of robotic gait rehabilitation. J Biomech Eng 127(4):672–679

    Article  Google Scholar 

  • Winnen E, Beckwée D, Meeusen R, Baeyens JP, Kerckhofs E (2014) Does robot-assisted gait rehabilitation improve balance in stroke patients? A systematic review. Topics Stroke Rehabil 21(2):87–100

    Article  Google Scholar 

  • Wirz M, Bastiaenen C, Bie RD, Dietz V (2011) Effectiveness of automated locomotor training in patients with acute incomplete spinal cord injury: a randomized controlled multicenter trial. BMC Neurol 11(1):1–5

    Article  Google Scholar 

  • Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at wadi tayyah basin, asir region, saudi arabia. Landslides 13(5):839–856

    Article  Google Scholar 

  • Yun Y, Kim HC, Shin SY, Lee J, Deshpande AD, Kim C (2013) Statistical method for prediction of gait kinematics with Gaussian process regression. J Biomech 47(1):186

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grants 91848110, 61720106012, 91648208) and the Strategic Priority Research Program of Chinese Academy of Science (Grant No. XDB32000000).

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Authors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Shixin Ren, Weiqun Wang, Zeng-Guang Hou, Xu Liang, Jiaxing Wang & Liang Peng

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Shixin Ren, Weiqun Wang, Xu Liang, Jiaxing Wang & Liang Peng

  3. The CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, 100190, China

    Zeng-Guang Hou

  4. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, 710049, China

    Badong Chen

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  1. Shixin Ren
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Correspondence to Weiqun Wang.

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Ren, S., Wang, W., Hou, ZG. et al. Personalized gait trajectory generation based on anthropometric features using Random Forest. J Ambient Intell Human Comput 14, 15597–15608 (2023). https://doi.org/10.1007/s12652-019-01390-3

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  • DOI: https://doi.org/10.1007/s12652-019-01390-3

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