Abstract
Human gait trajectory estimating and acquiring using human--robot interaction (HRI) is the most crucial issue for an assistive lower limb exoskeleton. The relationship between the HRI and the human gait trajectory is nonlinear, which is difficult to be obtained due to the complex dynamics parameters and physical properties of mechanism and human legs. A Gaussian process (GP) is an excellent algorithm for learning nonlinear approximation, as it is suitable for small-scale dataset. In this paper, an online sparse Gaussian process is proposed to learn the human gait trajectory, i.e., the increment of angular position of knee joints, where the input is the HRI signal and the output is the increment of angular position of knee joints. We collect the HRI signals and the actual angular position by using torque sensors and optical encoders, respectively. When collecting dataset, the subjects are required to wear the exoskeleton without actuation system and walks freely as far as possible. After purifying the dataset, a subspace of training set with appropriate dimensionality is chosen. The subspace will be regarded as the training dataset and is applied in the online sparse GP regression. A position control strategy, i.e., proportion- integration-differentiation (PID), is designed to drive the exoskeleton robot to track the learned human gait trajectory. Finally, an experiment is performed on a subject who walks on the floor wearing the exoskeleton actuated by a hydraulic system at a natural speed. The experiment results show that the proposed algorithm is able to acquire the human gait trajectory by using the physical HRI and the designed control strategy can ensure the exoskeleton system shadow the human gait trajectory.
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References
Aarno D, Kragic D (2008) Motion intention recognition in robot assisted applications. Robot Auton Syst 56(8):692–705. doi:10.1016/j.robot.2007.11.005
Andrew C (2005) Design of the Berkley lower extremity exoskeleton(BLEEX). University of California, Berkeley
Aznar F, Pujol FA, Pujol M et al. (2009) Using Gaussian processes in Bayesian robot programming. In: Distributed computing, artificial intelligence, bioinformatics, soft computing, and ambient assisted living. Springer, Berlin, Heidelberg, pp 547–553. doi:10.1007/978-3-642-02481-8_79
Boyle P, Frean M (2004) Dependent Gaussian processes. In: Advances in neural information processing systems, pp 217–224
Chandrapal M, Chen XQ, Wang W et al (2013) Preliminary evaluation of intelligent intention estimation algorithms for an actuated lower-limb exoskeleton. Int J Adv Rob Syst 10:1–10
Cheng CA, Huang TH, Huang HP (2013) Bayesian human intention estimator for exoskeleton system. In: IEEE/ASME international conference on advanced intelligent mechatronics (AIM), pp 465–470. doi:10.1109/AIM.2013.6584135
Ge SS, Li Y, He H (2011) Neural-network-based human intention estimation for physical human-robot interaction. In: 8th International conference on ubiquitous robots and ambient intelligence (URAI), pp 390–395. doi:10.1109/URAI.2011.6145849
He H, Kiguchi K (2007) A study on emg-based control of exoskeleton robots for human lower-limb motion assist. In: 6th International special topic conference on information technology applications in biomedicine, pp 292–295. doi:10.1109/ITAB.2007.4407405
Kawamoto H, Lee S, Kanbe S et al (2003) Power assist method for HAL-3 using EMG-based feedback controller. In: Proceeding of IEEE international conference on systems, man and cybernetics, 2:1648–1653. doi:10.1109/ICSMC.2003.1244649
Kazerooni H, Racine JL, Huang L et al (2005) On the control of the Berkeley lower extremity exoskeleton (BLEEX). In: Proceeding of IEEE international conference on robotics and automation, pp 4353–4360. doi:10.1109/ROBOT.2005.1570790
Kazerooni H, Steger R, Huang L (2006) Hybrid control of the Berkeley lower extremity exoskeleton (bleex). Int J Robot Res 25(5–6):561–573. doi:10.1177/02783649060655
Kiguchi K, Imada Y (2009) EMG-based control for lower-limb power-assist exoskeletons. In: IEEE Workshop on robotic intelligence in informationally structured space, pp 19–24. doi:10.1109/RIISS.2009.4937901
Kou P, Gao F (2014) Sparse Gaussian process regression model based on ℓ 1/2 regularization. Appl Intell 40(4):669–681. doi:10.1007/s10489-013-0482-0
Lawrence N, Seeger M, Herbrich R (2003) Fast sparse Gaussian process methods: the informative vector machine. In: Proceedings of the 16th annual conference on neural information processing systems (EPFL-CONF-161319):609–616
Lee H, Kim W, Han J et al (2012) The technical trend of the exoskeleton robot system for human power assistance. Int J Precis Eng Manuf 13(8):1491–1497. doi:10.1007/s12541-012-0197-x
Lewis FW, Jagannathan S, Yesildirak A (1998) Neural network control of robot manipulators and non-linear systems. CRC Press
Li Y, Ge SS (2014) Human–robot collaboration based on motion intention estimation. IEEE/ASME Trans Mechatron 19(3):1007–1014. doi:10.1109/TMECH.2013.2264533
Li J, Qu Y, Li C et al (2015) Learning local Gaussian process regression for image super-resolution. Neurocomputing 154(284–295):2014. doi:10.1016/j.neucom.11.064
Nguyen-Tuong D, Peters J (2008) Local gaussian process regression for real-time model-based robot control. In: IEEE/RSJ International conference on intelligent robots and systems, pp 380–385. doi:10.1109/IROS.2008.4650850
Park S, Mustafa SK, Shimada K (2013) Learning based robot control with sequential Gaussian process. In: IEEE Workshop on robotic intelligence in informationally structured space (RIISS), pp 120–127. doi:10.1109/RiiSS.2013.6607939
Quiñonero-Candela J, Rasmussen CE (2005) A unifying view of sparse approximate Gaussian process regression. J Mach Learn Res 6:1939–1959
Ranganathan A, Yang MH, Ho J (2011) Online sparse Gaussian process regression and its applications. IEEE Trans Image Process 20(2):391–404. doi:10.1109/TIP.2010.2066984
Rasmussen CE, Ghahramani Z (2002) Infinite mixtures of Gaussian process experts. Adv Neural Inform Process Syst 2:881–888
Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge
Sankai Y (2011) HAL: Hybrid assistive limb based on cybernics. In: Robotics research. Springer Berlin Heidelberg, pp 25–34. doi:10.1007/978-3-642-14743-2_3
Satoh H, Kawabata T, Sankai Y (2009) Bathing care assistance with robot suit HAL. In: Proceeding of IEEE international conference on robotics and biomimetics (ROBIO), pp 498–503. doi:10.1109/ROBIO.2009.5420697
Seeger M (2004) Gaussian processes for machine learning. Int J Neural Syst 14:69–106. doi:10.1142/S0129065704001899
Steger R, Kim SH, Kazerooni H (2006) Control scheme and networked control architecture for the Berkeley lower extremity exoskeleton (BLEEX). In: Proceeding of IEEE international conference on robotics and automation, pp 3469–3476. doi:10.1109/ROBOT.2006.1642232
Titsias MK (2009) Variational learning of inducing variables in sparse Gaussian processes. In: International conference on artificial intelligence and statistics, pp 567–574
Tran H, Cheng H, Lin X et al (2014) The relationship between physical human-exoskeleton interaction and dynamic factors: using a learning approach for control applications. Sci China Inform Sci 57:1–13. doi:10.1007/s11432-014-5203-8
Kirtley C CGA Normative Gait Database. Available: http://www.clinicalgaitanalysis.com/data/
Young AJ, Kuiken TA, Hargrove LJ (2014) Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses. J Neural Eng 11(5):056021. doi:10.1088/1741-2560/11/5/056021
Zhu J, Sun S (2014) Sparse Gaussian processes with manifold-preserving graph reduction. Neurocomputing 138:99–105. doi:10.1016/j.neucom.2014.02.039
Zoss AB, Kazerooni H, Chu A (2006) Biomechanical design of the Berkeley lower extremity exoskeleton(BLEEX). IEEE/ASME Trans Mechatron 11(2):128–138. doi:10.1109/TMECH.2006.871087
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Appendix A: Algorithm 1
Appendix A: Algorithm 1
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Long, Y., Du, Zj., Dong, W., Wang, Wd. (2017). Human Gait Trajectory Learning Using Online Gaussian Process for Assistive Lower Limb Exoskeleton. In: Yang, C., Virk, G., Yang, H. (eds) Wearable Sensors and Robots. Lecture Notes in Electrical Engineering, vol 399. Springer, Singapore. https://doi.org/10.1007/978-981-10-2404-7_14
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DOI: https://doi.org/10.1007/978-981-10-2404-7_14
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