Abstract
This paper proposes a novel mechanical design of a lower limb exoskeleton device which prevents the residual stresses due to arthro-kinematics movements of synovial joints and by the way allows effective compensation for dynamic disturbances in osteo-kinematic movements of the wearer. Here, the exoskeleton is only actuated at the knee joints to provide assistive torques, which are required to assist the anatomical joint motion and to increase the transparency of the device. Dynamic simulations of a virtual human equipped with this exoskeleton are used to quantify the disturbances induced by the device during locomotion and to show the benefit of passive mechanisms introduced in the mechanical attaches as well. The authors also demonstrated how the device’s transparency can be improved by providing the motor torques in order to compensate the inertial and gravitational effects. This can be done rely on the knowledge of the locomotion movement phases. A robust gait phase detection method was implemented on the experimental device in order to identify specific gait phases in real time. This method exploits the K-nearest neighbors algorithm to identify the k-closest trained vectors, coupling with a discrete time Markov chain to determine the phases shift probability during the gait cycle. This gait detection algorithm was tested with a percentage of success of more than 95% when the subjects walked with constant and variable stride lengths.
Similar content being viewed by others
References
Bishop M, Brunt D, Pathare N, Patel B (2004) The effect of velocity on the strategies used during gait termination. Gait Posture 20:134–139
Cai VAD, Bru B, Bidaud P, Hayward V, Gosselin F, Pasqui V (2010) Experimental evaluation of a goniometer for the identification of anatomical joint motions. In: Proceedings of the 13th international conference on climbing and walking robots and the support technologies for mobile machines, pp 1255–1262
Cai VAD, Bidaud P, Hayward V, Gosselin F, Dessaily E (2011) Self-adjusting isostatic exoskeleton for the human knee joint. In: Annual international conference of the IEEE engineering in medicine and biology society, pp 612–618
Cai VAD, Nguyen VL, Bidaud P (2014) Instrumented and active exoskeletons for human anatomical joints: design methodology and applications. In: Proceedings of the 2014 IEEE international conference on robotics and biomimetics, pp 974–979
Cavanagh PR, Komi PV (1979) Electromechanical delay in human skeletal muscle under concentric and eccentric contractions. Eur J Appl Physiol Occup Physiol 42(3):159–63
Chen K, Bassett D Jr (2005) The technology of accelerometrybased activity monitors: current and future. Med Sci Sports Exerc 37(11):S490–S500
Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ (2013) High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet 381(9866):557–64
Dawley JA, Fite KB, Fulk GD (2013) Rehabilitation robotics (ICORR). In: IEEE international conference on Piscataway, EMG control of a bionic knee prosthesis: exploiting muscle co-contractions for improved locomotor function
Dollar AM, Herr H (2008) Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans Robot 24(1):144158
Donath M (1974) Proportional EMG control for above-knee prosthesis
Dzahir MAM, Yamamoto S (2014) Recent trends in lower-limb robotic rehabilitation orthosis: control scheme and strategy for pneumatic muscle actuated gait trainers. Robotics 3(2):120–148
Farris RJ, Quintero HA, Goldfarb M (2011) Preliminary evaluation of a powered lower limb orthosis to aid walking in paraplegic individuals. IEEE Trans Neural Syst Rehabil Eng 19(6):652659
Farris RJ, Quintero HA, Murray SA, Ha KH, Hartigan C, Goldfarb M (2014) A preliminary assessment of legged mobility provided by a lower limb exoskeleton for persons with paraplegia. IEEE Trans Neural Syst Rehabil Eng 22(3):482–490
Fleischer C, Hommel G (2008) A humanexoskeleton interface utilizing electromyography. Robot IEEE Trans 24(4):872–82
Gorsic M, Kamnik R, Ambrozic L, Vitiello N, Lefeber D, Pasquini G, Munih M (2014) Online phase detection using wearable sensors for walking with a robotic prosthesis. Sensors 14:2776–2794
Grood ES, Suntay WJ (1983) A joint coordinate system for the clinical description of three dimensional motions: application to the knee. Trans ASME 105:136–144
Guittet J (1979) The Spartacus telethesis: manipulator control studies. Bull Prosthet Res 16:69–105
Herr H, Wilkenfeld A (2003) User-adaptive control of a magnetorheological prosthetic knee. Ind Robot Int J 30(1):42–55
Herr H (2009) Exoskeletons and orthoses: classification, design challenges and future directions. J Neuroeng Rehabil 6(1):1–9
Hirata Y, Iwano T, Kosuge K (2008) Control of wearable walking helper on slope based on integration of acceleration and GRF information. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 08), Nice, France, September 2008, pp 3731–3736
Hoover CD, Fulk GD, Fite KB (2012) The design and initial experimental validation of an active myoelectric transfemoral prosthesis. J Med Device 6(1):1–12
Huang H, Zhang F, Hargrove LJ, Dou Z, Rogers DR, Englehart KB (2011) Continuous locomotion-mode identification for prosthetic legs based on neuromuscular mechanical fusion. Biomed Eng IEEE Trans 58(10):2867–75
Jarrasse N, Robertson J, Garrec P, Pasqui V, Perrot Y, Roby-Brami A, Wang D, Morel G (2008) Design and acceptability assessment of a new reversible orthosis. In: IEEE international conference on intelligent robots and systems (IROS), France, pp 1933–1939
Jimnez-Fabian R, Verlinden O (2012) Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Med Eng Phys 34(4):397–408
Jung JY et al (2015) A neural network-based gait phase classification method using sensors equipped on lower limb exoskeleton robots. Sensors 15(11):27738–27759
Kajita S et al (2003) Biped walking pattern generation by using preview control of zero-moment point. In: IEEE international conference on proceedings of robotics and automation, ICRA’03, vol 2
Kong K, Tomizuka M (2008) Smooth and continuous human gait phase detection based on foot pressure patterns. In: Proceedings of the 2008 IEEE international conference on robotics and automation, Pasadena, CA, USA, 19–23 May 2008, pp 3678–3683
Mannini A, Sabatini AM (2011) A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope. In: Proceedings of the 2011 annual international conference of the IEEE engineering medicine and biology society, EMBS, Boston, MA, USA, 30 August–3 September 2011, pp 4369–4373
Mannini A, Sabatini AM (2012) Gait phase detection and discrimination between walking-jogging activities using hidden Markov models applied to foot motion data from a gyroscope. Gait Posture 36:657–661
May BJ, Lockard MA (2011) Prosthetics & orthotics in clinical practice: a case study approach, 1st edn. F.A. Davis Company
Meng M, She Q, Gao Y, Luo Z (2010) EMG signals based gait phases recognition using hidden Markov models. In: Proceedings of the 2010 IEEE international conference on information and automation, Harbin, China, 20–23 June 2010, pp 852–856
Mihelj M, Podobnik J (2012) Haptics for virtual reality and teleoperation, chapter 8: stability analysis of haptic interfaces—predictive control for transparent teleoperation under communication time delay robotics, “Intelligent systems, control and automation: science and engineering”
Novak D et al (2013) Automated detection of gait initiation and termination using wearable sensors. Med Eng Phys 35(12):1713–1720
O’Connor J, Goodfellow J (1978) The mechanics of the knee and prosthesis design. Bone Joint J 60:358–369
Orizio C (1993) Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies. Crit Rev Biomed Eng 21(3):201–243
Pappas IPI, Popovic MR, Keller T, Dietz V, Morari M (2001) A reliable gait phase detection system. IEEE Trans Neural Syst Rehabil Eng 9:113–125
Perry J, Burnfield J (2010) Gait analysis normal and pathological function. Slack-Incorporated, Ed., Thorofare
Salini J, Padois V, Ibanez A, Bidaud P, Buendia A (2011) A goal driven perspective to generate humanoid motion synthesis. In: Field robotics proceedings of the 14th international conference on climbing and walking robots and the support technologies for mobile machines. World Scientific, pp 889–897
Sancisi N, Parenti-Castelli V (2011) A new kinematic model of the passive motion of the knee inclusive of the patella. J Mech Robot 3(4):041003
Sancisi N, Baldisserri B, Parenti-Castelli V, Belvedere C, Leaedini A (2014) One-degree-of-freedom spherical model for the passive motion of the human ankle joint. Med Biol Eng Comput 52(4):363–373
Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL (2014) Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 40(1):11–19
Smith PN, Refshauge KM, Scarvell JM (2003) Development of the concepts of knee kinematics. Arch Phys Med Rehabil 84:1895–1902
Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors 12(2):2255–2283
Tucker MR, Olivier J, Pagel A, Bleuler H, Bouri M, Lambercy O, Millań JR, Riener R, Vallery H, Gassert Roger (2015) Control strategies for active lower extremity prosthetics and orthotics: a review. J Neuroeng Rehabil 12(1):1
Wang Z, Jiang M, Hu Y, Li H (2012) An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors. IEEE Trans Inf Technol Biomed 16(4):691–699
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Cai, V.A.D., Ibanez, A., Granata, C. et al. Transparency enhancement for an active knee orthosis by a constraint-free mechanical design and a gait phase detection based predictive control. Meccanica 52, 729–748 (2017). https://doi.org/10.1007/s11012-016-0575-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11012-016-0575-z