In this research we introduce a wearable sensory system for motion intention estimation and control of exoskeleton robot. The system comprises wearable inertial motion sensors and shoe-embedded force sensors. The system utilizes an instrumented cane as a part of the interface between the user and the robot. The cane reflects the motion of upper limbs, and is used in terms of human inter-limb synergies. The developed control system provides assisted motion in coherence with the motion of other unassisted limbs. The system utilizes the instrumented cane together with body worn sensors, and provides assistance for start, stop and continuous walking. We verified the function of the proposed method and the developed wearable system through gait trials on treadmill and on ground. The achievement contributes to finding an intuitive and feasible interface between human and robot through wearable gait sensors for practical use of assistive technology. It also contributes to the technology for cognitively assisted locomotion, which helps the locomotion of physically challenged people.
References
[1]
Sankai, Y. HAL: Hybrid Assistive Limb Based on Cybernics. In Robotics Research; Kaneko, M., Nakamura, Y., Eds.; Springer Berlin/Heidelberg: Berlin, Germany, 2011; Volume 66, pp. 25–34.
[2]
Strausser, K.A.; Kazerooni, H. The Development and Testing of a Human Machine Interface for a Mobile Medical Exoskeleton. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, CA, USA, 25–30 September 2011; pp. 4911–4916.
[3]
Veneman, J.; Kruidhof, R.; Hekman, E.; Ekkelenkamp, R.; van Asseldonk, E.; van der Kooij, H. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 2007, 15, 379–386.
[4]
Kawamoto, H.; Hayashi, T.; Sakurai, T.; Eguchi, K.; Sankai, Y. Development of Single Leg Version of HAL for Hemiplegia. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, MN, USA, 3–6 September 2009; pp. 5038–5043.
[5]
Suzuki, K.; Kawamura, Y.; Hayashi, T.; Sakurai, T.; Hasegawa, Y.; Sankai, Y. Intention-Based Walking Support for Paraplegia Patient. Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, Hawaii, USA, 10–12 October 2005; Volume 3, pp. 2707–2713.
[6]
Husemann, B.; Muller, F.; Krewer, C.; Heller, S.; Koenig, E. Effects of locomotion training with assistance of a robot-driven gait orthosis in hemiparetic patients after stroke: A randomized controlled pilot study. Stroke 2007, 38, 349–354.
[7]
Kawamoto, H.; Taal, S.; Niniss, H.; Hayashi, T.; Kamibayashi, K.; Eguchi, K.; Sankai, Y. Voluntary Motion Support Control of Robot Suit HAL Triggered by Bioelectrical Signal for Hemiplegia. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Buenos Aires, Argentina, 31 August–4 September 2010; pp. 462–466.
[8]
Kubota, S.; Nakata, Y.; Eguchi, K.; Kawamoto, H.; Kamibayashi, K.; Sakane, M.; Sankai, Y.; Ochiai, N. Feasibility of rehabilitation training with a newly developed wearable robot for patients with limited mobility. Arch. Phys. Med. Rehabi. 2013, 94, 1080–1087.
[9]
Kawamoto, H.; Kamibayashi, K.; Nakata, Y.; Yamawaki, K.; Ariyasu, R.; Sankai, Y.; Sakane, M.; Eguchi, K.; Ochiai, N. Pilot study of locomotion improvement using hybrid assistive limb in chronic stroke patients. BMC Neurol. 2013, 13, 141.
[10]
Liu, T.; Inoue, Y.; Shibata, K. A wearable ground reaction force sensor system and its application to the measurement of extrinsic gait variability. Sensors 2010, 10, 10240–10255.
[11]
Schepers, H.M.; van Asseldonk, E.H.; Baten, C.T.; Veltink, P.H. Ambulatory estimation of foot placement during walking using inertial sensors. J. Biomech. 2010, 43, 3138–3143.
[12]
Kong, K.; Tomizuka, M. Smooth and Continuous Human Gait Phase Detection Based on Foot Pressure Patterns. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, USA, 19–23 May 2008; pp. 3678–3683.
[13]
Dejnabadi, H.; Jolles, B.; Casanova, E.; Fua, P.; Aminian, K. Estimation and visualization of sagittal kinematics of lower limbs orientation using body-fixed sensors. IEEE Trans. Biomed. Eng. 2006, 53, 1385–1393.
[14]
Bergmann, J.; Mayagoitia, R.; Smith, I. A portable system for collecting anatomical joint angles during stair ascent: A comparison with an optical tracking device. Dyn. Med. 2009, 8, 3.
[15]
Liu, T.; Inoue, Y.; Shibata, K. Development of a wearable sensor system for quantitative gait analysis. Measurement 2009, 42, 978–988.
[16]
Wannier, T.; Bastiaanse, C.; Colombo, G.; Dietz, V. Arm to leg coordination in humans during walking, creeping and swimming activities. Exp. Brain Res. 2001, 141, 375–379.
[17]
Balter, J.E.; Zehr, E.P. Neural coupling between the arms and legs during rhythmic locomotor-like cycling movement. J. Neurophysiol. 2007, 97, 1809–1818.
[18]
Zehr, E.P.; Balter, J.E.; Ferris, D.P.; Hundza, S.R.; Loadman, P.M.; Stoloff, R.H. Neural regulation of rhythmic arm and leg movement is conserved across human locomotor tasks. J. Physiol. 2007, 582, 209–227.
[19]
Dietz, V.; Fouad, K.; Bastiaanse, C.M. Neuronal coordination of arm and leg movements during human locomotion. Eur. J. Neurosci. 2001, 14, 1906–1914.
[20]
Zehr, E.P.; Duysens, J. Regulation of arm and leg movement during human locomotion. Neuroscientist 2004, 10, 347–361.
[21]
Duysens, J.; de Crommert, H.W.V. Neural control of locomotion; Part 1: The central pattern generator from cats to humans. Gait Post. 1998, 7, 131–141.
[22]
Grillner, S. Control of Locomotion in Bipeds, Tetrapods, and Fish. In Handbook of Physiology, The Nervous System II, Motor Control; American Physiological Society: Bethesda, MD, USA, 1981; Volume 2, pp. 1179–1236.
[23]
Milovanovi, I.; Popovi, D.B. Principal component analysis of gait kinematics data in acute and chronic stroke patients. Comput. Math. Methods Med. 2012, doi:10.1155/2012/649743.
[24]
Stephenson, J.L.; Lamontagne, A.; Serres, S.J.D. The coordination of upper and lower limb movements during gait in healthy and stroke individuals. Gait Post. 2009, 29, 11–16.
[25]
Kuan, T.S.; Tsou, J.Y.; Su, F.C. Hemiplegic gait of stroke patients: The effect of using a cane. Arch. Phys. Med. Rehabil. 1999, 80, 777–784.
[26]
Bateni, H.; Maki, B.E. Assistive devices for balance and mobility: Benefits, demands, and adverse consequences. Arch. Phys. Med. Rehabil. 2005, 86, 134–145.
[27]
Jeka, J.J. Light touch contact as a balance aid. Phys. Ther. 1997, 77, 476–487.
[28]
Boonsinsukh, R.; Panichareon, L.; Phansuwan-Pujito, P. Light touch cue through a cane improves pelvic stability during walking in stroke. Arch. Phys. Med. Rehabil. 2008, 90, 919–926.
[29]
Jang, E.H.; Cho, Y.J.; Chi, S.Y.; Lee, J.Y.; Kang, S.S.; Chun, B.T. Recognition of Walking Intention Using Multiple Bio/Kinesthetic Sensors for Lower Limb Exoskeletons. Proceedings of the 2010 International Conference on Control Automation and Systems (ICCAS), Gyeonggi-do, Korea, 27–30 October 2010; pp. 1802–1805.
[30]
Hassan, M.; Kadone, H.; Suzuki, K.; Sankai, Y. Exoskeleton Robot Control Based on Cane and Body Joint Synergies. Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Algarve, Portugal, 7–12 October 2012; pp. 1609–1614.
[31]
Vallery, H.; Buss, M. Complementary Limb Motion Estimation Based on Interjoint Coordination Using Principal Components Analysis. Proceedings of the 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control Computer Aided Control System Design, Munich, Germany, 4–6 October 2006; pp. 933–938.
[32]
Vallery, H.; van Asseldonk, E.; Buss, M.; van der Kooij, H. Referencetrajectory generation for rehabilitation robots: Complementary limb motion estimation. IEEE Trans. Neural Syst. Rehabil. Eng. 2009, 17, 23–30.
[33]
Rebersek, P.; Novak, D.; Podobnik, J.; Munih, M. Intention Detection during Gait Initiation Using Supervised Learning. Proceedings of the 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Bled, Slovenia, 26–28 October 2011; pp. 34–39.
[34]
Prayudi, I.; Kim, D. Design and Implementation of IMU-Based Human Arm Motion Capture System. Proceedings of the 2012 International Conference on Mechatronics and Automation (ICMA), Chengdu, China, 5–8 August 2012; pp. 670–675.
[35]
Madgwick, S.; Harrison, A.; Vaidyanathan, R. Estimation of IMU and MARG Orientation Using a Gradient Descent Algorithm. Proceedings of the 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), Zurich, Switzerland, 29 June–1 July 2011; pp. 1–7.
[36]
Winter, D.A. Biomechanics and Motor Control of Human Movement; John Wiley & Sons, Inc.: Waterloo, ON, Canada, 2009.