Skip to main content
SpringerLink
Log in

Overview of Computational Intelligence (CI) Techniques for Powered Exoskeletons

  • Chapter
  • First Online:
Computational Intelligence in Sensor Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 776))

Abstract

There is an emerging need to synchronise wearable function with user intention as many exoskeletons reported in current literature have limited capability to predict user intention. In order to achieve good synchronization, closed loop feedback is required. Overcoming these limitations necessitates an architecture composed of networked sensors and actuators with smart control algorithms to fuse sensor data and create smooth actuation. This review chapter discusses the growing need to deploy computational intelligence (CI) techniques as well as machine learning (ML) algorithms so that exoskeletons are able to predict the user intentions and consequently operate in parallel with human intention. A comprehensive review of major portable, active exoskeletons are provided for both upper and lower limbs with a focus on the need for smart algorithms integration to drive them. The application areas include rehabilitation and human performance augmentation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Carpino, G., Accoto, D., Tagliamonte, N.L., Ghilardi, G., Guglielmelli, E.: Lower limb wearable robots for physiological gait restoration: state of the art and motivations. Medic 21, 72–80 (2013)

    Google Scholar 

  2. Shinohara, K., Wobbrock, J.O.: Self-conscious or self-confident? A diary study conceptualizing the social accessibility of assistive technology. ACM Trans. Accessible Comput. 8 (2016)

    Article  Google Scholar 

  3. Radder, B., Kottink, A., van der Vaart, N., Oosting, D., Buurke, J., Nijenhuis, S., Prange, G., Rietman, J.: User-centred input for a wearable soft-robotic glove supporting hand function in daily life. In: IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 502–507. IEEE (2015)

    Google Scholar 

  4. Pons, J.L.: Wearable Robots: Biomechatronic Exoskeletons. Wiley, New York (2008)

    Book  Google Scholar 

  5. Dollar, A.M., Herr, H.: Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans. Robot. 24, 144–158 (2008)

    Article  Google Scholar 

  6. Herr, H.: Exoskeletons and orthoses: classification, design challenges and future directions. J. Neuroeng. Rehabil. 6, 21 (2009)

    Article  Google Scholar 

  7. Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002)

    Article  Google Scholar 

  8. Lai, D.T.H., Palaniswami, M., Begg, R.: Healthcare Sensor Networks: Challenges Toward Practical Implementation. CRC Press, Boca Raton (2011)

    Book  Google Scholar 

  9. Yan, T., Cempini, M., Oddo, C.M., Vitiello, N.: Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Robot. Auton. Syst. 64, 120–136 (2015)

    Article  Google Scholar 

  10. Yagn, N.: Apparatus for facilitating walking. Google Patents (1890)

    Google Scholar 

  11. Dick, G.J., Edwards, E.A.: Human bipedal locomotion device. Google Patents (1991)

    Google Scholar 

  12. Saccares, L., Sarakoglou, I., Tsagarakis, N.G.: iT-Knee: an exoskeleton with ideal torque transmission interface for ergonomic power augmentation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 780–786. IEEE (2016)

    Google Scholar 

  13. Collins, S.H., Wiggin, M.B., Sawicki, G.S.: Reducing the energy cost of human walking using an unpowered exoskeleton. Nature 522, 212–215 (2015)

    Article  Google Scholar 

  14. Van Dijk, W., Van der Kooij, H., Hekman, E.: A passive exoskeleton with artificial tendons: design and experimental evaluation. In: IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–6. IEEE (2011)

    Google Scholar 

  15. Diller, S., Majidi, C., Collins, S.H.: A lightweight, low-power electroadhesive clutch and spring for exoskeleton actuation. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 682–689. IEEE (2016)

    Google Scholar 

  16. Dollar, A.M., Herr, H.: Design of a quasi-passive knee exoskeleton to assist running. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 747–754. IEEE (2008)

    Google Scholar 

  17. Gopura, R.A.R.C., Bandara, D.S.V., Kiguchi, K., Mann, G.K.I.: Developments in hardware systems of active upper-limb exoskeleton robots: a review. Robot. Auton. Syst. 75, 203–220 (2016)

    Article  Google Scholar 

  18. Kuo, A.D.: A mechanical analysis of force distribution between redundant multiple degree-of-freedom actuators in the human: implications for the central nervous system. Hum. Mov. Sci. 13, 635–663 (1994)

    Article  Google Scholar 

  19. Yi, J., Shen, Z., Song, C., Wang, Z.: A soft robotic glove for hand motion assistance. In: IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 111-116. IEEE (2016)

    Google Scholar 

  20. Yun, Y., Agarwal, P., Fox, J., Madden, K.E., Deshpande, A.D.: Accurate torque control of finger joints with UT hand exoskeleton through Bowden cable SEA. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 390–397 (2016)

    Google Scholar 

  21. Nycz, C.J., Btzer, T., Lambercy, O., Arata, J., Fischer, G.S., Gassert, R.: Design and characterization of a lightweight and fully portable remote actuation system for use with a hand exoskeleton. IEEE Robot. Autom. Lett. 1, 976–983 (2016)

    Article  Google Scholar 

  22. Yi, J., Shen, Z., Song, C., Wang, Z.: A soft robotic glove for hand motion assistance. In: 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 111–116 (2016)

    Google Scholar 

  23. de Michiel, P., Looze, T.B., Krause, F., Stadler, K.S., O’Sullivan, L.W.: Exoskeletons for industrial application and their potential effects on physical work load. Ergonomics 59, 671–681 (2016)

    Article  Google Scholar 

  24. Mudie, K.L., Boynton, A.C., Karakolis, T., O’Donovan, M.P., Kanagaki, G.B., Crowell, H.P., Begg, R.K., LaFiandra, M.E., Billing, D. C.: Consensus paper on testing and evaluation of military exoskeletons for the dismounted combatant. Under Review (2017)

    Google Scholar 

  25. Kang, B.B., Lee, H., In, H., Jeong, U., Chung, J., Cho, K. J.: Development of a polymer-based tendon-driven wearable robotic hand. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3750–3755. IEEE (2016)

    Google Scholar 

  26. Ugurlu, B., Nishimura, M., Hyodo, K., Kawanishi, M., Narikiyo, T.: Proof of concept for robot-aided upper limb rehabilitation using disturbance observers. IEEE Trans. Hum. Mach. Syst. 45, 110–118 (2015)

    Article  Google Scholar 

  27. Martinez, F., Retolaza, I., Pujana-Arrese, A., Cenitagoya, A., Basurko, J., Landaluze, J.: Design of a five actuated DoF upper limb exoskeleton oriented to workplace help. In: 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 169-174 (2008)

    Google Scholar 

  28. Toyama, S., Yamamoto, G.: Wearable agrirobot. J. Vibroeng. 12, 287–291 (2010)

    Google Scholar 

  29. Maeshima, S., Osawa, A., Nishio, D., Hirano, Y., Takeda, K., Kigawa, H., Sankai, Y.: Efficacy of a hybrid assistive limb in post-stroke hemiplegic patients: a preliminary report. BMC Neurol. 11, 116 (2011)

    Article  Google Scholar 

  30. 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. 13, 141 (2013)

    Article  Google Scholar 

  31. Watanabe, H., Tanaka, N., Inuta, T., Saitou, H., Yanagi, H.: Locomotion improvement using a hybrid assistive limb in recovery phase stroke patients: a randomized controlled pilot study. Arch. Phys. Med. Rehabil. 95, 2006–2012 (2014)

    Article  Google Scholar 

  32. 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. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 462-466. IEEE (2010)

    Google Scholar 

  33. Suzuki, K., Kawamura, Y., Hayashi, T., Sakurai, T., Hasegawa, Y., Sankai, Y.: Intention-based walking support for paraplegia patient. In: IEEE International Conference on Systems Man and Cybernetics, pp. 2707–2713 (2005)

    Google Scholar 

  34. Sankai, Y.: HAL: hybrid assistive limb based on cybernics. In: Robotics Research, pp. 25–34. Springer, Berlin (2010)

    Google Scholar 

  35. Walsh, C.J., Endo, K., Herr, H.: A quasi-passive leg exoskeleton for load-carrying augmentation. Int. J. Humanoid Rob. 4, 487–506 (2007)

    Article  Google Scholar 

  36. Martin, L.: University of Michigan study suggests soldiers could cover inclined terrain more easily using Lockheed Martins FORTIS K-SRD exoskeleton. Lockheed Martin (2017)

    Google Scholar 

  37. Australian Institute of Health Welfare: Stroke and Its Management in Australia: An Update, 37 edn., Canberra (2013)

    Google Scholar 

  38. Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S.: A survey on robotic devices for upper limb rehabilitation. J. Neuroeng. Rehabil. 11, 3 (2014)

    Article  Google Scholar 

  39. Jarrass, N., Morel, G., Proietti, T., Roby-Brami, A., Crocher, V., Robertson, J., Sahbani, A.: Robotic exoskeletons: a perspective for the rehabilitation of arm coordination in stroke patients. Front. Hum. Neurosci. 8, 947 (2014)

    Google Scholar 

  40. Yun, Y., Agarwal, P., Fox, J., Madden, K.E., Deshpande, A.D.: Accurate torque control of finger joints with UT hand exoskeleton through Bowden cable SEA. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 390–397. IEEE (2016)

    Google Scholar 

  41. Australian Institute of Health and Welfare (AIHW): Disability Support Services: Services Provided Under the National Disability Agreement 2015–16, vol. 140. Canberra (2017)

    Google Scholar 

  42. Popov, D., Gaponov, I., Ryu, J.H.: Portable exoskeleton glove with soft structure for hand assistance. In: Activities of Daily Living. IEEE/ASME Transactions on Mechatronics, vol. 22, issue 2, pp. 865–875 (2017)

    Google Scholar 

  43. Dinh, B.K., Xiloyannis, M., Antuvan, C.W., Cappello, L., Masia, L.: Hierarchical cascade controller for assistance modulation in a soft wearable arm exoskeleton. IEEE Rob. Autom. Lett. 2, 1786–1793 (2017)

    Article  Google Scholar 

  44. Mohammadi, E., Zohoor, H., Khadem, S.M.: Control system design of an active assistive exoskeletal robot for rehabilitation of elbow and wrist. In: Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pp. 834–839. IEEE (2014)

    Google Scholar 

  45. Balasubramanian, S., Ruihua, W., Perez, M., Shepard, B., Koeneman, E., Koeneman, J., Jiping, H.: RUPERT: An exoskeleton robot for assisting rehabilitation of arm functions. Virtual Rehabilitation, IEEE (2008)

    Google Scholar 

  46. Veneman, J.F., Kruidhof, R., Hekman, E.E., Ekkelenkamp, R., Van Asseldonk, E.H., Van Der Kooij, H.: Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 15, 379–386 (2007)

    Article  Google Scholar 

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

    Google Scholar 

  48. Bortole, M., Venkatakrishnan, A., Zhu, F., Moreno, J.C., Francisco, G.E., Pons, J.L., Contreras-Vidal, J.L.: The H2 robotic exoskeleton for gait rehabilitation after stroke: early findings from a clinical study. J. Neuroeng. Rehabil. 12, 54 (2015)

    Article  Google Scholar 

  49. Esquenazi, A., Talaty, M., Packel, A., Saulino, M.: The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am. J. Phys. Med. Rehabil. 91, 911–921 (2012)

    Article  Google Scholar 

  50. Quintero, H.A., Farris, R.J., Goldfarb, M.: A method for the autonomous control of lower limb exoskeletons for persons with paraplegia. J. Med. Devices 6, 041003 (2012)

    Article  Google Scholar 

  51. Agrawal, A., Harib, O., Hereid, A., Finet, S., Masselin, M., Praly, L., Ames, A., Sreenath, K., Grizzle, J.: First steps towards translating HZD control of bipedal robots to decentralized control of exoskeletons. IEEE Access 5, 9919–9934 (2017)

    Article  Google Scholar 

  52. Chu, A., Kazerooni, H., Zoss, A.: On the biomimetic design of the berkeley lower extremity exoskeleton (BLEEX). In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA), pp. 4345–4352. IEEE (2005)

    Google Scholar 

  53. Fukuda, S., De Baets, B.: A short review on the application of computational intelligence and machine learning in the bioenvironmental sciences. In: 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), pp. 106–110. IEEE (2012)

    Google Scholar 

  54. Jung, J.-Y., Heo, W., Yang, H., Park, H.: A neural network-based gait phase classification method using sensors equipped on lower limb exoskeleton robots. Sensors 15, 27738–27759 (2015)

    Article  Google Scholar 

  55. Perry, J., Davids, J.R.: Gait analysis: normal and pathological function. J. Pediatr. Orthop. 12, 815 (1992)

    Article  Google Scholar 

  56. Rushton, D.: Functional electrical stimulation and rehabilitationan hypothesis. Med. Eng. Phys. 25, 75–78 (2003)

    Article  Google Scholar 

  57. Williamson, R., Andrews, B.J.: Gait event detection for FES using accelerometers and supervised machine learning. IEEE Trans. Rehabil. Eng. 8, 312–319 (2000)

    Article  Google Scholar 

  58. Gori, M., Kamnik, R., Ambroi, L., Vitiello, N., Lefeber, D., Pasquini, G., Munih, M.: Online phase detection using wearable sensors for walking with a robotic prosthesis. Sensors 14, 2776–2794 (2014)

    Article  Google Scholar 

  59. Liu, D.-X., Wu, X., Du, W., Wang, C., Xu, T.: Gait phase recognition for lower-limb exoskeleton with only joint angular sensors. Sensors 16, 1579 (2016)

    Article  Google Scholar 

  60. Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10, 1154–1175 (2010)

    Article  Google Scholar 

  61. Rueterbories, J., Spaich, E.G., Larsen, B., Andersen, O.K.: Methods for gait event detection and analysis in ambulatory systems. Med. Eng. Phys. 32, 545–552 (2010)

    Article  Google Scholar 

  62. Begg, R., Kamruzzaman, J.: A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J. Biomech. 38, 401–408 (2005)

    Article  Google Scholar 

  63. O’Connor, C.M., Thorpe, S.K., O’Malley, M.J., Vaughan, C.L.: Automatic detection of gait events using kinematic data. Gait Posture 25, 469–474 (2007)

    Article  Google Scholar 

  64. Hanlon, M., Anderson, R.: Real-time gait event detection using wearable sensors. Gait Posture 30, 523–527 (2009)

    Article  Google Scholar 

  65. Preece, S.J., Kenney, L.P., Major, M.J., Dias, T., Lay, E., Fernandes, B.T.: Automatic identification of gait events using an instrumented sock. J. Neuroeng. Rehabil. 8, 32 (2011)

    Article  Google Scholar 

  66. Tao, W., Liu, T., Zheng, R., Feng, H.: Gait analysis using wearable sensors. Sensors 12, 2255–2283 (2012)

    Google Scholar 

  67. Abaid, N., Cappa, P., Palermo, E., Petrarca, M., Porfiri, M.: Gait detection in children with and without hemiplegia using single-axis wearable gyroscopes. PloS One 8, e73152 (2013)

    Article  Google Scholar 

  68. González, R.C., López, A.M., Rodriguez-Uría, J., Alvarez, D., Alvarez, J.C.: Real-time gait event detection for normal subjects from lower trunk accelerations. Gait Posture 31, 322–325 (2010)

    Article  Google Scholar 

  69. Nogueira, S.L., Siqueira, A.A., Inoue, R.S., Terra, M.H.: Markov jump linear systems-based position estimation for lower limb exoskeletons. Sensors 14, 1835–1849 (2014)

    Article  Google Scholar 

  70. Bamberg, S.J.M., Benbasat, A.Y., Scarborough, D.M., Krebs, D.E., Paradiso, J.A.: Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans. Inf. Technol. Biomed. 12, 413–423 (2008)

    Article  Google Scholar 

  71. Joshi, C.D., Lahiri, U., Thakor, N.V.: Classification of gait phases from lower limb EMG: application to exoskeleton orthosis. In: IEEE Point-of-Care Healthcare Technologies (PHT), pp. 228–231. IEEE (2013)

    Google Scholar 

  72. Li, J., Chen, G., Thangavel, P., Yu, H., Thakor, N., Bezerianos, A., Sun, Y.: A robotic knee exoskeleton for walking assistance and connectivity topology exploration in EEG signal. In: 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 1068–1073. IEEE (2016)

    Google Scholar 

  73. Kawamoto, H., Sankai, Y.: Comfortable power assist control method for walking aid by HAL-3. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4. IEEE (2002)

    Google Scholar 

  74. Lenzi, T., De Rossi, S.M.M., Vitiello, N., Carrozza, M.C.: Intention-based EMG control for powered exoskeletons. IEEE Trans. Biomed. Eng. 59, 2180–2190 (2012)

    Article  Google Scholar 

  75. Fleischer, C., Reinicke, C., Hommel, G.: Predicting the intended motion with EMG signals for an exoskeleton orthosis controller. In: IEEE/RSJ International Conference on Intelligent Robots and System (IROS), pp. 2029–2034. IEEE (2005)

    Google Scholar 

  76. Chen, X., Zeng, Y., Yin, Y.: Improving the transparency of an exoskeleton knee joint based on the understanding of motor intent using energy kernel method of EMG. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 577–588 (2017)

    Article  Google Scholar 

  77. Chen, X., Yin, Y., Fan, Y.: EMG oscillator model-based energy kernel method for characterizing muscle intrinsic property under isometric contraction. Chin. Sci. Bull. 59, 1556–1567 (2014)

    Article  Google Scholar 

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

    Google Scholar 

  79. Biggar, S., Yao, W.: Design and evaluation of a soft and wearable robotic glove for hand rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 1071–1080 (2016)

    Article  Google Scholar 

  80. Wang, S., Wang, L., Meijneke, C., Van Asseldonk, E., Hoellinger, T., Cheron, G., Ivanenko, Y., La Scaleia, V., Sylos-Labini, F., Molinari, M.: Design and control of the MINDWALKER exoskeleton. IEEE Trans. Neural Syst. Rehabil. Eng. 23, 277–286 (2015)

    Article  Google Scholar 

  81. Petersen, T.H., WillerslevOlsen, M., Conway, B.A., Nielsen, J.B.: The motor cortex drives the muscles during walking in human subjects. J. Physiol. 590, 2443–2452 (2012)

    Article  Google Scholar 

  82. Sabatini, A.M.: Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing. Sensors 11, 1489–1525 (2011)

    Article  Google Scholar 

  83. Barbour, N., Schmidt, G.: Inertial sensor technology trends. IEEE Sens. J. 1, 332–339 (2001)

    Article  Google Scholar 

  84. Elliott, G., Marecki, A., Herr, H.: Design of a clutchspring knee exoskeleton for running. J. Med. Devices 8, 031002 (2014)

    Article  Google Scholar 

  85. Beravs, T., Reberek, P., Novak, D., Podobnik, J., Munih, M.: Development and validation of a wearable inertial measurement system for use with lower limb exoskeletons. In: 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 212–217. IEEE (2011)

    Google Scholar 

  86. Nogueira, S.L., Lambrecht, S., Inoue, R.S., Bortole, M., Montagnoli, A.N., Moreno, J.C., Rocon, E., Terra, M.H., Siqueira, A. A., Pons, J.L.: Global Kalman Filter approaches to estimate absolute angles of lower limb segments. Biomed. Eng. Online 16, 58. BioMed. Central (2017)

    Google Scholar 

  87. Taborri, J., Rossi, S., Palermo, E., Patan, F., Cappa, P.: A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network. Sensors 14, 16212–16234 (2014)

    Article  Google Scholar 

  88. Mason, J.E., Traor, I., Woungang, I.: Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force. Springer, Berlin (2016)

    Book  Google Scholar 

  89. Paluszek, M., Thomas, S.: MATLAB Machine Learning. Apress, USA (2017)

    Book  Google Scholar 

  90. Karvanen, J.: The statistical basis of laboratory data normalization. Drug Inf. J. 37, 101–107 (2003)

    Article  Google Scholar 

  91. Chapman, A.D.: Principles and Methods of Data Cleaning. Primary species and species-occurrence data (2005)

    Google Scholar 

  92. Isabelle, G.: Feature Extraction Foundations and Applications. Pattern Recognition. Springer, Berlin (2006)

    Google Scholar 

  93. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)

    MATH  Google Scholar 

  94. Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, Cambridge (2011)

    Book  Google Scholar 

  95. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  96. Guyon, I., Saffari, A., Dror, G., Cawley, G.: Model selection: beyond the bayesian/frequentist divide. J. Mach. Learn. Res. 11, 61–87 (2010)

    MathSciNet  MATH  Google Scholar 

  97. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Cambridge (2016)

    Google Scholar 

  98. Wiering, M., Van Otterlo, M.: Reinforcement learning. Adapt. Learn. Optim. 12 (2012)

    Google Scholar 

  99. Kubat, M.: An Introduction to Machine Learning. Springer, Berlin (2015)

    Book  Google Scholar 

  100. Mannini, A., Sabatini, A.M.: Gait phase detection and discrimination between walkingjogging activities using hidden Markov models applied to foot motion data from a gyroscope. Gait Posture 36, 657–661 (2012)

    Article  Google Scholar 

  101. Salvador, R., Radua, J., Canales-Rodrguez, E.J., Solanes, A., Sarr, S., Goikolea, J.M., Valiente, A., Mont, G.C., del Carmen Natividad, M., Guerrero-Pedraza, A.: Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction. Psychosis PloS One 12, e0175683 (2017)

    Article  Google Scholar 

  102. Dugad, R., Desai, U.B.: A tutorial on hidden Markov models Signal Processing and Artificial Neural Networks Laboratory. Department of Electrical Engineering, Indian Institute of Technology, Bombay Technical Report (1996)

    Google Scholar 

  103. Fink, G.A.: Markov Models for Pattern Recognition: From Theory to Applications. Springer Science & Business Media (2014)

    Chapter  Google Scholar 

  104. Ching, W.-K., Huang, X., Ng, M.K., Siu, T.-K.: Markov Chains Models, Algorithms and Applications, 2nd edn. Springer, New York (2013)

    MATH  Google Scholar 

  105. Jurafsky, D., Martin, J.H.: Speech and Language Processing, vol. 3. Pearson, London (2014)

    Google Scholar 

  106. Yoon, B.-J.: Hidden Markov models and their applications in biological sequence analysis. Curr. Genomics 10, 402–415 (2009)

    Article  MathSciNet  Google Scholar 

  107. Wilson, A.D., Bobick, A.F.: Parametric hidden markov models for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21, 884–900 (1999)

    Article  Google Scholar 

  108. Crea, S., De Rossi, S.M., Donati, M., Reberek, P., Novak, D., Vitiello, N., Lenzi, T., Podobnik, J., Munih, M., Carrozza, M.C.: Development of gait segmentation methods for wearable foot pressure sensors. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5018–5021. IEEE (2012)

    Google Scholar 

  109. Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B., Valenzuela, O.: Human activity recognition based on a sensor weighting hierarchical classifier. Soft Comput. 17, 333–343 (2013)

    Article  Google Scholar 

  110. Chan, A.D., Englehart, K.B.: Continuous myoelectric control for powered prostheses using hidden Markov models. IEEE Trans. Biomed. Eng. 52, 121–124 (2005)

    Article  Google Scholar 

  111. Kim, P.: MATLAB Deep Learning With Machine Learning. Neural Networks and Artificial Intelligence. Springer, Berlin (2017)

    Book  Google Scholar 

  112. Da Silva, I.N., Spatti, D.H., Flauzino, R.A., Liboni, L.H.B., dos Reis Alves, S.F.: Artificial Neural Networks: A Practical Course. Springer, Berlin (2017)

    Book  Google Scholar 

  113. Alotaibi, M., Mahmood, A.: Improved gait recognition based on specialized deep convolutional neural network. Computer Vision and Image Understanding. In: 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE (2015)

    Google Scholar 

  114. McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, New York (2004)

    MATH  Google Scholar 

  115. Lakshmanan, V., Kain, J.S.: A Gaussian mixture model approach to forecast verification. Weather Forecast. 25, 908–920 (2010)

    Article  Google Scholar 

  116. Zhang, M.-H., Cheng, Q.-S.: Gaussian mixture modelling to detect random walks in capital markets. Math. Comput. Model. 38, 503–508 (2003)

    Article  MathSciNet  Google Scholar 

  117. Stepanek, M., Kus, V., Franc, J.: Modification of Gaussian mixture models for data classification in high energy physics. J. Phys. Conf. Ser. 574, 012150 (2015)

    Article  Google Scholar 

  118. Park, S., Mustafa, S.K., Shimada, K.: Learning based robot control with sequential Gaussian process. In: 2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS), pp. 120–127. IEEE (2013)

    Google Scholar 

  119. Allen, F.R., Ambikairajah, E., Lovell, N.H., Celler, B.G.: An adapted Gaussian mixture model approach to accelerometry-based movement classification using time-domain features. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3600–3603. IEEE (2006)

    Google Scholar 

  120. Vögele, A.M., Zsoldos, R.R., Kürger, B., Licka, T.: Novel methods for surface EMG analysis and exploration based on multi-modal gaussian mixture models. PloS One 11, 0157239 (2016)

    Article  Google Scholar 

  121. Papavasileiou, I., Zhang, W., Han, S.: Real-time data-driven gait phase detection using infinite Gaussian mixture model and parallel particle filter. In: IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 302–311. IEEE (2016)

    Google Scholar 

  122. Long, Y., Du, Z.-j., Dong, W., Wang, W.-d.: Human gait trajectory learning using online Gaussian process for assistive lower limb exoskeleton. In: Wearable Sensors and Robots, pp. 165–179. Springer, Berlin (2017)

    Google Scholar 

  123. Siu, H.C., Shah, J.A., Stirling, L.A.: Classification of anticipatory signals for grasp and release from surface electromyography. Sensors 16, 1782 (2016)

    Article  Google Scholar 

  124. Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media (2013)

    Google Scholar 

  125. Le Borgne, H., O’Connor, N.: Natural scene classification and retrieval using Ridgelet-based image signatures. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 116–122. Springer, Berlin (2005)

    Google Scholar 

  126. Begg, R.K., Palaniswami, M., Owen, B.: Support vector machines for automated gait classification. IEEE Trans. Biomed. Eng. 52, 828–838 (2005)

    Article  Google Scholar 

  127. Nakano, T., Nukala, B.T., Zupancic, S., Rodriguez, A., Lie, D.Y., Lopez, J., Nguyen, T.Q.: Gaits classification of normal vs. patients by wireless gait sensor and Support Vector Machine (SVM) classifier. In: IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (2016)

    Google Scholar 

  128. Jee, H., Lee, K., Pan, S.: Eye and face detection using SVM. In: Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, pp. 577–580, IEEE (2004)

    Google Scholar 

  129. Rajnoha, M., Burget, R., Dutta, M.K.: Offline handwritten text recognition using support vector machines. In: 4th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 132–136 (2017)

    Google Scholar 

  130. Cai, C., Han, L., Ji, Z.L., Chen, X., Chen, Y.Z.: SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 31, 3692–3697 (2003)

    Article  Google Scholar 

  131. Liu, X., Zhou, Z., Mai, J., Wang, Q.: Multi-class SVM based real-time recognition of sit-to-stand and stand-to-sit transitions for a bionic knee exoskeleton in transparent mode. In: International Conference on Intelligent Robotics and Applications, pp. 262-272. Springer, Berlin (2017)

    Chapter  Google Scholar 

  132. Nukala, B.T., Shibuya, N., Rodriguez, A., Tsay, J., Lopez, J., Nguyen, T., Zupancic, S., Lie, D.Y.-C.: An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network (ANN) and support vector machine (SVM) algorithms. Open J. Appl. Biosens. 3, 29–39 (2014)

    Article  Google Scholar 

  133. Yoo, J.-H., Hwang, D., Nixon, M.S.: Gender classification in human gait using support vector machine. In: ACIVS, pp. 138–145. Springer, Berlin (2005)

    Chapter  Google Scholar 

  134. Mai, J., Zhang, Z., Wang, Q.: A real-time intent recognition system based on SoC-FPGA for robotic transtibial prosthesis. In: International Conference on Intelligent Robotics and Applications. Springer, pp. 280-289. (2017)

    Chapter  Google Scholar 

Download references

Acknowledgements

The authors gracefully acknowledge the funding of this research by the Defence Science and Technology Group (DSTGroup), Melbourne, Australia.

Author information

Authors and Affiliations

  1. Institute of Sport, Exercise and Active Living (ISEAL), Victoria University, Melbourne, Australia

    Abdelrahman Zaroug, Jasmine K. Proud, Daniel T. H. Lai, Kurt Mudie & Rezaul Begg

  2. College of Engineering and Science, Victoria University, Melbourne, Australia

    Daniel T. H. Lai

  3. Defence Science and Technology Group, Melbourne, Australia

    Dan Billing

Authors
  1. Abdelrahman Zaroug
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jasmine K. Proud
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel T. H. Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kurt Mudie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dan Billing
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rezaul Begg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdelrahman Zaroug .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Silicon Institute of Technology, Bhubaneswar, India

    Bijan Bihari Mishra

  2. Department of Information and Communication, Fakir Mohan University, Balasore, Odisha, India

    Satchidanand Dehuri

  3. Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India

    Bijaya Ketan Panigrahi

  4. Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, India

    Ajit Kumar Nayak

  5. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Bhabani Shankar Prasad Mishra

  6. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Himansu Das

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zaroug, A., Proud, J.K., Lai, D.T.H., Mudie, K., Billing, D., Begg, R. (2019). Overview of Computational Intelligence (CI) Techniques for Powered Exoskeletons. In: Mishra, B., Dehuri, S., Panigrahi, B., Nayak, A., Mishra, B., Das, H. (eds) Computational Intelligence in Sensor Networks. Studies in Computational Intelligence, vol 776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57277-1_15

Download citation

Publish with us

Policies and ethics

Search

Navigation