Overview of Computational Intelligence (CI) Techniques for Powered Exoskeletons

  • Abdelrahman ZarougEmail author
  • Jasmine K. Proud
  • Daniel T. H. Lai
  • Kurt Mudie
  • Dan Billing
  • Rezaul Begg
Part of the Studies in Computational Intelligence book series (SCI, volume 776)


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.


Wearable Robotics Exoskeletons Computational Intelligence Machine Learning Hidden Markov Model Artificial Neural Networks Gaussian Mixture Model Support Vector Machines 



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


  1. 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. 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)CrossRefGoogle Scholar
  3. 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. 4.
    Pons, J.L.: Wearable Robots: Biomechatronic Exoskeletons. Wiley, New York (2008)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Herr, H.: Exoskeletons and orthoses: classification, design challenges and future directions. J. Neuroeng. Rehabil. 6, 21 (2009)CrossRefGoogle Scholar
  7. 7.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002)CrossRefGoogle Scholar
  8. 8.
    Lai, D.T.H., Palaniswami, M., Begg, R.: Healthcare Sensor Networks: Challenges Toward Practical Implementation. CRC Press, Boca Raton (2011)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 10.
    Yagn, N.: Apparatus for facilitating walking. Google Patents (1890)Google Scholar
  11. 11.
    Dick, G.J., Edwards, E.A.: Human bipedal locomotion device. Google Patents (1991)Google Scholar
  12. 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. 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)CrossRefGoogle Scholar
  14. 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. 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. 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. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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. 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. 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)CrossRefGoogle Scholar
  22. 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. 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)CrossRefGoogle Scholar
  24. 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. 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. 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)CrossRefGoogle Scholar
  27. 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. 28.
    Toyama, S., Yamamoto, G.: Wearable agrirobot. J. Vibroeng. 12, 287–291 (2010)Google Scholar
  29. 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)CrossRefGoogle Scholar
  30. 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)CrossRefGoogle Scholar
  31. 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)CrossRefGoogle Scholar
  32. 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. 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. 34.
    Sankai, Y.: HAL: hybrid assistive limb based on cybernics. In: Robotics Research, pp. 25–34. Springer, Berlin (2010)Google Scholar
  35. 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)CrossRefGoogle Scholar
  36. 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. 37.
    Australian Institute of Health Welfare: Stroke and Its Management in Australia: An Update, 37 edn., Canberra (2013)Google Scholar
  38. 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)CrossRefGoogle Scholar
  39. 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. 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. 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. 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. 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)CrossRefGoogle Scholar
  44. 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. 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. 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)CrossRefGoogle Scholar
  47. 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. 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)CrossRefGoogle Scholar
  49. 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)CrossRefGoogle Scholar
  50. 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)CrossRefGoogle Scholar
  51. 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)CrossRefGoogle Scholar
  52. 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. 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. 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)CrossRefGoogle Scholar
  55. 55.
    Perry, J., Davids, J.R.: Gait analysis: normal and pathological function. J. Pediatr. Orthop. 12, 815 (1992)CrossRefGoogle Scholar
  56. 56.
    Rushton, D.: Functional electrical stimulation and rehabilitationan hypothesis. Med. Eng. Phys. 25, 75–78 (2003)CrossRefGoogle Scholar
  57. 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)CrossRefGoogle Scholar
  58. 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)CrossRefGoogle Scholar
  59. 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)CrossRefGoogle Scholar
  60. 60.
    Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10, 1154–1175 (2010)CrossRefGoogle Scholar
  61. 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)CrossRefGoogle Scholar
  62. 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)CrossRefGoogle Scholar
  63. 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)CrossRefGoogle Scholar
  64. 64.
    Hanlon, M., Anderson, R.: Real-time gait event detection using wearable sensors. Gait Posture 30, 523–527 (2009)CrossRefGoogle Scholar
  65. 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)CrossRefGoogle Scholar
  66. 66.
    Tao, W., Liu, T., Zheng, R., Feng, H.: Gait analysis using wearable sensors. Sensors 12, 2255–2283 (2012)Google Scholar
  67. 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)CrossRefGoogle Scholar
  68. 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)CrossRefGoogle Scholar
  69. 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)CrossRefGoogle Scholar
  70. 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)CrossRefGoogle Scholar
  71. 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. 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. 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. 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)CrossRefGoogle Scholar
  75. 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. 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)CrossRefGoogle Scholar
  77. 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)CrossRefGoogle Scholar
  78. 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. 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)CrossRefGoogle Scholar
  80. 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)CrossRefGoogle Scholar
  81. 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)CrossRefGoogle Scholar
  82. 82.
    Sabatini, A.M.: Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing. Sensors 11, 1489–1525 (2011)CrossRefGoogle Scholar
  83. 83.
    Barbour, N., Schmidt, G.: Inertial sensor technology trends. IEEE Sens. J. 1, 332–339 (2001)CrossRefGoogle Scholar
  84. 84.
    Elliott, G., Marecki, A., Herr, H.: Design of a clutchspring knee exoskeleton for running. J. Med. Devices 8, 031002 (2014)CrossRefGoogle Scholar
  85. 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. 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. 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)CrossRefGoogle Scholar
  88. 88.
    Mason, J.E., Traor, I., Woungang, I.: Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force. Springer, Berlin (2016)CrossRefGoogle Scholar
  89. 89.
    Paluszek, M., Thomas, S.: MATLAB Machine Learning. Apress, USA (2017)CrossRefGoogle Scholar
  90. 90.
    Karvanen, J.: The statistical basis of laboratory data normalization. Drug Inf. J. 37, 101–107 (2003)CrossRefGoogle Scholar
  91. 91.
    Chapman, A.D.: Principles and Methods of Data Cleaning. Primary species and species-occurrence data (2005)Google Scholar
  92. 92.
    Isabelle, G.: Feature Extraction Foundations and Applications. Pattern Recognition. Springer, Berlin (2006)Google Scholar
  93. 93.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)zbMATHGoogle Scholar
  94. 94.
    Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, Cambridge (2011)CrossRefGoogle Scholar
  95. 95.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  96. 96.
    Guyon, I., Saffari, A., Dror, G., Cawley, G.: Model selection: beyond the bayesian/frequentist divide. J. Mach. Learn. Res. 11, 61–87 (2010)MathSciNetzbMATHGoogle Scholar
  97. 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. 98.
    Wiering, M., Van Otterlo, M.: Reinforcement learning. Adapt. Learn. Optim. 12 (2012)Google Scholar
  99. 99.
    Kubat, M.: An Introduction to Machine Learning. Springer, Berlin (2015)CrossRefGoogle Scholar
  100. 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)CrossRefGoogle Scholar
  101. 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)CrossRefGoogle Scholar
  102. 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. 103.
    Fink, G.A.: Markov Models for Pattern Recognition: From Theory to Applications. Springer Science & Business Media (2014)CrossRefGoogle Scholar
  104. 104.
    Ching, W.-K., Huang, X., Ng, M.K., Siu, T.-K.: Markov Chains Models, Algorithms and Applications, 2nd edn. Springer, New York (2013)zbMATHGoogle Scholar
  105. 105.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing, vol. 3. Pearson, London (2014)Google Scholar
  106. 106.
    Yoon, B.-J.: Hidden Markov models and their applications in biological sequence analysis. Curr. Genomics 10, 402–415 (2009)MathSciNetCrossRefGoogle Scholar
  107. 107.
    Wilson, A.D., Bobick, A.F.: Parametric hidden markov models for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21, 884–900 (1999)CrossRefGoogle Scholar
  108. 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. 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)CrossRefGoogle Scholar
  110. 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)CrossRefGoogle Scholar
  111. 111.
    Kim, P.: MATLAB Deep Learning With Machine Learning. Neural Networks and Artificial Intelligence. Springer, Berlin (2017)CrossRefGoogle Scholar
  112. 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)CrossRefGoogle Scholar
  113. 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. 114.
    McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, New York (2004)zbMATHGoogle Scholar
  115. 115.
    Lakshmanan, V., Kain, J.S.: A Gaussian mixture model approach to forecast verification. Weather Forecast. 25, 908–920 (2010)CrossRefGoogle Scholar
  116. 116.
    Zhang, M.-H., Cheng, Q.-S.: Gaussian mixture modelling to detect random walks in capital markets. Math. Comput. Model. 38, 503–508 (2003)MathSciNetCrossRefGoogle Scholar
  117. 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)CrossRefGoogle Scholar
  118. 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. 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. 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)CrossRefGoogle Scholar
  121. 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. 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. 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)CrossRefGoogle Scholar
  124. 124.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media (2013)Google Scholar
  125. 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. 126.
    Begg, R.K., Palaniswami, M., Owen, B.: Support vector machines for automated gait classification. IEEE Trans. Biomed. Eng. 52, 828–838 (2005)CrossRefGoogle Scholar
  127. 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. 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. 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. 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)CrossRefGoogle Scholar
  131. 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)CrossRefGoogle Scholar
  132. 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)CrossRefGoogle Scholar
  133. 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)CrossRefGoogle Scholar
  134. 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)CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Abdelrahman Zaroug
    • 1
    Email author
  • Jasmine K. Proud
    • 1
  • Daniel T. H. Lai
    • 1
    • 2
  • Kurt Mudie
    • 1
  • Dan Billing
    • 3
  • Rezaul Begg
    • 1
  1. 1.Institute of Sport, Exercise and Active Living (ISEAL)Victoria UniversityMelbourneAustralia
  2. 2.College of Engineering and ScienceVictoria UniversityMelbourneAustralia
  3. 3.Defence Science and Technology GroupMelbourneAustralia

Personalised recommendations