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Opportunities and challenges in the development of exoskeletons for locomotor assistance

  • Review Article
  • Published:

From Nature Biomedical Engineering

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Abstract

Exoskeletons can augment the performance of unimpaired users and restore movement in individuals with gait impairments. Knowledge of how users interact with wearable devices and of the physiology of locomotion have informed the design of rigid and soft exoskeletons that can specifically target a single joint or a single activity. In this Review, we highlight the main advances of the past two decades in exoskeleton technology and in the development of lower-extremity exoskeletons for locomotor assistance, discuss research needs for such wearable robots and the clinical requirements for exoskeleton-assisted gait rehabilitation, and outline the main clinical challenges and opportunities for exoskeleton technology.

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Fig. 1: Examples of autonomous exoskeletons.
Fig. 2: Timeline of exoskeleton design and function.
Fig. 3: AF versus metabolic improvement of wearable exoskeletons.

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References

  1. Yagn, N. Apparatus for facilitating walking, running, and jumping. US patent 420,179 (1890).

  2. Scholder, C. A. Movement-cure apparatus. US patent 675,678 (1901).

  3. Büdingen, T. Movement-cure apparatus. US patent 964,898 (1910).

  4. Cobb, G. L. Walking motion. US patent 2,010,482 (1935).

  5. Pietro, F. Device for the automatic control of the articulation of the knee applicable to a prosthesis of the thigh. US patent 2,305,291 (1937).

  6. Jansen, J. Phase I Report: DARPA Exoskeleton Program Technical Report January (Oak Ridge National Laboratory, 2004).

  7. Kazerooni, H. & Steger, R. The Berkeley lower extremity exoskeleton. J. Dyn. Syst. Meas. Control 128, 14–25 (2006).

    Article  Google Scholar 

  8. Zoss, A., Kazerooni, H. & Chu, A. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Trans. Mechatron. 11, 128–138 (2006).

    Article  Google Scholar 

  9. Guizzo, E. & Goldstein, H. The rise of the body bots. IEEE Spectr. 42, 50–56 (2005).

    Article  Google Scholar 

  10. Walsh, C., Pasch, K. & Herr, H. An autonomous, underactuated exoskeleton for load-carrying augmentation. In IEEE/RSJ International Conference on Intelligent Robots and Systems 1410–1415 (IEEE, 2006).

  11. Walsh, C. J. et al. Development of a lightweight, underactuated exoskeleton for load-carrying augmentation. In IEEE International Conference on Robotics and Automation 3485–3491 (IEEE, 2006).

  12. Walsh, C. J. Biomimetic Design for an Under-actuated Leg Exoskeleton for Load-carrying Augmentation. PhD thesis, Massachusetts Institute of Technology (2006).

  13. Valiente, A. Design of a Quasi-Passive Parallel Leg Exoskeleton to Augment Load Carrying for Walking. PhD thesis, Massachusetts Institute of Technology (2005).

  14. Walsh, C. J., Endo, K. & Herr, H. A quasi-passive leg exoskeleton for load-carrying augmentation. Int. J. HR 04, 487–506 (2007).

    Google Scholar 

  15. Sawicki, G. S. & Ferris, D. P. Mechanics and energetics of level walking with powered ankle exoskeletons. J. Exp. Biol. 211, 1402–1413 (2008).

    Article  PubMed  Google Scholar 

  16. Gregorczyk, K. N. et al. Effects of a lower-body exoskeleton device on metabolic cost and gait biomechanics during load carriage. Ergonomics 53, 1263–1275 (2010).

    Article  PubMed  Google Scholar 

  17. Colombo, G., Joerg, M., Schreier, R. & Dietz, V. Treadmill training of paraplegic patients using a robotic orthosis. J. Rehabil. Res. Dev. 37, 693–700 (2000).

    CAS  PubMed  Google Scholar 

  18. Hesse, S. & Uhlenbrock, D. A mechanized gait trainer for restoration of gait. J. Rehabil. Res. Dev. 37, 701–708 (2000).

    CAS  PubMed  Google Scholar 

  19. Veneman, J. et al. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 15, 379–386 (2007).

  20. Banala, S. K., Agrawal, S. K. & Scholz, J. P. Active leg exoskeleton (ALEX) for gait rehabilitation of motor-impaired patients. In IEEE 10th International Conference on Rehabilitation Robotics 401–407 (IEEE, 2007).

  21. Banala, S. K., Kim, S. H., Agrawal, S. K. & Scholz, J. P. Robot assisted gait training with active leg exoskeleton (ALEX). IEEE Trans Neural Syst. Rehabil. Eng. 17, 2–8 (2009).

  22. Girone, M., Burdea, G., Bouzit, M., Popescu, V. & Deutsch, J. E. Orthopedic rehabilitation using the ‘rutgers ankle’ interface. Stud. Health Technol. Inform. 70, 89–95 (2000).

    CAS  PubMed  Google Scholar 

  23. Kleim, J. A. & Jones, T. A. Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. J. Speech Lang. Hear. Res. 51, 225–239 (2008).

    Article  Google Scholar 

  24. Hidler, J. et al. Multicenter randomized clinical trial evaluating the effectiveness of the lokomat in subacute stroke. Neurorehabil. Neural Repair 23, 5–13 (2009).

    Article  PubMed  Google Scholar 

  25. Hornby, T. G. et al. Enhanced gait-related improvements after therapist- versus robotic-assisted locomotor training in subjects with chronic stroke: a randomized controlled study. Stroke 39, 1786–1792 (2008).

    Article  PubMed  Google Scholar 

  26. Husemann, B., Mu, F. & Krewer, C. Effects of locomotion training with assistance of a robot-driven gait orthosis in hemiparetic patients after. Stroke 38, 349–354 (2007).

    Article  PubMed  Google Scholar 

  27. Peurala, S. H., Tarkka, I. M., Pitkänen, K. & Sivenius, J. The effectiveness of body weight-supported gait training and floor walking in patients with chronic stroke. Arch. Phys. Med. Rehabil. 86, 1557–1564 (2005).

    Article  PubMed  Google Scholar 

  28. Peurala, S. H. et al. Effects of intensive therapy using gait trainer or floor walking exercises early after stroke. J. Rehabil. Med. 41, 166–173 (2009).

    Article  PubMed  Google Scholar 

  29. Werner, C., von Frankenberg, S., Treig, T., Konrad, M. & Hesse, S. Treadmill training with partial body weight support and an electromechanical gait trainer for restoration of gait in subacute stroke patients: a randomized crossover study. Stroke 33, 2895–2901 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Dobkin, B. H. & Duncan, P. W. Should body weight-supported treadmill training and robotic-assistive steppers for locomotor training trot back to the starting gate? Neurorehabil. Neural Repair 26, 308–317 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ferris, D. P., Czerniecki, J. M. & Hannaford, B. An ankle-foot orthosis powered by artificial pneumatic muscles. J. Appl. Biomech. 21, 189–197 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ferris, D. P., Gordon, K. E., Sawicki, G. S. & Peethambaran, A. An improved powered ankle–foot orthosis using proportional myoelectric control. Gait Posture 23, 425–428 (2006).

    Article  PubMed  Google Scholar 

  33. Hollander, K. W., Ilg, R., Sugar, T. G. & Herring, D. An efficient robotic tendon for gait assistance. J. Biomech. Eng. 128, 788–791 (2006).

    Article  PubMed  Google Scholar 

  34. Gordon, K. E., Sawicki, G. S. & Ferris, D. P. Mechanical performance of artificial pneumatic muscles to power an ankle–foot orthosis. J. Biomech. 39, 1832–1841 (2006).

    Article  PubMed  Google Scholar 

  35. Gordon, K. E. & Ferris, D. P. Learning to walk with a robotic ankle exoskeleton. J. Biomech. 40, 2636–2644 (2007).

    Article  PubMed  Google Scholar 

  36. Beyl, P., Van Damme, M., Van Ham, R., Vanderborght, B. & Lefeber, D. Design and control of a lower limb exoskeleton for robot-assisted gait training. Appl. Bionics Biomech. 6, 229–243 (2009).

    Article  Google Scholar 

  37. Jezernik, S., Jezernik, K. & Morari, M. Impedance Control Based Gait-Pattern Adaptation for a Robotic Rehabilitation Device. IFAC Proc. Volumes 35, 389–393 (2002).

  38. Jezernik, S. & Morari, M. Controlling the human-robot interaction for robotic rehabilitation of locomotion. In Proc. 7th International Workshop on Advanced Motion Control 133–135 (IEEE, 2002).

  39. Jezernik, S., Colombo, G. & Morari, M. Automatic gait-pattern adaptation algorithms for rehabilitation with a 4-DOF robotic orthosis. IEEE Trans. Robot. Autom. 20, 574–582 (2004).

    Article  Google Scholar 

  40. Riener, R. et al. Patient-cooperative strategies for robot-aided treadmill training: first experimental results. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 380–394 (2005).

    Article  PubMed  Google Scholar 

  41. Ekkelenkamp, R., Veltink, P., Stramigioli, S. & van der Kooij, H. Evaluation of a Virtual Model Control for the selective support of gait functions using an exoskeleton. In IEEE 10th International Conference on Rehabilitation Robotics 693–699 (IEEE, 2007).

  42. Emken, J., Bobrow, J. & Reinkensmeyer, D. Robotic movement training as an optimization problem: designing a controller that assists only as needed. In IEEE 9th International Conference on Rehabilitation Robotics 307–312 (IEEE, 2005).

  43. Emken, J. L., Harkema, S. J., Beres-Jones, J. A., Ferreira, C. K. & Reinkensmeyer, D. J. Feasibility of manual teach-and-replay and continuous impedance shaping for robotic locomotor training following spinal cord injury. IEEE Trans. Biomed. Eng. 55, 322–334 (2008).

    Article  PubMed  Google Scholar 

  44. Riener, R. The Cybathlon promotes the development of assistive technology for people with physical disabilities. J. Neuroeng. Rehabil. 13, 49 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  45. 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  CAS  PubMed  PubMed Central  Google Scholar 

  46. 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 

  47. Young, A. J. & Ferris, D. P. State of the art and future directions for lower limb robotic exoskeletons. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 171–182 (2017).

    Article  PubMed  Google Scholar 

  48. 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 

  49. Louie, D. R. & Eng, J. J. Powered robotic exoskeletons in post-stroke rehabilitation of gait: a scoping review. J. Neuroeng. Rehabil. 13, 53 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Pennycott, A., Wyss, D., Vallery, H., Klamroth-Marganska, V. & Riener, R. Towards more effective robotic gait training for stroke rehabilitation: a review. J. Neuroeng. Rehabil. 9, 65 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Lajeunesse, V., Vincent, C., Routhier, F., Careau, E. & Michaud, F. Exoskeletons’ design and usefulness evidence according to a systematic review of lower limb exoskeletons used for functional mobility by people with spinal cord injury. Disabil. Rehabil. Assist. Technol. 11, 535–547 (2016).

    Article  PubMed  Google Scholar 

  52. Lim, D. et al. Development of a lower extremity exoskeleton robot with a quasi-anthropomorphic design approach for load carriage. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 5345–5350 (IEEE, 2015).

  53. Fontana, M., Vertechy, R., Marcheschi, S., Salsedo, F. & Bergamasco, M. The body extender: a full-body exoskeleton for the transport and handling of heavy loads. IEEE Robot. Autom. Mag. 21, 34–44 (2014).

    Article  Google Scholar 

  54. De Looze, M. P., Bosch, T., 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  PubMed  Google Scholar 

  55. Kermavnar, T., de Vries, A. W., de Looze, M. P. & O’Sullivan, L. W. Effects of industrial back-support exoskeletons on body loading and user experience: an updated systematic review. Ergonomics 64, 685–711 (2020).

  56. He, Y., Eguren, D., Luu, T. P. & Contreras-Vidal, J. L. Risk management and regulations for lower limb medical exoskeletons: a review. Med. Devices 10, 89–107 (2017).

    Article  Google Scholar 

  57. Maeshima, S. et al. Efficacy of a hybrid assistive limb in post-stroke hemiplegic patients: a preliminary report. BMC Neurol. 11, 116 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Suzuki, K., Mito, G. & Kawamoto, H. Intention-based walking support for paraplegia patients with robot Suit HAL. Adv. Robot. 21, 1441–1469 (2007).

    Article  Google Scholar 

  59. 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  PubMed  Google Scholar 

  60. Strausser, K. A. & Kazerooni, H. The development and testing of a human machine interface for a mobile medical exoskeleton. In IEEE/RSJ International Conference on Intelligent Robots and Systems 4911–4916 (IEEE, 2011).

  61. Strausser, K. A., Swift, T. A., Zoss, A. B., Kazerooni, H. & Bennett, B. C. Mobile exoskeleton for spinal cord injury: development and testing. In Proc. ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control Vol. 2, 419–425 (ASMEDC, 2011).

  62. Farris, R. J., Quintero, H. A., Withrow, T. J. & Goldfarb, M. Design and simulation of a joint-coupled orthosis for regulating FES-aided gait. In IEEE International Conference on Robotics and Automation 1916–1922 (IEEE, 2009).

  63. Kawamoto, H., Hayashi, T., Sakurai, T., Eguchi, K. & Sankai, Y. Development of single leg version of HAL for hemiplegia. in Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2009, 5038–5043 (2009).

  64. Wang, S. et al. Design and control of the MINDWALKER exoskeleton. IEEE Trans. Neural Syst. Rehabil. Eng. 23, 277–286 (2015).

    Article  PubMed  Google Scholar 

  65. Noda, T. et al. Brain-controlled exoskeleton robot for BMI rehabilitation. In 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012) 21–27 (IEEE, 2012).

  66. Kilicarslan, A., Prasad, S., Grossman, R. G. & Contreras-Vidal, J. L. High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2013, 5606–5609 (2013).

  67. Sawicki, G. S. & Ferris, D. P. Powered ankle exoskeletons reveal the metabolic cost of plantar flexor mechanical work during walking with longer steps at constant step frequency. J. Exp. Biol. 212, 21–31 (2009).

    Article  PubMed  Google Scholar 

  68. Malcolm, P., Derave, W., Galle, S. & De Clercq, D. A simple exoskeleton that assists plantarflexion can reduce the metabolic cost of human walking. PLoS ONE 8, e56137 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Mooney, L. M., Rouse, E. J. & Herr, H. M. Autonomous exoskeleton reduces metabolic cost of human walking during load carriage. J. Neuroeng. Rehabil. 11, 80 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Mooney, L. M., Rouse, E. J. & Herr, H. M. Autonomous exoskeleton reduces metabolic cost of human walking. J. Neuroeng. Rehabil. 11, 151 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Roy, A. et al. Robot-aided neurorehabilitation: a novel robot for ankle rehabilitation. IEEE Trans. Robot. 25, 569–582 (2009).

    Article  Google Scholar 

  72. Blaya, J. & Herr, H. Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait. IEEE Trans. Neural Syst. Rehabil. Eng. 12, 24–31 (2004).

    Article  PubMed  Google Scholar 

  73. Boehler, A. W., Hollander, K. W., Sugar, T. G. & Dosun S. Design, implementation and test results of a robust control method for a powered ankle foot orthosis (AFO). In IEEE International Conference on Robotics and Automation 2025–2030 (IEEE, 2008).

  74. Hitt, J. et al. Dynamically controlled ankle-foot orthosis (DCO) with regenerative kinetics: incrementally attaining user portability. In IEEE International Conference on Robotics and Automation 1541–1546 (IEEE, 2007).

  75. Roy, A., Krebs, H. I., Barton, J. E., Macko, R. F. & Forrester, L. W. Anklebot-assisted locomotor training after stroke: a novel deficit-adjusted control approach. In IEEE International Conference on Robotics and Automation 2175–2182 (IEEE, 2013).

  76. Witte, K. A., Zhang, J., Jackson, R. W. & Collins, S. H. Design of two lightweight, high-bandwidth torque-controlled ankle exoskeletons. In IEEE International Conference on Robotics and Automation 1223–1228 (IEEE, 2015).

  77. Caputo, J. M. & Collins, S. H. A universal ankle–foot prosthesis emulator for human locomotion experiments. J. Biomech. Eng. 136, 035002 (2014).

    Article  PubMed  Google Scholar 

  78. Zhang, J. et al. Human-in-the-loop optimization of exoskeleton assistance during walking. Science 356, 1280–1284 (2017).

    Article  CAS  PubMed  Google Scholar 

  79. Ding, Y. et al. Multi-joint actuation platform for lower extremity soft exosuits. In IEEE International Conference on Robotics and Automation 1327–1334 (IEEE, 2014).

  80. Bae, J. et al. A soft exosuit for patients with stroke: feasibility study with a mobile off-board actuation unit. In IEEE International Conference on Rehabilitation Robotics 131–138 (IEEE, 2015).

  81. Bryan, G. M., Franks, P. W., Klein, S. C., Peuchen, R. J. & Collins, S. H. A hip–knee–ankle exoskeleton emulator for studying gait assistance. Int. J. Robot. Res. 40, 722–746 (2021).

    Article  Google Scholar 

  82. Quinlivan, B. T. et al. Assistance magnitude versus metabolic cost reductions for a tethered multiarticular soft exosuit. Sci. Robot. 2, eaah4416 (2017).

    Article  PubMed  Google Scholar 

  83. Jackson, R. W. & Collins, S. H. An experimental comparison of the relative benefits of work and torque assistance in ankle exoskeletons. J. Appl. Physiol. 119, 541–557 (2015).

    Article  PubMed  Google Scholar 

  84. Grimmer, M. et al. Comparison of the human-exosuit interaction using ankle moment and ankle positive power inspired walking assistance. J. Biomech. 83, 76–84 (2019).

    Article  PubMed  Google Scholar 

  85. Ding, Y., Kim, M., Kuindersma, S. & Walsh, C. J. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking. Sci. Robot. 3, eaar5438 (2018).

    Article  PubMed  Google Scholar 

  86. Hidayah, R. et al. Gait adaptation using a cable-driven active leg exoskeleton (C-ALEX) with post-stroke participants. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 1984–1993 (2020).

    Article  PubMed  Google Scholar 

  87. Mooney, L. M. & Herr, H. M. Biomechanical walking mechanisms underlying the metabolic reduction caused by an autonomous exoskeleton. J. Neuroeng. Rehabil. 13, 4 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Giovacchini, F. et al. A light-weight active orthosis for hip movement assistance. Robot. Auton. Syst. 73, 123–134 (2015).

    Article  Google Scholar 

  89. Kang, I., Hsu, H. & Young, A. J. Design and validation of a torque controllable hip exoskeleton for walking assistance. In ASME 2018 Dynamic Systems and Control Conference https://doi.org/10.1115/DSCC2018-9198 (2018).

  90. Kang, I., Kunapuli, P., Hsu, H. & Young, A. J. Electromyography (EMG) signal contributions in speed and slope estimation using robotic exoskeletons. In IEEE International Conference on Rehabilitation Robotics 548–553 (IEEE, 2019).

  91. Martini, E. et al. Gait training using a robotic hip exoskeleton improves metabolic gait efficiency in the elderly. Sci. Rep. 9, 7157 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Lerner, Z. F., Damiano, D. L., Park, H.-S., Gravunder, A. J. & Bulea, T. C. A robotic exoskeleton for treatment of crouch gait in children with cerebral palsy: design and initial application. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 650–659 (2017).

    Article  PubMed  Google Scholar 

  93. Lerner, Z. F., Damiano, D. L. & Bulea, T. C. A lower-extremity exoskeleton improves knee extension in children with crouch gait from cerebral palsy. Sci. Transl. Med. 9, eaam9145 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Lv, G., Zhu, H. & Gregg, R. D. On the design and control of highly backdrivable lower-limb exoskeletons: a discussion of past and ongoing work. IEEE Control Syst. 38, 88–113 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Sanz-Morere, C. B. et al. A bioinspired control strategy for the CYBERLEGs knee-ankle-foot orthosis: feasibility study with lower-limb amputees. In 7th IEEE International Conference on Biomedical Robotics and Biomechatronics 503–508 (IEEE, 2018).

  96. Shimada, H. et al. The use of positron emission tomography and [18F]fluorodeoxyglucose for functional imaging of muscular activity during exercise with a stride assistance system. IEEE Trans. Neural Syst. Rehabil. Eng. 15, 442–448 (2007).

    Article  PubMed  Google Scholar 

  97. Shimada, H. et al. Effects of an automated stride assistance system on walking parameters and muscular glucose metabolism in elderly adults. Br. J. Sports Med. 42, 622–629 (2008).

    Article  Google Scholar 

  98. Shimada, H. et al. Effects of a robotic walking exercise on walking performance in community-dwelling elderly adults. Geriatr. Gerontol. Int. 9, 372–381 (2009).

    Article  PubMed  Google Scholar 

  99. Seo, K., Hyung, S., Choi, B. K., Lee, Y. & Shim, Y. A new adaptive frequency oscillator for gait assistance. In IEEE International Conference on Robotics and Automation 5565–5571 (IEEE, 2015).

  100. Seo, K., Lee, J., Lee, Y., Ha, T. & Shim, Y. Fully autonomous hip exoskeleton saves metabolic cost of walking. In IEEE International Conference on Robotics and Automation 4628–4635 (IEEE, 2016).

  101. Nagarajan, U., Aguirre-Ollinger, G. & Goswami, A. Integral admittance shaping for exoskeleton control. In IEEE International Conference on Robotics and Automation 5641–5648 (IEEE, 2015).

  102. Nagarajan, U. & Goswami, A. Improved mobility with a neutral, motion-amplifying controller for an experimental exoskeleton. SAE Int. J. Passeng. Cars Mech. Syst. 8, 606–6131 (2015).

  103. Aguirre-Ollinger, G., Nagarajan, U. & Goswami, A. An admittance shaping controller for exoskeleton assistance of the lower extremities. Auton. Robots 40, 701–728 (2016).

    Article  Google Scholar 

  104. Lee, Y. et al. A flexible exoskeleton for hip assistance. In IEEE/RSJ International Conference on Intelligent Robots and Systems 1058–1063 (IEEE, 2017).

  105. Seo, K., Lee, J. & Park, Y. J. Autonomous hip exoskeleton saves metabolic cost of walking uphill. In International Conference on Rehabilitation Robotics 246–251 (IEEE, 2017).

  106. Lee, J. et al. Effects of assistance timing on metabolic cost, assistance power, and gait parameters for a hip-type exoskeleton. In International Conference on Rehabilitation Robotics 498–504 (IEEE, 2017).

  107. Lee, H.-J. et al. A wearable hip assist robot can improve gait function and cardiopulmonary metabolic efficiency in elderly adults. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 1549–1557 (2017).

  108. Lee, S.-H. et al. Gait performance and foot pressure distribution during wearable robot-assisted gait in elderly adults. J. Neuroeng. Rehabil. 14, 123 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Orekhov, G., Fang, Y., Cuddeback, C. F. & Lerner, Z. F. Usability and performance validation of an ultra-lightweight and versatile untethered robotic ankle exoskeleton. J. Neuroeng. Rehabil. 18, 163 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Conner, B., Orekhov, G. & Lerner, Z. Ankle exoskeleton assistance increases six-minute walk test performance in cerebral palsy. IEEE Open J. Eng. Med. Biol. 2, 320–323 (2021).

    Article  PubMed  Google Scholar 

  111. Pour Aji Bishe, S. S., Liebelt, L., Fang, Y. & Lerner, Z. F. A low-profile hip exoskeleton for pathological gait assistance: design and pilot testing. In IEEE International Conference on Robotics and Automation 5461–5466 (2022).

  112. Jayaraman, A. et al. Stride management assist exoskeleton vs functional gait training in stroke. Neurology 92, e263–e273 (2019).

    Article  PubMed  Google Scholar 

  113. Buesing, C. et al. Effects of a wearable exoskeleton stride management assist system (SMA®) on spatiotemporal gait characteristics in individuals after stroke: a randomized controlled trial. J. Neuroeng. Rehabil. 12, 69 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Wehner, M. et al. A lightweight soft exosuit for gait assistance. In IEEE International Conference on Robotics and Automation 3362–3369 (IEEE, 2013).

  115. Lee, G. et al. Reducing the metabolic cost of running with a tethered soft exosuit. Sci. Robot. 2, eaan6708 (2017).

    Article  PubMed  Google Scholar 

  116. Lee, S. et al. Autonomous multi-joint soft exosuit with augmentation-power-based control parameter tuning reduces energy cost of loaded walking. J. Neuroeng. Rehabil. 15, 66 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Kim, J. et al. Reducing the metabolic rate of walking and running with a versatile, portable exosuit. Science 365, 668–672 (2019).

    Article  CAS  PubMed  Google Scholar 

  118. Kim, J. et al. Autonomous and portable soft exosuit for hip extension assistance with online walking and running detection algorithm. In IEEE International Conference on Robotics and Automation 5473–5480 (IEEE, 2018).

  119. Bae, J. et al. A lightweight and efficient portable soft exosuit for paretic ankle assistance in walking after stroke. In IEEE International Conference on Robotics and Automation 2820–2827 (IEEE, 2018).

  120. Awad, L. N. et al. A soft robotic exosuit improves walking in patients after stroke. Sci. Transl. Med. 9, eaai9084 (2017).

    Article  PubMed  Google Scholar 

  121. Awad, L. N. et al. Reducing circumduction and hip hiking during hemiparetic walking through targeted assistance of the paretic limb using a soft robotic exosuit. Am. J. Phys. Med. Rehabil. 96, S157–S164 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  122. Awad, L. N., Esquenazi, A., Francisco, G. E., Nolan, K. J. & Jayaraman, A. The ReWalk ReStoreTM soft robotic exosuit: a multi-site clinical trial of the safety, reliability, and feasibility of exosuit-augmented post-stroke gait rehabilitation. J. Neuroeng. Rehabil. 17, 80 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Schmidt, K. et al. The Myosuit: bi-articular anti-gravity exosuit that reduces hip extensor activity in sitting transfers. Front. Neurorobot. 11, 57 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Haufe, F. L. et al. User-driven walking assistance: first experimental results using the MyoSuit. IEEE Int. Conf. Rehabil. Robot. 2019, 944–949 (2019).

  125. Kwon, J. et al. A soft wearable robotic ankle-foot-orthosis for post-stroke patients. IEEE Robot. Autom. Lett. 4, 2547–2552 (2019).

    Article  Google Scholar 

  126. Murakami, K., John, S. W., Komatsu, M. & Adachi, S. External control of walking direction, using cross-wire mobile assist suit. In IEEE/RSJ International Conference on Intelligent Robots and Systems 1046–1051 (IEEE, 2017).

  127. Di Natali, C. et al. Design and evaluation of a soft assistive lower limb exoskeleton. Robotica 37, 2014–2034 (2019).

    Article  Google Scholar 

  128. Nasiri, R., Ahmadi, A. & Ahmadabadi, M. N. Reducing the energy cost of human running using an unpowered exoskeleton. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 2026–2032 (2018).

    Article  PubMed  Google Scholar 

  129. Cherry, M. S., Kota, S. & Ferris, D. P. An Elastic Exoskeleton for Assisting Human Running. In Proc. ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Vol. 7, 727–738 (ASME, 2009).

  130. Cherry, M. S., Kota, S., Young, A. & Ferris, D. P. Running with an elastic lower limb exoskeleton. J. Appl. Biomech. 32, 269–277 (2016).

    Article  PubMed  Google Scholar 

  131. Yandell, M. B., Tacca, J. R. & Zelik, K. E. Design of a low profile, unpowered ankle exoskeleton that fits under clothes: overcoming practical barriers to widespread societal adoption. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 712–723 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Panizzolo, F. A., Bolgiani, C., Di Liddo, L., Annese, E. & Marcolin, G. Reducing the energy cost of walking in older adults using a passive hip flexion device. J. Neuroeng. Rehabil. 16, 117 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  133. Nuckols, R. W., Dick, T. J. M., Beck, O. N. & Sawicki, G. S. Ultrasound imaging links soleus muscle neuromechanics and energetics during human walking with elastic ankle exoskeletons. Sci. Rep. 10, 3604 (2020).

  134. Sawicki, G. S., Beck, O. N., Kang, I. & Young, A. J. The exoskeleton expansion: improving walking and running economy. J. Neuroeng. Rehabil. 17, 25 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Lim, B. et al. Delayed output feedback control for gait assistance and resistance using a robotic exoskeleton. IEEE Robot. Autom. Lett. 4, 3521–3528 (2019).

    Article  Google Scholar 

  136. Browning, R. C., Modica, J. R., Kram, R. & Goswami, A. The effects of adding mass to the legs on the energetics and biomechanics of walking. Med. Sci. Sports Exerc. 39, 515–525 (2007).

    Article  PubMed  Google Scholar 

  137. Koller, J. R., Jacobs, D. A., Ferris, D. P. & Remy, C. D. Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton. J. Neuroeng. Rehabil. 12, 97 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  138. Galle, S., Malcolm, P., Collins, S. H. & De Clercq, D. Reducing the metabolic cost of walking with an ankle exoskeleton: interaction between actuation timing and power. J. Neuroeng. Rehabil. 14, 35 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  139. Khazoom, C. et al. Design and control of a multifunctional ankle exoskeleton powered by magnetorheological actuators to assist walking, jumping, and landing. IEEE Robot. Autom. Lett. 4, 3083–3090 (2019).

    Article  Google Scholar 

  140. Ding, Y. et al. Effect of timing of hip extension assistance during loaded walking with a soft exosuit. J. Neuroeng. Rehabil. 13, 87 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Ding, Y. et al. Biomechanical and physiological evaluation of multi-joint assistance with soft exosuits. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 119–130 (2017).

    Article  PubMed  Google Scholar 

  142. Panizzolo, F. A. et al. Metabolic cost adaptations during training with a soft exosuit assisting the hip joint. Sci. Rep. 9, 9779 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  143. Donelan, J. M. et al. Biomechanical energy harvesting: generating electricity during walking with minimal user effort. Science 319, 807–810 (2008).

    Article  CAS  PubMed  Google Scholar 

  144. van Dijk, W., van der Kooij, H. & Hekman, E. A passive exoskeleton with artificial tendons: design and experimental evaluation. IEEE Int. Conf. Rehabil. Robot. 2011, 5975470 (2011).

  145. Panizzolo, F. A. et al. A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking. J. Neuroeng. Rehabil. 13, 43 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  146. Zelik, K. E., Huang, T.-W. P., Adamczyk, P. G. & Kuo, A. D. The role of series ankle elasticity in bipedal walking. J. Theor. Biol. 346, 75–85 (2014).

    Article  PubMed  Google Scholar 

  147. Robertson, B. D., Farris, D. J. & Sawicki, G. S. More is not always better: modeling the effects of elastic exoskeleton compliance on underlying ankle muscle–tendon dynamics. Bioinsp. Biomim. 9, 046018 (2014).

    Article  PubMed  Google Scholar 

  148. Nuckols, R. W. & Sawicki, G. S. Impact of elastic ankle exoskeleton stiffness on neuromechanics and energetics of human walking across multiple speeds. J. Neuroeng. Rehabil. 17, 75 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  149. Donelan, J. M., Kram, R. & Kuo, A. D. Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking. J. Exp. Biol. 205, 3717–3727 (2002).

    Article  PubMed  Google Scholar 

  150. Wong, J. D. & Donelan, J. M. in Humanoid Robotics: A Reference (eds Goswami, A. & Vadakkepat, P.) 1–28 (Springer, 2017).

  151. Sugar, T. G. et al. Limit cycles to enhance human performance based on phase oscillators. J. Mech. Robot. 7, 011001 (2015).

    Article  Google Scholar 

  152. Uchida, T. K., Hicks, J. L., Dembia, C. L. & Delp, S. L. Stretching your energetic budget: how tendon compliance affects the metabolic cost of running. PLoS ONE 11, e0150378 (2016).

  153. Dembia, C. L., Silder, A., Uchida, T. K., Hicks, J. L. & Delp, S. L. Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads. PLoS ONE 12, e0180320 (2017).

  154. Slade, P., Troutman, R., Kochenderfer, M. J., Collins, S. H. & Delp, S. L. Rapid energy expenditure estimation for ankle assisted and inclined loaded walking. J. Neuroeng. Rehabil. 16, 67 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  155. Durandau, G., Rampeltshammer, W. F., van der Kooij, H. & Sartori, M. Neuromechanical model-based adaptive control of bilateral ankle exoskeletons: biological joint torque and electromyogram reduction across walking conditions. IEEE Trans. Robot. 38, 1380–1394 (2022).

    Article  Google Scholar 

  156. Farris, D. J., Hicks, J. L., Delp, S. L. & Sawicki, G. S. Musculoskeletal modelling deconstructs the paradoxical effects of elastic ankle exoskeletons on plantar-flexor mechanics and energetics during hopping. J. Exp. Biol. 217, 4018–4028 (2014).

    PubMed  PubMed Central  Google Scholar 

  157. See, P. A. & de Leon, R. D. Robotic loading during treadmill training enhances locomotor recovery in rats spinally transected as neonates. J. Neurophysiol. 110, 760–767 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  158. Kolakowsky-Hayner, S. A., Crew, J., Moran, S. & Shah, A. Safety and feasibility of using the EksoTM bionic exoskeleton to aid ambulation after spinal cord injury. J. Spine S4, 003 (2013).

  159. Kozlowski, A., Bryce, T. & Dijkers, M. Time and effort required by persons with spinal cord injury to learn to use a powered exoskeleton for assisted walking. Top. Spinal Cord Inj. Rehabil. 21, 110–121 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  160. Sale, P. et al. Effects on mobility training and de-adaptations in subjects with spinal cord injury due to a wearable robot: a preliminary report. BMC Neurol. 16, 12 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  161. Tefertiller, C. et al. Initial outcomes from a multicenter study utilizing the indego powered exoskeleton in spinal cord injury. Top. Spinal Cord Inj. Rehabil. 24, 78–85 (2018).

    Article  PubMed  Google Scholar 

  162. Kozlowski, A. J., Fabian, M., Lad, D. & Delgado, A. D. Feasibility and safety of a powered exoskeleton for assisted walking for persons with multiple sclerosis: a single-group preliminary study. Arch. Phys. Med. Rehabil. 98, 1300–1307 (2017).

    Article  PubMed  Google Scholar 

  163. Nilsson, A. et al. Gait training early after stroke with a new exoskeleton – the hybrid assistive limb: a study of safety and feasibility. J. Neuroeng. Rehabil. 11, 92 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  164. Shin, S. Y. et al. Soft robotic exosuit augmented high intensity gait training on stroke survivors: a pilot study. J. Neuroeng. Rehabil. 19, 51 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  165. Kressler, J. et al. Understanding therapeutic benefits of overground bionic ambulation: exploratory case series in persons with chronic, complete spinal cord injury. Arch. Phys. Med. Rehabil. 95, 1878–1887 (2014).

    Article  PubMed  Google Scholar 

  166. Saunders, D. H. et al. Physical fitness training for stroke patients. Cochrane Database Syst. Rev. 2020, CD003316 (2020).

  167. Mehrholz, J., Kugler, J. & Pohl, M. Locomotor training for walking after spinal cord injury. Cochrane Database Syst. Rev. 2012, CD006676 (2012).

  168. Chang, J. L. et al. Intensive seated robotic training of the ankle in patients with chronic stroke differentially improves gait. NeuroRehabilitation 41, 61–68 (2017).

  169. Darrah, J., Hickman, R., O’Donnell, M., Vogtle, L. & Wiart, L. AACPDM Methodology to Develop Systematic Reviews of Treatment Interventions (Revision 1.2) (AACPDM, 2008); https://www.aacpdm.org/UserFiles/file/systematic-review-methodology.pdf

  170. Howick, J. et al. The 2011 Oxford Levels of Evidence (Centre for Evidence-Based Medicine, 2011); http://www.cebm.net/index.aspx?o=5653

  171. Ochi, M., Wada, F., Saeki, S. & Hachisuka, K. Gait training in subacute non-ambulatory stroke patients using a full weight-bearing gait-assistance robot: a prospective, randomized, open, blinded-endpoint trial. J. Neurol. Sci. 353, 130–136 (2015).

    Article  PubMed  Google Scholar 

  172. Stein, J., Bishop, L., Stein, D. J. & Wong, C. K. Gait training with a robotic leg brace after stroke. Am. J. Phys. Med. Rehabil. 93, 987–994 (2014).

    Article  PubMed  Google Scholar 

  173. Taveggia, G., Borboni, A., Mule, C., Villafañe, J. H. & Negrini, S. Conflicting results of robot-assisted versus usual gait training during postacute rehabilitation of stroke patients. Int. J. Rehabil. Res. 39, 29–35 (2016).

    Article  PubMed  Google Scholar 

  174. 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  PubMed  Google Scholar 

  175. Bang, D.-H. & Shin, W.-S. Effects of robot-assisted gait training on spatiotemporal gait parameters and balance in patients with chronic stroke: a randomized controlled pilot trial. NeuroRehabilitation 38, 343–349 (2016).

    Article  PubMed  Google Scholar 

  176. Yeung, L.-f et al. Randomized controlled trial of robot-assisted gait training with dorsiflexion assistance on chronic stroke patients wearing ankle-foot-orthosis. J. Neuroeng. Rehabil. 15, 51 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  177. Gandolfi, M. et al. Robot-assisted vs. sensory integration training in treating gait and balance dysfunctions in patients with multiple sclerosis: a randomized controlled trial. Front. Hum. Neurosci. 8, 318 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  178. Straudi, S. et al. Does robot-assisted gait training ameliorate gait abnormalities in multiple sclerosis? A pilot randomized-control trial. NeuroRehabilitation 33, 555–563 (2013).

    Article  CAS  PubMed  Google Scholar 

  179. Esquenazi, A., Lee, S., Packel, A. T. & Braitman, L. A randomized comparative study of manually assisted versus robotic-assisted body weight supported treadmill training in persons with a traumatic brain injury. PM R 5, 280–290 (2013).

    Article  PubMed  Google Scholar 

  180. Hornby, T. G. et al. Clinical practice guideline to improve locomotor function following chronic stroke, incomplete spinal cord injury, and brain injury. J. Neurol. Phys. Ther. 44, 49–100 (2020).

    Article  PubMed  Google Scholar 

  181. Dobkin, B. H. Progressive staging of pilot studies to improve phase iii trials for motor interventions. Neurorehabil. Neural Repair 23, 197–206 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  182. Lo, A. C. Clinical designs of recent robot rehabilitation trials. Am. J. Phys. Med. Rehabil. 91, S204–S216 (2012).

    Article  PubMed  Google Scholar 

  183. Winstein, C. J. et al. Guidelines for adult stroke rehabilitation and recovery: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 47, e98–e169 (2016).

    Article  PubMed  Google Scholar 

  184. Morone, G. et al. Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics. Neuropsychiatr. Dis. Treat. 13, 1303–1311 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  185. Mehrholz, J. et al. Electromechanical-assisted training for walking after stroke. Cochrane Database Syst. Rev. 5, CD006185 (2017).

    PubMed  Google Scholar 

  186. Field-Fote, E. E. Lessons from COVID-19 on the stepwise development of interventions. J. Neurol. Phys. Ther. 44, 177–178 (2020).

    Article  PubMed  Google Scholar 

  187. Bowden, M. G. et al. Advancing measurement of locomotor rehabilitation outcomes to optimize interventions and differentiate between recovery versus compensation. J. Neurol. Phys. Ther. 36, 38–44 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  188. Micera, S., Caleo, M., Chisari, C., Hummel, F. C. & Pedrocchi, A. Advanced neurotechnologies for the restoration of motor function. Neuron 105, 604–620 (2020).

    Article  CAS  PubMed  Google Scholar 

  189. Dobkin, B. H. & Martinez, C. Wearable sensors to monitor, enable feedback, and measure outcomes of activity and practice. Curr. Neurol. Neurosci. Rep. 18, 87 (2018).

    Article  PubMed  Google Scholar 

  190. Channa, A., Popescu, N. & Ciobanu, V. Wearable solutions for patients with Parkinson’s disease and neurocognitive disorder: a systematic review. Sensors 20, 2713 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  191. Contreras-Vidal, J. L. et al. Neural decoding of robot-assisted gait during rehabilitation after stroke. Am. J. Phys. Med. Rehabil. 97, 541–550 (2018).

    Article  PubMed  Google Scholar 

  192. Malcolm, P. et al. Varying negative work assistance at the ankle with a soft exosuit during loaded walking. J. Neuroeng. Rehabil. 14, 62 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  193. Lee, S. et al. Controlling negative and positive power at the ankle with a soft exosuit. In IEEE International Conference on Robotics and Automation 3509–3515 (IEEE, 2016).

  194. Seel, T., Schauer, T. & Raisch, J. Joint axis and position estimation from inertial measurement data by exploiting kinematic constraints. In IEEE International Conference on Control Applications 45–49 (2012).

  195. Seel, T., Raisch, J. & Schauer, T. IMU-based joint angle measurement for gait analysis. Sensors 14, 6891–6909 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  196. Laidig, D., Schauer, T. & Seel, T. Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors. In International Conference on Rehabilitation Robotics 971–976 (IEEE, 2017).

  197. Picerno, P. 25 years of lower limb joint kinematics by using inertial and magnetic sensors: a review of methodological approaches. Gait Posture 51, 239–246 (2017).

    Article  PubMed  Google Scholar 

  198. Gregg, R. D., Rouse, E. J., Hargrove, L. J. & Sensinger, J. W. Evidence for a time-invariant phase variable in human ankle control. PLoS ONE 9, e89163 (2014).

  199. Quintero, D., Villarreal, D. J. & Gregg, R. D. Preliminary experiments with a unified controller for a powered knee-ankle prosthetic leg across walking speeds. In IEEE/RSJ International Conference on Intelligent Robots and Systems 5427–5433 (IEEE, 2016).

  200. Lee, Y. et al. A flexible exoskeleton for hip assistance. In IEEE/RSJ International Conference on Intelligent Robots and Systems 1058–1063 (IEEE, 2017).

  201. Kang, I., Kunapuli, P. & Young, A. J. Real-time neural network-based gait phase estimation using a robotic hip exoskeleton. IEEE Trans. Med. Robot. Bionics 2, 28–37 (2020).

    Article  Google Scholar 

  202. Kang, I. et al. Real-time gait phase estimation for robotic hip exoskeleton control during multimodal locomotion. IEEE Robot. Autom. Lett. 6, 3491–3497 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  203. Shepherd, M. K., Molinaro, D. D., Sawicki, G. S. & Young, A. J. Deep learning enables exoboot control to augment variable-speed walking. IEEE Robot. Autom. Lett. 7, 3571–3577 (2022).

    Article  Google Scholar 

  204. Al-dabbagh, A. H. & Ronsse, R. A review of terrain detection systems for applications in locomotion assistance. Robot. Auton. Syst. 133, 103628 (2020).

    Article  Google Scholar 

  205. Kang, I., Molinaro, D. D., Choi, G., Camargo, J. & Young, A. J. Subject-independent continuous locomotion mode classification for robotic hip exoskeleton applications. IEEE Trans. Biomed. Eng. 69, 3234–3242 (2022).

    Article  PubMed  Google Scholar 

  206. Novak, D. et al. Automated detection of gait initiation and termination using wearable sensors. Med. Eng. Phys. 35, 1713–1720 (2013).

    Article  PubMed  Google Scholar 

  207. Takahashi, K. Z., Lewek, M. D. & Sawicki, G. S. A neuromechanics-based powered ankle exoskeleton to assist walking post-stroke: a feasibility study. J. Neuroeng. Rehabil. 12, 23 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  208. McCain, E. M. et al. Mechanics and energetics of post-stroke walking aided by a powered ankle exoskeleton with speed-adaptive myoelectric control. J. Neuroeng. Rehabil. 16, 57 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  209. Hariharan, H. et al. in Medical Imaging 2016: Ultrasonic Imaging and Tomography (eds Duric, N. & Heyde, B.) 97901Q (SPIE, 2016).

  210. Nuckols, R. W. et al. Automated detection of soleus concentric contraction in variable gait conditions for improved exosuit control. In IEEE International Conference on Robotics and Automation 4855–4862 (IEEE, 2020).

  211. Tariq, M., Trivailo, P. M. & Simic, M. EEG-based BCI control schemes for lower-limb assistive-robots. Front. Hum. Neurosci. 12, 312 (2018).

  212. Popovic, M., Keller, T., Papas, I., Dietz, V. & Morari, M. Surface-stimulation technology for grasping and walking neuroprostheses. IEEE Eng. Med. Biol. Mag. 20, 82–93 (2001).

    Article  CAS  PubMed  Google Scholar 

  213. Thrasher, T. & Popovic, M. Functional electrical stimulation of walking: function, exercise and rehabilitation. Ann. Readapt. Med. Phys. 51, 452–460 (2008).

    Article  CAS  PubMed  Google Scholar 

  214. Sartori, M. & Sawicki, G. Closing the loop between wearable technology and human biology: a new paradigm for steering neuromuscular form and function. Prog. Biomed. Eng. 3, 023001 (2021).

  215. Khan, A. S. et al. Retraining walking over ground in a powered exoskeleton after spinal cord injury: a prospective cohort study to examine functional gains and neuroplasticity. J. Neuroeng. Rehabil. 16, 145 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  216. del-Ama, A. J. et al. Review of hybrid exoskeletons to restore gait following spinal cord injury. J. Rehabil. Res. Dev. 49, 497 (2012).

    Article  PubMed  Google Scholar 

  217. del-Ama, A. J., Gil-Agudo, A. ́, Pons, J. L. & Moreno, J. C. Hybrid FES-robot cooperative control of ambulatory gait rehabilitation exoskeleton. J. Neuroeng. Rehabil. 11, 27 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  218. Sanchez-Villamañan, M. D. C., Gonzalez-Vargas, J., Torricelli, D., Moreno, J. C. & Pons, J. L. Compliant lower limb exoskeletons: a comprehensive review on mechanical design principles. J. Neuroeng. Rehabil. 16, 55 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  219. Kitatani, R. et al. Reduction in energy expenditure during walking using an automated stride assistance device in healthy young adults. Arch. Phys. Med. Rehabil. 95, 2128–2133 (2014).

    Article  PubMed  Google Scholar 

  220. Azocar, A. F. & Rouse, E. J. Characterization of open-loop impedance control and efficiency in wearable robots. IEEE Robot. Autom. Lett. 7, 4313–4320 (2022).

    Article  Google Scholar 

  221. Allen, D. P. et al. Towards an ankle-foot orthosis powered by a dielectric elastomer actuator. Mechatronics 76, 102551 (2021).

    Article  Google Scholar 

  222. Thalman, C. M. & Lee, H. Design and validation of a soft robotic ankle-foot orthosis (SR-AFO) exosuit for inversion and eversion ankle support. In IEEE International Conference on Robotics and Automation 1735–1741 (2020).

  223. Thalman, C. M., Member, S., Hertzell, T., Debeurre, M. & Lee, H. The Multi-material Actuator for Variable Stiffness (MAVS): design, modeling, and characterization of a soft actuator for lateral ankle support. In IEEE/RSJ International Conference on Intelligent Robots and Systems 8694–8700 (2020).

  224. Elevate ski exoskeleton. Roam Robotics https://www.roamrobotics.com/ski (2019).

  225. 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 682–689 (IEEE, 2016).

  226. Diller, S. B., Collins, S. H. & Majidi, C. The effects of electroadhesive clutch design parameters on performance characteristics. J. Intell. Mater. Syst. Struct. 29, 3804–3828 (2018).

    Article  Google Scholar 

  227. Agarwal, P., Kuo, P.-H., Neptune, R. R. & Deshpande, A. D. A novel framework for virtual prototyping of rehabilitation exoskeletons. IEEE Int. Conf. Rehabil. Robot. 2013, 6650382 (2013).

  228. Uchida, T. K. et al. Simulating ideal assistive devices to reduce the metabolic cost of running. PLoS ONE 11, e0163417 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  229. Krebs, H. I. et al. HHS public access. Stroke 45, 200–204 (2014).

    Article  PubMed  Google Scholar 

  230. Kanzler, C. M., Lamers, I., Feys, P., Gassert, R. & Lambercy, O. Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning. Med. Biol. Eng. Comput. 60, 249–261 (2022).

    Article  PubMed  Google Scholar 

  231. Mulroy, S., Gronley, J. A., Weiss, W., Newsam, C. & Perry, J. Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke. Gait Posture 18, 114–125 (2003).

    Article  PubMed  Google Scholar 

  232. Filli, L. et al. Profiling walking dysfunction in multiple sclerosis: characterisation, classification and progression over time. Sci. Rep. 8, 4984 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  233. Mannini, A., Trojaniello, D., Cereatti, A. & Sabatini, A. A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and Huntington’s disease patients. Sensors 16, 134 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  234. Ferrante, S. et al. A biofeedback cycling training to improve locomotion: a case series study based on gait pattern classification of 153 chronic stroke patients. J. Neuroeng. Rehabil. 8, 47 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  235. Martin, J. A. et al. Gauging force by tapping tendons. Nat. Commun. 9, 1592 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  236. Jin, Y. et al. Soft sensing shirt for shoulder kinematics estimation. In IEEE International Conference on Robotics and Automation 4863–4869 (IEEE, 2020).

  237. Slade, P., Kochenderfer, M. J., Delp, S. L. & Collins, S. H. Personalizing exoskeleton assistance while walking in the real world. Nature 610, 277–282 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  238. Fickey, S. N., Browne, M. G. & Franz, J. R. Biomechanical effects of augmented ankle power output during human walking. J. Exp. Biol. 221, jeb182113 (2018).

  239. Molinaro, D. D., Kang, I., Camargo, J., Gombolay, M. C. & Young, A. J. Subject-independent, biological hip moment estimation during multimodal overground ambulation using deep learning. IEEE Trans. Med. Robot. Bionics 4, 219–229 (2022).

    Article  Google Scholar 

  240. Kanik, M. et al. Strain-programmable fiber-based artificial muscle. Science 365, 145–150 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  241. Zhang, X. et al. Enhancing gait assistance control robustness of a hip exosuit by means of machine learning. IEEE Robot. Autom. Lett. 7, 7566–7573 (2022).

    Article  Google Scholar 

  242. Medrano, R. L., Thomas, G. C. & Rouse, E. J. Can humans perceive the metabolic benefit provided by augmentative exoskeletons? J. Neuroeng. Rehabil. 19, 26 (2022).

  243. Ingraham, K. A., Remy, C. D. & Rouse, E. J. The role of user preference in the customized control of robotic exoskeletons. Sci. Robot. 7, eabj3487 (2022).

  244. George Hornby, T. Rethinking the tools in the toolbox. J. Neuroeng. Rehabil. 19, 61 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  245. Labruyère, R. Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not? J. Neuroeng. Rehabil. 19, 58 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  246. Klingmann, I. et al. EUPATI and patients in medicines research and development: guidance for patient involvement in ethical review of clinical trials. Front. Med. 5, 251 (2018).

    Article  Google Scholar 

  247. Torricelli, D. et al. Benchmarking wearable robots: challenges and recommendations from functional, user experience, and methodological perspectives. Front. Robot. AI 7, 561774 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  248. Ármannsdóttir, A. L. et al. Assessing the involvement of users during development of lower limb wearable robotic exoskeletons: a survey study. Hum. Factors 62, 351–364 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  249. Pinto-Fernandez, D. et al. Performance evaluation of lower limb exoskeletons: a systematic review. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 1573–1583 (2020).

    Article  PubMed  Google Scholar 

  250. Koller, J. R., Gates, D. H., Ferris, D. P. & Remy, C. D. 'Body-in-the-loop’ optimization of assistive robotic devices: a validation study. Robot. Sci. Syst. https://doi.org/10.15607/RSS.2016.XII.007 (2016).

  251. Siviy, C. et al. Offline assistance optimization of a soft exosuit for augmenting ankle power of stroke survivors during walking. IEEE Robot. Autom. Lett. 5, 828–835 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  252. Dimyan, M. A. et al. Baseline predictors of response to repetitive task practice in chronic stroke. Neurorehabil. Neural Repair 36, 426–436 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  253. Nesler, C., Thomas, G., Divekar, N., Rouse, E. J. & Gregg, R. D. Enhancing voluntary motion with modular, backdrivable, powered hip and knee orthoses. IEEE Robot. Autom. Lett. 7, 6155–6162 (2022).

    Article  PubMed  Google Scholar 

  254. Shorter, A. L. et al. Characterization and clinical implications of ankle impedance during walking in chronic stroke. Sci. Rep. 11, 16726 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  255. Schweighofer, N. et al. Dissociating motor learning from recovery in exoskeleton training post-stroke. J. Neuroeng. Rehabil. 15, 89 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  256. Reisman, D. S., Wityk, R., Silver, K. & Bastian, A. J. Locomotor adaptation on a split-belt treadmill can improve walking symmetry post-stroke. Brain 130, 1861–1872 (2007).

    Article  PubMed  Google Scholar 

  257. Reisman, D. S., McLean, H., Keller, J., Danks, K. A. & Bastian, A. J. Repeated split-belt treadmill training improves poststroke step length asymmetry. Neurorehabil. Neural Repair 27, 460–468 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  258. Reisman, D. S., Wityk, R., Silver, K. & Bastian, A. J. Split-belt treadmill adaptation transfers to overground walking in persons poststroke. Neurorehabil. Neural Repair 23, 735–744 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  259. Day, K. A., Leech, K. A., Roemmich, R. T. & Bastian, A. J. Accelerating locomotor savings in learning: compressing four training days to one. J. Neurophysiol. 119, 2100–2113 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  260. Dhawale, A. K., Smith, M. A. & Ölveczky, B. P. The role of variability in motor learning. Annu. Rev. Neurosci. 40, 479–498 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  261. Marchal-Crespo, L., Lopez-Oloriz, J., Jaeger, L. & Riener, R. Optimizing learning of a locomotor task: amplifying errors as needed. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2014, 5304–5307 (2014).

  262. Sombric, C. J. & Torres-Oviedo, G. Augmenting propulsion demands during split-belt walking increases locomotor adaptation of asymmetric step lengths. J. Neuroeng. Rehabil. 17, 69 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  263. Porciuncula, F. et al. Targeting paretic propulsion and walking speed with a soft robotic exosuit: a consideration-of-concept trial. Front. Neurorobot. 15, 689577 (2021).

  264. Jonsdottir, J. et al. Task-oriented biofeedback to improve gait in individuals with chronic stroke: motor learning approach. Neurorehabil. Neural Repair 24, 478–485 (2010).

    Article  PubMed  Google Scholar 

  265. Parker, C. J., Guerin, H., Buchanan, B. & Lewek, M. D. Targeted verbal cues can immediately alter gait following stroke. Top. Stroke Rehabil. 29, 382–391 (2022).

    Article  PubMed  Google Scholar 

  266. Miller, K. K., Porter, R. E., DeBaun-Sprague, E., Van Puymbroeck, M. & Schmid, A. A. Exercise after stroke: patient adherence and beliefs after discharge from rehabilitation. Top. Stroke Rehabil. 24, 142–148 (2016).

    Article  PubMed  Google Scholar 

  267. Leroux, A. Exercise training to improve motor performance in chronic stroke: effects of a community-based exercise program. Int. J. Rehabil. Res. 28, 17–23 (2005).

    Article  PubMed  Google Scholar 

  268. Macko, R. F. et al. Treadmill exercise rehabilitation improves ambulatory function and cardiovascular fitness in patients with chronic stroke. Stroke 36, 2206–2211 (2005).

    Article  PubMed  Google Scholar 

  269. Telehealth HOD P06191509 (APTA, 2019); https://www.apta.org/apta-and-you/leadership-and-governance/policies/telehealth

  270. Dobkin, B. H. A Rehabilitation-Internet-of-Things in the home to augment motor skills and exercise training. Neurorehabil. Neural Repair 31, 217–227 (2017).

    Article  PubMed  Google Scholar 

  271. Sen-Gupta, E. et al. A pivotal study to validate the performance of a novel wearable sensor and system for biometric monitoring in clinical and remote environments. Digit. Biomark. 3, 1–13 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  272. Ray, T. R. et al. Bio-integrated wearable systems: a comprehensive review. Chem. Rev. 119, 5461–5533 (2019).

    Article  CAS  PubMed  Google Scholar 

  273. Elery, T., Reznick, E., Shearin, S., McCain, K. & Gregg, R. D. Design and initial validation of a multiple degree-of-freedom joint for an ankle-foot orthosis. J. Med. Devices 16, MED-19-1201 (2022).

  274. Fineman, R. A., McGrath, T. M., Kelty-Stephen, D. G., Abercromby, A. F. J. & Stirling, L. A. Objective metrics quantifying fit and performance in spacesuit assemblies. Aerosp. Med. Hum. Perform. 89, 985–995 (2018).

    Article  PubMed  Google Scholar 

  275. Tamez-Duque, J. et al. Real-time strap pressure sensor system for powered exoskeletons. Sensors 15, 4550–4563 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank L. Schumm and D. Orzel for help with the illustrations. This work was supported by the National Institutes of Health under award numbers BRG R01HD088619 and R01AG067394, and by the American Heart Association grant AHA 18TPA34170171. This work is based on studies supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE1144152, and by the National Science Foundation under Grant No. CMMI-1925085.

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  1. These authors contributed equally: Christopher Siviy, Lauren M. Baker.

Authors and Affiliations

  1. John A Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Christopher Siviy, Lauren M. Baker, Brendan T. Quinlivan, Franchino Porciuncula, Krithika Swaminathan & Conor J. Walsh

  2. Department of Physical Therapy, College of Health and Rehabilitation Sciences: Sargent, Boston University, Boston, MA, USA

    Franchino Porciuncula & Louis N. Awad

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Contributions

C.S. and L.M.B. contributed equally to the writing and revision of the manuscript. C.S., L.M.B. and B.T.Q. drafted the section ‘Reduction of metabolic cost’. C.S., L.M.B. and F.P. drafted the section ‘In-clinic validation’. K.S. provided input to the ‘Component technology’ and ‘Outlook’ sections. C.J.W. and L.N.A. provided input on the overall manuscript. All authors approved the final version of the manuscript.

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Correspondence to Conor J. Walsh.

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Competing interests

Harvard University has entered into a licensing-and-collaboration agreement with ReWalk Robotics. C.J.W., C.S. and B.T.Q. are co-inventors on licensed patents and are paid consultants for ReWalk Robotics. L.N.A. is a paid consultant for MedRhythms. The other authors declare no competing interests.

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Siviy, C., Baker, L.M., Quinlivan, B.T. et al. Opportunities and challenges in the development of exoskeletons for locomotor assistance. Nat. Biomed. Eng 7, 456–472 (2023). https://doi.org/10.1038/s41551-022-00984-1

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