Skip to main content
SpringerLink
Log in

Intelligent Knee Prostheses: A Systematic Review of Control Strategies

  • Review Article
  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

The intelligent knee prosthesis is capable of human-like bionic lower limb control through advanced control systems and artificial intelligence algorithms that will potentially minimize gait limitations for above-knee amputees and facilitate their reintegration into society. In this paper, we sum up the control strategies corresponding to the prevailing control objectives (position and impedance) of the current intelligent knee prosthesis. Although these control strategies have been successfully implemented and validated in relevant experiments, the existing deficiencies still fail to achieve optimal performance of the controllers, which complicates the definition of a standard control method. Before a mature control system can be developed, it is more important to realize the full potential for the control strategy, which requires upgrading and refining the relevant key technologies based on the existing control methods. For this reason, we discuss potential areas for improvement of the prosthetic control system based on the summarized control strategies, including intent recognition, sensor system, prosthetic evaluation, and parameter optimization algorithms, providing future directions toward optimizing control strategies for the next generation of intelligent knee prostheses.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Narang, I. C., Mathur, B. P., Singh, P., & Jape, V. S. (1984). Functional capabilities of lower limb amputees. Prosthetics and Orthotics International, 8, 43–51.

    Article  Google Scholar 

  2. Bellmann, M., Köhler, T. M., & Schmalz, T. (2018). Comparative biomechanical evaluation of two technologically different microprocessor-controlled prosthetic knee joints in safety-relevant daily-life situations. Biomedical Engineering-Biomedizinische Technik, 64, 407–420.

    Article  Google Scholar 

  3. Campbell, J. H., Stevens, P. M., & Wurdeman, S. R. (2020). OASIS 1: Retrospective analysis of four different microprocessor knee types. Journal of Rehabilitation and Assistive Technologies Engineering, 7, 2055668320968476.

    Article  Google Scholar 

  4. Kaufman, K. R., Bernhardt, K. A., & Symms, K. (2018). Functional assessment and satisfaction of transfemoral amputees with low mobility (FASTK2): A clinical trial of microprocesso-controlled vs. non-microprocessor -controlled knees. Clinical Biomechanics, 58, 116–122.

    Article  Google Scholar 

  5. McGrath, M., Laszczak, P., Zahedi, S., & Moser, D. (2018). Microprocessor knees with “standing support” and articulating, hydraulic ankles improve balance control and inter-limb loading during quiet standing. Journal of Rehabilitation and Assistive Technologies Engineering, 5, 2055668318795396.

    Article  Google Scholar 

  6. Thiele, J., Schöllig, C., Bellmann, M., & Kraft, M. (2019). Designs and performance of three new microprocessor-controlled knee joints. Biomedical Engineering - Biomedizinische Technik, 64, 119–126.

    Google Scholar 

  7. Awad, M. I., Dehghani-Sanij, A. A., Moser, D., & Zahedi, S. (2016). Motor electrical damping for back-drivable prosthetic knee. In Proceedings of the 11th France-Japan & 9th Europe-Asia Congress on Mechatronics/17th International Conference on Research and Education in Mechatronics, Compiegne, France, pp. 348–353.

  8. Elery, T., Rezazadeh, S., Reznick, E., Gray, L., & Gregg, R. (2020). Effects of a powered knee-ankle prosthesis on amputee hip compensations: A case series. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 2944–2954.

    Article  Google Scholar 

  9. Elery, T., Rezazadeh, S., Nesler, C., & Gregg, R. D. (2020). Design and validation of a powered knee–ankle prosthesis with high-torque, low-impedance actuators. IEEE Transactions on Robotics, 36, 1649–1668.

    Article  Google Scholar 

  10. Hoover, C. D., Fulk, G. D., & Fite, K. B. (2012). The design and initial experimental validation of an active myoelectric transfemoral prosthesis. Journal of Medical Devices-transactions of The ASME, 6, 011005.

    Article  Google Scholar 

  11. Lawson, B. E., Mitchell, J., Truex, D., Shultz, A., Ledoux, E., & Goldfarb, M. (2014). A robotic leg prosthesis: Design, control, and implementation. IEEE Robotics & Automation Magazine, 21, 70–81.

    Article  Google Scholar 

  12. Liu, M., Zhang, F., Datseris, P., & Huang, H. (2014). Improving finite state impedance control of active-transfemoral prosthesis using dempster-shafer based state transition rules. Journal of Intelligent & Robotic Systems, 76, 461–474.

    Article  Google Scholar 

  13. Quintero, D., Villarreal, D. J., Lambert, D. J., Kapp, S., & Gregg, R. (2018). Continuous-phase control of a powered knee–ankle prosthesis: Amputee experiments across speeds and inclines. IEEE Transactions on Robotics, 34, 686–701.

    Article  Google Scholar 

  14. Rouse, E., Mooney, L. M., & Herr, H. (2014). Clutchable series-elastic actuator: Implications for prosthetic knee design. The International Journal of Robotics Research, 33, 1611–1625.

    Article  Google Scholar 

  15. Sup, F., Bohara, A., & Goldfarb, M. (2008). Design and control of a powered transfemoral prosthesis. The International Journal of Robotics Research, 27, 263–273.

    Article  Google Scholar 

  16. Winter, D. A. (2009). Biomechanics and motor control of human movement (pp. 139–175). Wiley.

    Google Scholar 

  17. Lenzi, T., Cempini, M., Hargrove, L., & Kuiken, T. (2018). Design, development, and testing of a lightweight hybrid robotic knee prosthesis. The International Journal of Robotics Research, 37, 953–976.

    Article  Google Scholar 

  18. Lee, J. T., & Goldfarb, M. (2020). Swing-assist for enhancing stair ambulation in a primarily-passive knee prosthesis. In Proceedings of the IEEE international conference on robotics and automation, Paris, France, pp. 740–746.

  19. Tucker, M. R., Olivier, J., Pagel, A., Bleuler, H., Bouri, M., Lambercy, O., del Millán, R. J., Riener, R., Vallery, H., & Gassert, R. (2015). Control strategies for active lower extremity prosthetics and orthotics: a review. Journal of NeuroEngineering and Rehabilitation, 12, 1–30.

    Article  Google Scholar 

  20. El-Sayed, A. M., Hamzaid, N. A., & Osman, N. A. A. (2014). Technology efficacy in active prosthetic knees for transfemoral amputees: a quantitative evaluation. The Scientific World Journal, 2014, 297431.

    Article  Google Scholar 

  21. Torrealba, R. R., & Fonseca-Rojas, E. D. (2019). Toward the development of knee prostheses: review of current active devices. Applied Mechanics Reviews, 71, 030801.

    Article  Google Scholar 

  22. Farina, D., Vujaklija, I., Brånemark, R., Bull, A., Dietl, H., Graimann, B., Hargrove, L., Hoffmann, K., Huang, H., Ingvarsson, T., Janusson, H. B., Kristjánsson, K., Kuiken, T., Micera, S., Stieglitz, T., Sturma, A., Tyler, D., Weir, R., & Aszmann, O. (2021). Toward higher-performance bionic limbs for wider clinical use. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-021-00732-x

    Article  Google Scholar 

  23. Windrich, M., Grimmer, M., Christ, O., Rinderknecht, S., & Beckerle, P. (2016). Active lower limb prosthetics: A systematic review of design issues and solutions. BioMedical Engineering OnLine, 15, 140.

    Article  Google Scholar 

  24. Fluit, R., Prinsen, E. C., Wang, S. Q., & van der Kooij, H. (2020). A comparison of control strategies in commercial and research knee prostheses. IEEE Transactions on Biomedical Engineering, 67, 277–290.

    Article  Google Scholar 

  25. Jimenez-Fabian, R., & Verlinden, O. (2012). Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Medical Engineering & Physics, 34, 397–408.

    Article  Google Scholar 

  26. Tepe, V., & Peterson, C. M. (2017). Full stride: Advancing the state of the art in lower extremity gait systems (pp. 137–153). Springer.

    Book  Google Scholar 

  27. Fleming, A., Stafford, N. E., Huang, S., Hu, X. G., Ferris, D., & Huang, H. (2021). Myoelectric control of robotic lower limb prostheses: A review of electromyography interfaces, control paradigms, challenges and future directions. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/ac1176

    Article  Google Scholar 

  28. Nasr, A., Laschowski, B., & McPhee, J. (2021). Myoelectric control of robotic leg prostheses and exoskeletons: a review. In Proceedings of the ASME 2021 international design engineering technical conferences and computers and information in engineering conference, Online.

  29. Lenzi, T., Hargrove, L., & Sensinger, J. (2014). Speed-adaptation mechanism: Robotic prostheses can actively regulate joint torque. IEEE Robotics & Automation Magazine, 21, 94–107.

    Article  Google Scholar 

  30. Vallery, H., Burgkart, R., Hartmann, C., Mitternacht, J., Riener, R., & Buss, M. (2011). Complementary limb motion estimation for the control of active knee prostheses. Biomedical Engineering-Biomedizinische Technik, 56, 45–51.

    Article  Google Scholar 

  31. Lawson, B., Varol, H. A., Huff, A., Erdemir, E., & Goldfarb, M. (2013). Control of stair ascent and descent with a powered transfemoral prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21, 466–473.

    Article  Google Scholar 

  32. Sup, F., Varol, H. A., Mitchell, J., Withrow, T. J., & Goldfarb, M. (2009). Preliminary evaluations of a self-contained anthropomorphic transfemoral prosthesis. IEEE/ASME Transactions on Mechatronics, 14, 667–676.

    Article  Google Scholar 

  33. Bernal-Torres, M., Medellín-Castillo, H. I., & Arellano-González, J. C. (2018). Design and control of a new biomimetic transfemoral knee prosthesis using an echo-control scheme. Journal of Healthcare Engineering, 2018, 8783642.

    Article  Google Scholar 

  34. Ueyama, Y., Kubo, T., & Shibata, M. (2020). Robotic hip-disarticulation prosthesis: Evaluation of prosthetic gaits in a non-amputee individual. Advanced Robotics, 34, 37–44.

    Article  Google Scholar 

  35. Borjian, R., Khamesee, M. B., & Melek, W. (2009). Feasibility study on echo control of a prosthetic knee: Sensors and wireless communication. Microsystem Technologies, 16, 257–265.

    Article  Google Scholar 

  36. Ranzani, R. (2014). Adaptive human model-based control for active knee prosthetics. In Master thesis, sensory-motor systems lab, Swiss Federal Institute of Technology (ETH), Zürich.

  37. Zhang, K. G., de Silva, C. W., & Fu, C. L. (2019). Sensor fusion for predictive control of human-prosthesis-environment dynamics in assistive walking: a survey. arxiv 1903.07674.

  38. Lenzi, T., Sensinger, J., Lipsey, J., Hargrove, L., & Kuiken, T. (2015). Design and preliminary testing of the RIC hybrid knee prosthesis. In Proceedings of the 37th annual international conference of the IEEE engineering in medicine and biology society, Milan, Italy, pp. 1683–1686.

  39. Tran, M., Gabert, L., Cempini, M., & Lenzi, T. (2019). A lightweight, efficient fully powered knee prosthesis with actively variable transmission. IEEE Robotics and Automation Letters, 4, 1186–1193.

    Article  Google Scholar 

  40. Mendez, J., Hood, S., Gunnel, A., & Lenzi, T. (2020). Powered knee and ankle prosthesis with indirect volitional swing control enables level-ground walking and crossing over obstacles. Science Robotics, 5, eaba6635.

    Article  Google Scholar 

  41. Gao, H. B., Luo, L., Pi, M., Li, Z. J., Li, Q. J., Zhao, K. K., & Huang, J. L. (2021). EEG-based volitional control of prosthetic legs for walking in different terrains. IEEE Transactions on Automation Science and Engineering, 18, 530–540.

    Article  Google Scholar 

  42. Zhao, H. H., Ambrose, E., & Ames, A. (2017). Preliminary results on energy efficient 3D prosthetic walking with a powered compliant transfemoral prosthesis. In Proceedings of the 2017 IEEE International Conference on Robotics and Automation, Singapore, pp. 1140–1147.

  43. Gregg, R., Lenzi, T., Fey, N., Hargrove, L., & Sensinger, J. (2013). Experimental effective shape control of a powered transfemoral prosthesis. In Proceedings of the IEEE 13th international conference on rehabilitation robotics, Seattle, USA, pp. 1–7.

  44. Gregg, R., Lenzi, T., Hargrove, L., & Sensinger, J. (2014). Virtual constraint control of a powered prosthetic leg: From simulation to experiments with transfemoral amputees. IEEE Transactions on Robotics, 30, 1455–1471.

    Article  Google Scholar 

  45. Gregg, R., & Sensinger, J. (2013). Biomimetic virtual constraint control of a transfemoral powered prosthetic leg. In Proceedings of the 2013 American Control Conference, Washington, USA, pp. 5702–5708.

  46. Gregg, R., & Sensinger, J. (2014). Towards biomimetic virtual constraint control of a powered prosthetic leg. IEEE Transactions on Control Systems Technology, 22, 246–254.

    Article  Google Scholar 

  47. Quintero, D., Lambert, D. J., Villarreal, D. J., & Gregg, R. (2017). Real-time continuous gait phase and speed estimation from a single sensor. In Proceedings of the 2017 IEEE conference on control technology and applications, Maui, USA, pp. 847–852.

  48. Rezazadeh, S., Quintero, D., Divekar, N. V., & Gregg, R. (2018). A phase variable approach to volitional control of powered knee-ankle prostheses. In Proceedings of the 2018 IEEE/RSJ international conference on intelligent robots and systems, Madrid, Spain, pp. 2292–2298.

  49. Villarreal, D. J., Poonawala, H. A., & Gregg, R. (2017). A robust parameterization of human gait patterns across phase-shifting perturbations. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25, 265–278.

    Article  Google Scholar 

  50. Villarreal, D. J., & Gregg, R. (2016). Unified phase variables of relative degree two for human locomotion. In Proceedings of the 38th Annual international conference of the IEEE engineering in medicine and biology society, Orlando, USA, pp. 6262–6267.

  51. Quintero, D., Martin, A. E., & Gregg, R. (2018). Toward unified control of a powered prosthetic leg: A simulation study. IEEE Transactions on Control Systems Technology, 26, 305–312.

    Article  Google Scholar 

  52. Quintero, D., Villarreal, D. J., & Gregg, R. (2016). Preliminary experiments with a unified controller for a powered knee-ankle prosthetic leg across walking speeds. In Proceedings of the 2016 IEEE/RSJ international conference on intelligent robots and systems, Daejeon, Korea (South), pp. 5427–5433.

  53. Quintero, D., Reznick, E., Lambert, D. J., Rezazadeh, S., Gray, L., & Gregg, R. (2018). Intuitive clinician control interface for a powered knee-ankle prosthesis: A case study. IEEE Journal of Translational Engineering in Health and Medicine, 6, 1–9.

    Article  Google Scholar 

  54. Fite, K., Mitchell, J., Sup, F., & Goldfarb, M. (2007). Design and control of an electrically powered knee prosthesis. In Proceedings of the 2007 IEEE 10th international conference on rehabilitation robotics, Noordwijk, Netherlands, pp. 902–905.

  55. Sup, F., Varol, H. A., Mitchell, J., Withrow, T., & Goldfarb, M. (2008). Design and control of an active electrical knee and ankle prosthesis. In Proceedings of the 2008 2nd IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics, Scottsdale, USA, pp. 523–528.

  56. Sup, F., Varol, H. A., Mitchell, J. E., Withrow, T., & Goldfarb, M. (2009). Self-contained powered knee and ankle prosthesis: initial evaluation on a transfemoral amputee. In Proceedings of the IEEE international conference on rehabilitation robotics, Kyoto, Japan, pp. 638–644.

  57. Lawson, B., Varol, H. A., & Goldfarb, M. (2011). Standing stability enhancement with an intelligent powered transfemoral prosthesis. IEEE Transactions on Biomedical Engineering, 58, 2617–2624.

    Article  Google Scholar 

  58. Sup, F., Varol, H. A., & Goldfarb, M. (2011). Upslope walking with a powered knee and ankle prosthesis: Initial results with an amputee subject. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19, 71–78.

    Article  Google Scholar 

  59. Shultz, A., Lawson, B., & Goldfarb, M. (2015). Running with a powered knee and ankle prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23, 403–412.

    Article  Google Scholar 

  60. Lawson, B., Ruhe, B., Shultz, A., & Goldfarb, M. (2015). A powered prosthetic intervention for bilateral transfemoral amputees. IEEE Transactions on Biomedical Engineering, 62, 1042–1050.

    Article  Google Scholar 

  61. Chen, G. X., Liu, Z. J., Chen, L. L., & Yang, P. (2015). Control of powered knee joint prosthesis based on finite-state machine. In: Proceedings of the 2015 Chinese Intelligent Automation Conference, China, pp. 395–403.

  62. Martinez-Villalpando, E., & Herr, H. (2009). Agonist-antagonist active knee prosthesis: A preliminary study in level-ground walking. Journal of Rehabilitation Research and Development, 46, 361–373.

    Article  Google Scholar 

  63. Flynn, L., Geeroms, J., Jimenez-Fabian, R., Vanderborght, B., & Lefeber, D. (2015). CYBERLEGS Beta-prosthesis active knee system. In Proceedings of the 2015 IEEE international conference on rehabilitation robotics, Singapore, pp. 410–415.

  64. Ambrozic, L., Gorsic, M., Geeroms, J., Flynn, L., Lova, R. M., Kamnik, R., Munih, M., & Vitiello, N. (2014). CYBERLEGs: A user-oriented robotic transfemoral prosthesis with whole-body awareness control. IEEE Robotics & Automation Magazine, 21, 82–93.

    Article  Google Scholar 

  65. Bhakta, K., Camargo, J., Kunapuli, P., Childers, L., & Young, A. (2020). Impedance control strategies for enhancing sloped and level walking capabilities for individuals with transfemoral amputation using a powered multi-joint prosthesis. Military Medicine, 185, 490–499.

    Article  Google Scholar 

  66. Wen, Y., Brandt, A., Liu, M., Huang, H., & Si, J. (2017). Comparing parallel and sequential control parameter tuning for a powered knee prosthesis. In Proceedings of the 2017 IEEE international conference on systems, man, and cybernetics, Banff, Canada, pp. 1716–1721.

  67. Wen, Y., Li, M., Si, J., & Huang, H. (2020). Wearer-prosthesis interaction for symmetrical gait: A study enabled by reinforcement learning prosthesis control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 904–913.

    Article  Google Scholar 

  68. Wen, Y., Si, J., Brandt, A., Gao, X., & Huang, H. (2020). Online reinforcement learning control for the personalization of a robotic knee prosthesis. IEEE Transactions on Cybernetics, 50, 2346–2356.

    Article  Google Scholar 

  69. Carney, M. E., Shu, T., Stolyarov, R., Duval, J.-F., & Herr, H. (2021). Design and preliminary results of a reaction force series elastic actuator for bionic knee and ankle prostheses. IEEE Transactions on Medical Robotics and Bionics, 3, 542–553.

    Article  Google Scholar 

  70. Sup, F., Bohara, A., & Goldfarb, M. (2007). Design and control of a powered knee and ankle prosthesis. In Proceedings of the 2007 IEEE international conference on robotics and automation, Rome, Italy, pp. 4134–4139.

  71. Flynn, L., Geeroms, J., Jimenez-Fabian, R., Heins, S., Vanderborght, B., Munih, M., Lova, R. M., Vitiello, N., & Lefeber, D. (2018). The challenges and achievements of experimental implementation of an active transfemoral prosthesis based on biological quasi-stiffness: The CYBERLEGs Beta-prosthesis. Frontiers in Neurorobotics, 12, 80.

    Article  Google Scholar 

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

    Article  Google Scholar 

  73. Simon, A. M., Ingraham, K. A., Fey, N., Finucane, S., Lipschutz, R., Young, A., & Hargrove, L. (2014). Configuring a powered knee and ankle prosthesis for transfemoral amputees within five specific ambulation modes. PLoS ONE, 9, 1–10.

    Article  Google Scholar 

  74. Simon, A. M., Fey, N., Finucane, S., Lipschutz, R., & Hargrove, L. (2013). Strategies to reduce the configuration time for a powered knee and ankle prosthesis across multiple ambulation modes. In Proceedings of the 2013 IEEE 13th international conference on rehabilitation robotics, Seattle, USA, pp. 1–6.

  75. Lawson, B., Shultz, A., & Goldfarb, M. (2013). Evaluation of a coordinated control system for a pair of powered transfemoral prostheses. In Proceedings of the 2013 IEEE international conference on robotics and automation, Karlsruhe, Germany, pp. 3888–3893.

  76. Azocar, A. F., Mooney, L. M., Duval, J.-F., Simon, A. M., Hargrove, L., & Rouse, E. (2020). Design and clinical implementation of an open-source bionic leg. Nature Biomedical Engineering, 4, 941–953.

    Article  Google Scholar 

  77. Flynn, L., Geeroms, J., Hoeven, T. V. D., Vanderborght, B., & Lefeber, D. (2017). VUB-CYBERLEGs CYBATHLON 2016 Beta-prosthesis: case study in control of an active two degree of freedom transfemoral prosthesis. Journal of NeuroEngineering and Rehabilitation, 15, 3.

    Article  Google Scholar 

  78. Khademi, G., & Simon, D. (2021). Toward minimal-sensing locomotion mode recognition for a powered knee-ankle prosthesis. IEEE Transactions on Biomedical Engineering, 68, 967–979.

    Article  Google Scholar 

  79. Hargrove, L., Simon, A. M., Lipschutz, R., Finucane, S., & Kuiken, T. (2012). Non-weight-bearing neural control of a powered transfemoral prosthesis. Journal of NeuroEngineering and Rehabilitation, 10, 62.

    Article  Google Scholar 

  80. Ha, K. H., Varol, H. A., & Goldfarb, M. (2011). Volitional control of a prosthetic knee using surface electromyography. IEEE Transactions on Biomedical Engineering, 58, 144–151.

    Article  Google Scholar 

  81. Hargrove, L., Simon, A. M., Lipschutz, R., Finucane, S., & Kuiken, T. (2011). Real-time myoelectric control of knee and ankle motions for transfemoral amputees. JAMA, 305, 1542–1544.

    Article  Google Scholar 

  82. Hargrove, L., Simon, A. M., Young, A., Lipschutz, R., Finucane, S., Smith, D. G., & Kuiken, T. (2013). Robotic leg control with EMG decoding in an amputee with nerve transfers. The New England Journal of Medicine, 369, 1237–1242.

    Article  Google Scholar 

  83. Hargrove, L., Young, A., Simon, A. M., Fey, N., Lipschutz, R., Finucane, S., Halsne, E. G., Ingraham, K. A., & Kuiken, T. (2015). Intuitive control of a powered prosthetic leg during ambulation: A randomized clinical trial. JAMA, 313, 2244–2252.

    Article  Google Scholar 

  84. Spanias, J. A., Perreault, E., & Hargrove, L. (2016). Detection of and compensation for EMG disturbances for powered lower limb prosthesis control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24, 226–234.

    Article  Google Scholar 

  85. Spanias, J. A., Simon, A. M., Finucane, S., Perreault, E., & Hargrove, L. (2018). Online adaptive neural control of a robotic lower limb prosthesis. Journal of Neural Engineering, 15, 016015.

    Article  Google Scholar 

  86. Liu, M., Zhang, F., & Huang, H. (2017). An adaptive classification strategy for reliable locomotion mode recognition. Sensors, 17, 2020.

    Article  Google Scholar 

  87. Hoover, C. D., Fulk, G., & Fite, K. (2013). Stair ascent with a powered transfemoral prosthesis under direct myoelectric control. IEEE/ASME Transactions on Mechatronics, 18, 1191–1200.

    Article  Google Scholar 

  88. Dawley, J. A., Fite, K., & Fulk, G. (2013). EMG control of a bionic knee prosthesis: exploiting muscle co-contractions for improved locomotor function. In Proceedings of the 2013 IEEE 13th international conference on rehabilitation robotics, Seattle, USA, pp. 1–6.

  89. Hoover, C. D., Fite, K., Fulk, G., & Holmes, D. (2011). Myoelectric torque control of an active transfemoral prosthesis during stair ascent. In Proceedings of the ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Arlington, USA, pp. 451–458.

  90. Thatte, N., & Geyer, H. (2016). Toward balance recovery with leg prostheses using neuromuscular model control. IEEE Transactions on Biomedical Engineering, 63, 904–913.

    Article  Google Scholar 

  91. Cimolato, A., Milandri, G., Mattos, L., Momi, E., Laffranchi, M., & Michieli, L. D. (2020). Hybrid machine learning-neuromusculoskeletal modeling for control of lower limb prosthetics. In Proceedings of the 2020 8th IEEE RAS/EMBS international conference for biomedical robotics and biomechatronics, New York, USA, pp. 557–563.

  92. Hoover, C. D., & Fite, K. (2011). A configuration dependent muscle model for the myoelectric control of a transfemoral prosthesis. In Proceedings of the 2011 IEEE international conference on rehabilitation robotics, Zurich, Switzerland, pp. 1–6.

  93. Grimes, D. L., Flowers, W., & Donath, M. (1977). Feasibility of an active control scheme for above knee prostheses. Journal of Biomechanical Engineering-Transactions of The ASME, 99, 215–221.

    Article  Google Scholar 

  94. Stein, J. (1983). Design issues in the stance phase control of above-knee prostheses. In Dept. of Mechanical Engineering PhD Thesis, MIT.

  95. Bédard, S. (2006). Control system and method for controlling an actuated prosthesis. U.S. Patent No. 7147667.

  96. Vallery, H., Asseldonk, E. V. V., Buss, M., & Kooij, H. V. D. (2009). Reference trajectory generation for rehabilitation robots: Complementary limb motion estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17, 23–30.

    Article  Google Scholar 

  97. Vallery, H., Ekkelenkamp, R., Buss, M., & Kooij, H. V. D. (2007). Complementary limb motion estimation based on interjoint coordination: experimental evaluation. In Proceedings of the 2007 IEEE 10th international conference on rehabilitation robotics, Noordwijk, Netherlands, pp. 798–803.

  98. St-Onge, N., & Feldman, A. G. (2002). Interjoint coordination in lower limbs during different movements in humans. Experimental Brain Research, 148, 139–149.

    Article  Google Scholar 

  99. Zhao, H. H., Horn, J. C., Reher, J., Paredes, V., & Ames, A. (2017). First steps toward translating robotic walking to prostheses: A nonlinear optimization based control approach. Autonomous Robots, 41, 725–742.

    Article  Google Scholar 

  100. Grimmer, M., Zeiss, J., Weigand, F., Zhao, G. P., Lamm, S., Steil, M., & Heller, A. (2020). Lower limb joint biomechanics-based identification of gait transitions in between level walking and stair ambulation. PLoS ONE, 15, 148.

    Article  Google Scholar 

  101. Hogan, N. (1984). Impedance control: an approach to manipulation. In Proceedings of the 1984 American Control Conference, San Diego, USA, pp. 304–313.

  102. Rabuffetti, M., Recalcati, M., & Ferrarin, M. (2005). Transfemoral amputee gait: Socket-pelvis constraints and compensation strategies. Prosthetics and Orthotics International, 29, 183–192.

    Article  Google Scholar 

  103. Sanderson, D., & Martin, P. E. (1997). Lower extremity kinematic and kinetic adaptations in unilateral below-knee amputees during walking. Gait & Posture, 6, 126–136.

    Article  Google Scholar 

  104. Clancy, E., Liu, L. K., Liu, P., & Moyer, D. V. (2012). Identification of constant-posture EMG torque relationship about the elbow using nonlinear dynamic models. IEEE Transactions on Biomedical Engineering, 59, 205–212.

    Article  Google Scholar 

  105. Pfeifer, S., Vallery, H., Hardegger, M., Riener, R., & Perreault, E. (2012). Model-based estimation of knee stiffness. IEEE Transactions on Biomedical Engineering, 59, 2604–2612.

    Article  Google Scholar 

  106. Koller, J. R., Jacobs, D. A., Ferris, D. P., & Remy, C. D. (2015). Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton. Journal of NeuroEngineering and Rehabilitation, 12, 1–14.

    Article  Google Scholar 

  107. Geyer, H., & Herr, H. (2010). A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18, 263–273.

    Article  Google Scholar 

  108. Desai, R., & Geyer, H. (2012). Robust swing leg placement under large disturbances. In Proceedings of the 2012 IEEE International Conference on Robotics and Biomimetics, Guangzhou, China, pp. 265–270.

  109. Nasr, A., & McPhee, J. (2020). Control-oriented muscle torque (COMT) modelfor EMG-based control of assistive robots. In Proceedings of the 7th international conference of control dynamic systems, and robotics, virtual conference, p. 144.

  110. Parri, A., Martini, E., Geeroms, J., Flynn, L., Pasquini, G., Crea, S., Molino Lova, R., Lefeber, D., Kamnik, R., Munih, M., & Vitiello, N. (2017). Whole body awareness for controlling a robotic transfemoral prosthesis. Frontiers in Neurorobotics, 11, 1–14.

    Article  Google Scholar 

  111. Woodward, R. B., Spanias, J. A., & Hargrove, L. (2016). User intent prediction with a scaled conjugate gradient trained artificial neural network for lower limb amputees using a powered prosthesis. In Proceedings of the 2016 38th annual international conference of the IEEE engineering in medicine and biology society, Orlando, USA, pp. 6405–6408.

  112. Young, A., Simon, A. M., Fey, N., & Hargrove, L. (2013). Classifying the intent of novel users during human locomotion using powered lower limb prostheses. In Proceedings of the 2013 6th international IEEE/EMBS conference on neural engineering, San Diego, USA, pp. 311–314. https://doi.org/10.1109/NER.2013.6695934.

  113. Varol, H. A., Sup, F., & Goldfarb, M. (2010). Multiclass real-time intent recognition of a powered lower limb prosthesis. IEEE Transactions on Biomedical Engineering, 57, 542–551.

    Article  Google Scholar 

  114. Young, A., Simon, A. M., Fey, N., & Hargrove, L. (2013). Intent recognition in a powered lower limb prosthesis using time history information. Annals of Biomedical Engineering, 42, 631–641.

    Article  Google Scholar 

  115. Simon, A. M., Ingraham, K. A., Spanias, J. A., Young, A., Finucane, S., Halsne, E. G., & Hargrove, L. (2017). Delaying ambulation mode transition decisions improves accuracy of a flexible control system for powered knee-ankle prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25, 1164–1171.

    Article  Google Scholar 

  116. Young, A., & Hargrove, L. (2016). A classification method for user-independent intent recognition for transfemoral amputees using powered lower limb prostheses. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24, 217–225.

    Article  Google Scholar 

  117. Su, B. Y., Wang, J., Liu, S.-Q., Sheng, M., Jiang, J., & Xiang, K. (2019). A CNN-based method for intent recognition using inertial measurement units and intelligent lower limb prosthesis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27, 1032–1042.

    Article  Google Scholar 

  118. Lee, U. H., Bi, J., Patel, R., Fouhey, D. F., & Rouse, E. (2020). Image transformation and CNNs: A strategy for encoding human locomotor intent for autonomous wearable robots. IEEE Robotics and Automation Letters, 5, 5440–5447.

    Article  Google Scholar 

  119. Zhang, K. G., Luo, J. W., Xiao, W. T., Zhang, W., Liu, H. Y., Zhu, J. L., Lu, Z. Y., Rong, Y. M., de Silva, C. W., & Fu, C. L. (2021). A subvision system for enhancing the environmental adaptability of the powered transfemoral prosthesis. IEEE Transactions on Cybernetics, 51, 3285–3297.

    Article  Google Scholar 

  120. Zhang, K. G., Xiong, C., Zhang, W., Liu, H. H., Lai, D. Y., Rong, Y. M., & Fu, C. L. (2019). Environmental features recognition for lower limb prostheses toward predictive walking. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27, 465–476.

    Article  Google Scholar 

  121. Huang, H., Zhang, F., Hargrove, L., Dou, Z., Rogers, D. R., & Englehart, K. (2011). Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion. IEEE Transactions on Biomedical Engineering, 58, 2867–2875.

    Article  Google Scholar 

  122. Young, A., Kuiken, T., & Hargrove, L. (2014). Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses. Journal of Neural Engineering, 11, 056021.

    Article  Google Scholar 

  123. Hu, B. H., Rouse, E., & Hargrove, L. (2018). Fusion of bilateral lower-limb neuromechanical signals improves prediction of locomotor activities. Frontiers in Robotics and AI, 5, 1–16.

    Article  Google Scholar 

  124. Zhang, F., Liu, M., & Huang, H. (2015). Investigation of timing to switch control mode in powered knee prostheses during task transitions. PLoS ONE, 10, e0133965.

    Article  Google Scholar 

  125. Zhang, F., Liu, M., & Huang, H. (2012). Preliminary study of the effect of user intent recognition errors on volitional control of powered lower limb prostheses. In Proceedings of the 2012 annual international conference of the IEEE engineering in medicine and biology society, San Diego, USA, pp. 2768–2771.

  126. Zhang, F., Liu, M., & Huang, H. (2015). Detection of critical errors of locomotion mode recognition for volitional control of powered transfemoral prostheses. In Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, pp. 1128–1131.

  127. Artemiadis, P., & Kyriakopoulos, K. (2010). An EMG-based robot control scheme robust to time-varying EMG signal features. IEEE Transactions on Information Technology in Biomedicine, 14, 582–588.

    Article  Google Scholar 

  128. Young, A., Hargrove, L., & Kuiken, T. (2012). Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration. IEEE Transactions on Biomedical Engineering, 59, 645–652.

    Article  Google Scholar 

  129. Kapti, A. O., & Yucenur, M. S. (2006). Design and control of an active artificial knee joint. Mechanism and Machine Theory, 41, 1477–1485.

    Article  MATH  Google Scholar 

  130. Wentink, E., Koopman, H., Stramigioli, S., Rietman, J., & Veltink, P. (2013). Variable stiffness actuated prosthetic knee to restore knee buckling during stance: A modeling study. Medical Engineering & Physics, 35, 838–845.

    Article  Google Scholar 

  131. Papaioannou, G., Mitrogiannis, C., Nianios, G., & Fiedler, G. (2010). Assessment of amputee socket-stump-residual bone kinematics during strenuous activities using dynamic roentgen stereogrammetric analysis. Journal of biomechanics, 43, 871–878.

    Article  Google Scholar 

  132. Stachaczyk, M., Atashzar, S. F., & Farina, D. (2020). Adaptive spatial filtering of high-density EMG for reducing the influence of noise and artefacts in myoelectric control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 1511–1517.

    Article  Google Scholar 

  133. Graczyk, E. L., Resnik, L., Schiefer, M., Schmitt, M. S., & Tyler, D. (2018). Home use of a neural-connected sensory prosthesis provides the functional and psychosocial experience of having a hand again. Scientific Reports, 8, 9866.

    Article  Google Scholar 

  134. Lewis, S., Russold, M., Dietl, H., Ruff, R., Audi, J. C., Hoffmann, K., Saleh, L. A., Schroeder, D., Krautschneider, W., Westendorff, S., Gail, A., Meiners, T., & Kaniusas, E. (2013). Fully implantable multi-channel measurement system for acquisition of muscle activity. IEEE Transactions on Instrumentation and Measurement, 62, 1972–1981.

    Article  Google Scholar 

  135. Merrill, D. R., Lockhart, J., Troyk, P., Weir, R., & Hankin, D. (2011). Development of an implantable myoelectric sensor for advanced prosthesis control. Artificial Organs, 35, 249–252.

    Article  Google Scholar 

  136. Ortiz-Catalán, M., Håkansson, B., & Brånemark, R. (2014). An osseointegrated human-machine gateway for long-term sensory feedback and motor control of artificial limbs. Science Translational Medicine, 6, 257re6.

    Article  Google Scholar 

  137. Matthis, J., Yates, J., & Hayhoe, M. (2018). Gaze and the control of foot placement when walking in natural terrain. Current Biology, 28, 1224–1233.

    Article  Google Scholar 

  138. Blanke, O. (2012). Multisensory brain mechanisms of bodily self-consciousness. Nature Reviews Neuroscience, 13, 556–571.

    Article  Google Scholar 

  139. Ehrsson, H., Rosén, B., Stockselius, A., Ragnö, C., Köhler, P., & Lundborg, G. (2008). Upper limb amputees can be induced to experience a rubber hand as their own. Brain, 131, 3443–3452.

    Article  Google Scholar 

  140. Fogelberg, D. J., Allyn, K. J., Smersh, M., & Maitland, M. (2016). What people want in a prosthetic foot: A focus group study. Journal of Prosthetics and Orthotics, 28, 145–151.

    Article  Google Scholar 

  141. Preatoni, G., Valle, G., Petrini, F., & Raspopovic, S. (2021). Lightening the perceived prosthesis weight with neural embodiment promoted by sensory feedback. Current Biology, 31, 1065–1071.

    Article  Google Scholar 

  142. Witteveen, H. J. B., Rietman, H., & Veltink, P. (2015). Vibrotactile grasping force and hand aperture feedback for myoelectric forearm prosthesis users. Prosthetics and Orthotics International, 39, 204–212.

    Article  Google Scholar 

  143. Dpaen, S., Ninu, A., Yakimovich, T., Dietl, H., & Farina, D. (2016). A novel method to generate amplitude-frequency modulated vibrotactile stimulation. IEEE Transactions on Haptics, 9, 3–12.

    Article  Google Scholar 

  144. Patterson, P., & Katz, J. A. (1992). Design and evaluation of a sensory feedback system that provides grasping pressure in a myoelectric hand. Journal of Rehabilitation Research and Development, 29, 1–8.

    Article  Google Scholar 

  145. Srinivasan, S., Carty, M., Calvaresi, P., Clites, T., Maimon, B., Taylor, C. R., Zorzos, A., & Herr, H. (2017). On prosthetic control: A regenerative agonist-antagonist myoneural interface. Science Robotics, 2, 2971.

    Article  Google Scholar 

  146. Clites, T., Carty, M., Ullauri, J. B., Carney, M., Mooney, L. M., Duval, J.-F., Srinivasan, S., & Herr, H. J. S. T. M. (2018). Proprioception from a neurally controlled lower-extremity prosthesis. Science Translational Medicine, 10, eaap8373.

    Article  Google Scholar 

  147. Petrini, F., Valle, G., Bumbasirevic, M., Barberi, F., Bortolotti, D., Cvancara, P., Hiairrassary, A., Mijovic, P., Sverrisson, A., Pedrocchi, A., Divoux, J., Popovj, I., Lechler, K., Mijovic, B., Guiraud, D., Stieglitz, T., Alexandersson, A., Micera, S., Lfai, A., & Raspopovic, S. (2019). Enhancing functional abilities and cognitive integration of the lower limb prosthesis. Science Translational Medicine, 11, 512.

    Article  Google Scholar 

  148. Petrini, F., Bumbasirevic, M., Valle, G., Ili, V., Mijovic, P., Vaoara, P., Barberi, F., Katj, N., Bortolotti, D., Andreu, D., Lechler, K., Leaj, A., Mazi, S., Mijovic, B., Guiraud, D., Stieglitz, T., Alexandersson, A., Micera, S., & Raspopovic, S. (2019). Sensory feedback restoration in leg amputees improves walking speed, metabolic cost and phantom pain. Nature Medicine, 25, 1356–1363.

    Article  Google Scholar 

  149. Valle, G., Saliji, A., Fogle, E., Cimolato, A., Petrini, F., & Raspopovic, S. (2021). Mechanisms of neuro-robotic prosthesis operation in leg amputees. Science Advances, 7, eabd8354.

    Article  Google Scholar 

  150. Charkhkar, H., Shell, C. E., Marasco, P., Pinault, G., Tyler, D., & Triolo, R. (2018). High-density peripheral nerve cuffs restore natural sensation to individuals with lower-limb amputations. Journal of Neural Engineering, 15, 05602.

    Article  Google Scholar 

  151. Ordonez, J., Schuettler, M., Boehler, C., Boretius, T., & Stieglitz, T. (2012). Thin films and microelectrode arrays for neuroprosthetics. MRS Bulletin, 37, 590–598.

    Article  Google Scholar 

  152. Zelechowski, M., Valle, G., & Raspopovic, S. (2020). A computational model to design neural interfaces for lower-limb sensory neuroprostheses. Journal of NeuroEngineering and Rehabilitation, 17, 24.

    Article  Google Scholar 

  153. Wanamaker, A. B., Andridge, R. R., & Chaudhari, A. M. (2017). When to biomechanically examine a lower-limb amputee: A systematic review of accommodation times. Prosthetics and Orthotics International, 41, 431–445.

    Article  Google Scholar 

  154. Zhang, X. Y., Fiedler, G., & Liu, Z. C. (2019). Evaluation of gait variable change over time as transtibial amputees adapt to a new prosthesis foot. BioMed Research International, 2019, 6.

    Google Scholar 

  155. Fleming, A., Huang, S., & Huang, H. (2019). Proportional myoelectric control of a virtual inverted pendulum using residual antagonistic muscles: Toward voluntary postural control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27, 1473–1482.

    Article  Google Scholar 

  156. Johnson, S. S., & Mansfield, E. (2014). Prosthetic training: Upper limb. Physical Medicine and Rehabilitation Clinics of North America, 25, 133–151.

    Article  Google Scholar 

  157. Lee, J., Bartlett, H. L., & Goldfarb, M. (2020). Design of a semipowered stance-control swing-assist transfemoral prosthesis. IEEE/ASME Transactions on Mechatronics, 25, 175–184.

    Article  Google Scholar 

  158. Huang, H., Crouch, D., Liu, M., Sawicki, G., & Wang, D. (2015). A cyber expert system for auto-tuning powered prosthesis impedance control parameters. Annals of Biomedical Engineering, 44, 1613–1624.

    Article  Google Scholar 

  159. Chen, B. J., Zheng, E. H., Wang, Q. N., & Wang, L. (2015). A new strategy for parameter optimization to improve phase-dependent locomotion mode recognition. Neurocomputing, 149, 585–593.

    Article  Google Scholar 

  160. Khademi, G., Mohammadi, H., & Simon, D. (2019). Gradient-based multi-objective feature selection for gait mode recognition of transfemoral amputees. Sensors, 19, 253.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the support of the National Natural Science Foundation of China (grant no. 62073224) and National Key Research and Development Program of China (grant no. 2018YFB1307303).

Author information

Authors and Affiliations

  1. Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, No. 516, Jungong Road, Yangpu District, Shanghai, 200093, China

    Linrong Li, Xiaoming Wang, Qiaoling Meng, Changlong Chen, Jie Sun & Hongliu Yu

  2. Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China

    Linrong Li, Xiaoming Wang, Qiaoling Meng, Changlong Chen, Jie Sun & Hongliu Yu

  3. Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China

    Linrong Li, Xiaoming Wang, Qiaoling Meng, Changlong Chen, Jie Sun & Hongliu Yu

Authors
  1. Linrong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoming Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiaoling Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changlong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongliu Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongliu Yu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, L., Wang, X., Meng, Q. et al. Intelligent Knee Prostheses: A Systematic Review of Control Strategies. J Bionic Eng 19, 1242–1260 (2022). https://doi.org/10.1007/s42235-022-00169-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-022-00169-1

Keywords

Search

Navigation