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Showing content from https://link.springer.com/article/10.1007/s10439-020-02617-7 below:

Ultrasound Features of Skeletal Muscle Can Predict Kinematics of Upcoming Lower-Limb Motion

References
  1. Ageberg, E., J. Flenhagen, and J. Ljung. Test-retest reliability of knee kinesthesia in healthy adults. BMC Musculoskelet. Disord. 8:57, 2007.

    Article  Google Scholar 

  2. Akhlaghi, N., C. A. Baker, M. Lahlou, H. Zafar, K. G. Murthy, H. S. Rangwala, J. Kosecka, W. M. Joiner, J. J. Pancrazio, and S. Sikdar. Real-time classification of hand motions using ultrasound imaging of forearm muscles. IEEE Trans. Biomed. Eng. 63:1687–1698, 2016.

    Article  Google Scholar 

  3. AlMohimeed, I., and Y. Ono. Ultrasound measurement of skeletal muscle contractile parameters using flexible and wearable single-element ultrasonic sensor. Sensors 20:3616, 2020.

    Article  CAS  Google Scholar 

  4. Barrack, R. L., H. B. Skinner, and S. L. Buckley. Proprioception in the anterior cruciate deficient knee. Am. J. Sports Med. 17:1–6, 1989.

    Article  CAS  Google Scholar 

  5. Begovic, H., G.-Q. Zhou, T. Li, Y. Wang, and Y.-P. Zheng. Detection of the electromechanical delay and its components during voluntary isometric contraction of the quadriceps femoris muscle. Front. Physiol. 5:494, 2014.

    Article  Google Scholar 

  6. Cooper, R. A., and R. Cooper. Rehabilitation engineering: a perspective on the past 40-years and thoughts for the future. Med. Eng. Phys. 72:3–12, 2019.

    Article  Google Scholar 

  7. Dai, C., Y. Cao, and X. Hu. Prediction of individual finger forces based on decoded motoneuron activities. Ann. Biomed. Eng. 47:1357–1368, 2019.

    Article  Google Scholar 

  8. Dhawan, A. S., B. Mukherjee, S. Patwardhan, N. Akhlaghi, G. Diao, G. Levay, R. Holley, W. M. Joiner, M. Harris-Love, and S. Sikdar. Proprioceptive sonomyographic control: a novel method for intuitive and proportional control of multiple degrees-of-freedom for individuals with upper extremity limb loss. Sci. Rep. 9:1–15, 2019.

    Article  Google Scholar 

  9. Dieterich, A. V., A. Botter, T. M. Vieira, A. Peolsson, F. Petzke, P. Davey, and D. Falla. Spatial variation and inconsistency between estimates of onset of muscle activation from EMG and ultrasound. Sci. Rep. 7:42011, 2017.

    Article  CAS  Google Scholar 

  10. Dieterich, A. V., C. M. Pickard, L. E. Deshon, G. R. Strauss, W. Gibson, P. Davey, and J. McKay. M-mode ultrasound used to detect the onset of deep muscle activity. J. Electromyogr. Kinesiol. 25:224–231, 2015.

    Article  Google Scholar 

  11. Dixon, J. R. The international conference on harmonization good clinical practice guideline. Qual. Assur. 6:65–74, 1999.

    Article  Google Scholar 

  12. Elder, G. C., K. Bradbury, and R. Roberts. Variability of fiber type distributions within human muscles. J. Appl. Physiol. 53:1473–1480, 1982.

    Article  CAS  Google Scholar 

  13. Farmer, S., B. Silver-Thorn, P. Voglewede, and S. A. Beardsley. Within-socket myoelectric prediction of continuous ankle kinematics for control of a powered transtibial prosthesis. J. Neural Eng. 11:056027, 2014.

    Article  Google Scholar 

  14. Farrell, T. R., and R. F. Weir. The optimal controller delay for myoelectric prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 15:111–118, 2007.

    Article  Google Scholar 

  15. Fluit, R., E. C. Prinsen, S. Wang, and H. van der Kooij. A comparison of control strategies in commercial and research knee prostheses. IEEE Trans. Biomed. Eng. 67:277–290, 2020.

    Article  Google Scholar 

  16. Fukunaga, T., Y. Kawakami, S. Kuno, K. Funato, and S. Fukashiro. Muscle architecture and function in humans. J. Biomech. 30:457–463, 1997.

    Article  CAS  Google Scholar 

  17. Gerardo, C. D., E. Cretu, and R. Rohling. Fabrication and testing of polymer-based capacitive micromachined ultrasound transducers for medical imaging. Microsyst. Nanoeng. 4:1–12, 2018.

    Article  CAS  Google Scholar 

  18. Grob, K. R., M. S. Kuster, S. A. Higgins, D. G. Lloyd, and H. Yata. Lack of correlation between different measurements of proprioception in the knee. J. Bone Joint Surg. 84:5, 2002.

    Article  Google Scholar 

  19. Hagberg, K., and R. Brånemark. Consequences of non-vascular trans-femoral amputation: a survey of quality of life, prosthetic use and problems. Prosthet. Orthot. Int. 25:186–194, 2001.

    Article  CAS  Google Scholar 

  20. He, J., H. Luo, J. Jia, J. T. W. Yeow, and N. Jiang. Wrist and finger gesture recognition with single-element ultrasound signals: a comparison with single-channel surface electromyogram. IEEE Trans. Biomed. Eng. 66:1277–1284, 2019.

    Article  Google Scholar 

  21. Henneman, E., G. Somjen, and D. O. Carpenter. Functional significance of cell size in spinal motoneurons. J. Neurophysiol. 28:560–580, 1965.

    Article  CAS  Google Scholar 

  22. Hodson-Tole, E. F., and J. M. Wakeling. Motor unit recruitment for dynamic tasks: current understanding and future directions. J. Comp. Physiol. B. 179:57–66, 2009.

    Article  Google Scholar 

  23. Howell D. C. Statistical Methods for Psychology. Boston: Cengage Learning, 2009.

  24. Jahanandish, M. H., N. P. Fey, and K. Hoyt. Lower limb motion estimation using ultrasound imaging: a framework for assistive device control. IEEE J. Biomed Health Inform. 23:2505–2514, 2019.

    Article  Google Scholar 

  25. Jahanandish M. H., N. P. Fey and K. Hoyt. Prediction of distal lower-limb motion using ultrasound-derived features of proximal skeletal muscle. In: 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), 2019, pp. 71–76.

  26. Jahanandish M. H., K. G. Rabe, N. P. Fey and K. Hoyt. Gait phase identification during level, incline and decline ambulation tasks using portable sonomyographic sensing. In: 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), 2019, pp. 988–993.

  27. Johnson, M. A., J. Polgar, D. Weightman, and D. Appleton. Data on the distribution of fibre types in thirty-six human muscles: an autopsy study. J. Neurol. Sci. 18:111–129, 1973.

    Article  CAS  Google Scholar 

  28. Koch P., H. Phan, M. Maass, F. Katzberg and A. Mertins. Recurrent neural network based early prediction of future hand movements. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE, 2018, pp. 4710–4713.

  29. Li J., F. Kong, X. Gao, Y. Shen and S. Gao. Prospective randomized comparison of knee stability and proprioception for posterior cruciate ligament reconstruction with autograft, hybrid graft, and γ-irradiated allograft. Arthroscopy 32: 2548-2555, 2016.

  30. Lopata, R. G. P., J. P. van Dijk, S. Pillen, M. M. Nillesen, H. Maas, J. M. Thijssen, D. F. Stegeman, and C. L. de Korte. Dynamic imaging of skeletal muscle contraction in three orthogonal directions. J. Appl. Physiol. 109:906–915, 2010.

    Article  Google Scholar 

  31. Marcucci, L., M. Bondì, G. Randazzo, C. Reggiani, A. N. Natali, and P. G. Pavan. Fibre and extracellular matrix contributions to passive forces in human skeletal muscles: an experimental based constitutive law for numerical modelling of the passive element in the classical Hill-type three element model. PLoS ONE 14:e0224232, 2019.

    Article  CAS  Google Scholar 

  32. Molenberghs, G., and G. Verbeke. A review on linear mixed models for longitudinal data, possibly subject to dropout. Stat. Model. 1:235–269, 2001.

    Article  Google Scholar 

  33. National Academies of S. and Medicine. The Promise of Assistive Technology to Enhance Activity and Work Participation. Washington: National Academies Press, 2017.

  34. Pataky, T. C. Generalized n-dimensional biomechanical field analysis using statistical parametric mapping. J. Biomech. 43:1976–1982, 2010.

    Article  Google Scholar 

  35. Penny W. D., K. J. Friston, J. T. Ashburner, S. J. Kiebel and T. E. Nichols. Statistical Parametric Mapping: The Analysis of Functional Brain Images. New York: Elsevier, 2011.

  36. Qiu, S., J. Feng, J. Xu, R. Xu, X. Zhao, P. Zhou, H. Qi, L. Zhang, and D. Ming. Sonomyography analysis on thickness of skeletal muscle during dynamic contraction induced by neuromuscular electrical stimulation: a pilot study. IEEE Trans. Neural Syst. Rehabil. Eng. 25:62–70, 2017.

    Article  Google Scholar 

  37. Rabe K. G., M. H. Jahanandish, K. Hoyt and N. P. Fey. Use of sonomyographic sensing to estimate knee angular velocity during varying modes of ambulation. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020.

  38. Rabe K. G., M. H. Jahanandish, K. Hoyt and N. P. Fey. Use of sonomyography for continuous estimation of hip, knee and ankle moments during multiple ambulation tasks. In: 2020 8th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), 2020.

  39. Rasmussen C. E. Gaussian Processes in Machine Learning. New York: Springer, 2003, pp. 63–71.

  40. Reider B., M. A. Arcand, L. H. Diehl, K. Mroczek, A. Abulencia, C. C. Stroud, M. Palm, J. Gilbertson and P. Staszak. Proprioception of the knee before and after anterior cruciate ligament reconstruction. Arthroscopy 19: 2–12, 2003.

  41. Simon, A. M., K. A. Ingraham, J. A. Spanias, A. J. Young, S. B. Finucane, E. G. Halsne, and L. J. Hargrove. Delaying ambulation mode transition decisions improves accuracy of a flexible control system for powered knee-ankle prosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 25:1164–1171, 2017.

    Article  Google Scholar 

  42. Smith, L. H., L. J. Hargrove, B. A. Lock, and T. A. Kuiken. Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay. IEEE Trans. Neural Syst. Rehabil. Eng. 19:186–192, 2011.

    Article  Google Scholar 

  43. Tidball J. and T. Daniel. Myotendinous junctions of tonic muscle cells: structure and loading. Cell Tissue Res 245:1986.

  44. Voloshina A. S. and S. H. Collins. Lower limb active prosthetic systems—Overview. In: Wearable Robotics, edited by J. Rosen and P. W. Ferguson. New York: Academic Press, 2020, pp. 469–486.

  45. Wakeling, J. M. Motor units are recruited in a task-dependent fashion during locomotion. J. Exp. Biol. 207:3883–3890, 2004.

    Article  Google Scholar 

  46. Wakeling, J. M., K. Uehli, and A. I. Rozitis. Muscle fibre recruitment can respond to the mechanics of the muscle contraction. J. R. Soc. Interface 3:533–544, 2006.

    Article  Google Scholar 

  47. Wei, P.-N., R. Xie, R. Tang, C. Li, J. Kim, and M. Wu. sEMG based gait phase recognition for children with spastic cerebral palsy. Ann. Biomed. Eng. 47:223–230, 2019.

    Article  Google Scholar 

  48. Wentink, E. C., S. I. Beijen, H. J. Hermens, J. S. Rietman, and P. H. Veltink. Intention detection of gait initiation using EMG and kinematic data. Gait Posture 37:223–228, 2013.

    Article  CAS  Google Scholar 

  49. Wentink, E. C., V. G. H. Schut, E. C. Prinsen, J. S. Rietman, and P. H. Veltink. Detection of the onset of gait initiation using kinematic sensors and EMG in transfemoral amputees. Gait Posture 39:391–396, 2014.

    Article  CAS  Google Scholar 

  50. World Health Organization. World report on disability 2011. World Health Organization, 2011.

  51. Yang X., J. Yan and H. Liu. Comparative analysis of wearable a-mode ultrasound and sEMG for muscle-computer interface. IEEE Trans. Biomed. Eng.1–1, 2019.

  52. Zhang F., M. Liu and H. Huang. Investigation of timing to switch control mode in powered knee prostheses during task transitions. PLoS ONE 10: 2015.

  53. Zhang, Q., K. Kim, and N. Sharma. Prediction of ankle dorsiflexion moment by combined ultrasound sonography and electromyography. IEEE Trans. Neural Syst. Rehabil. Eng. 28:318–327, 2020.

    Article  Google Scholar 

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