Pressure based MRI-compatible muscle fascicle length and joint angle estimation

Journal of NeuroEngineering and Rehabilitation - Tập 17 - Trang 1-11 - 2020
Hyungeun Song1,2, Erica Israel1,3, Shriya Srinivasan1,2, Hugh Herr1,3
1Center for Extreme Bionics, Massachusetts Institute of Technology (MIT) Media Lab, Cambridge, USA
2Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, USA
3Department of Media Arts and Sciences, MIT, Cambridge, USA

Tóm tắt

Functional magnetic resonance imaging (fMRI) provides critical information about the neurophysiology of the central nervous systems (CNS), posing clinical significance for the understanding of neuropathologies and advancement of rehabilitation. Typical fMRI study designs include subjects performing designed motor tasks within specific time frames, in which fMRI data are then analyzed by assuming that observed functional brain activations correspond to the designed tasks. Therefore, developing MRI-compatible sensors that enable real-time monitoring of subjects’ task performances would allow for highly accurate fMRI studies. While several MRI-compatible sensors have been developed, none have demonstrated the ability to measure individual muscle fascicle length during fMRI, which could help uncover the complexities of the peripheral and central nervous systems. Furthermore, previous MRI-compatible sensors have been focused on biologically intact populations, limiting accessibility to populations such as those who have undergone amputation. We propose a lightweight, low-cost, skin impedance-insensitive pressure-based muscular motion sensor (pMMS) that provides reliable estimates of muscle fascicle length and joint angle. The muscular motions are captured through measured pressure changes in an air pocket wrapped around the muscle of interest, corresponding to its muscular motion. The muscle fascicle length and joint angle are then estimated from the measured pressure changes based on the proposed muscle-skin-sensor interaction dynamics. Furthermore, we explore an integration method of multiple pMMS systems to expand the sensor capacity of estimating muscle fascicle length and joint angle. Ultrasound imaging paired with joint encoder measurements are utilized to assess pMMS estimation accuracy of muscle fascicle length in the tibialis anterior (TA) and ankle joint angle, respectively, of five biologically intact subjects. We found that a single pMMS sufficiently provides robust and accurate estimations of TA muscle fascicle length and ankle joint angle during dorsiflexion at various speeds and amplitudes. Further, differential pressure readings from two pMMSs, in which each pMMS were proximally and distally placed, were able to mitigate errors due to perturbations, expanding pMMS capacity for muscle fascicle length and ankle joint angle estimation during the full range of plantar flexion and dorsiflexion. Our results from this study demonstrate the feasibility of the pMMS system to further be incorporated in fMRI settings for real-time monitoring of subjects’ task performances, allowing sophisticated fMRI study designs.

Tài liệu tham khảo

Huettel SA. Event-related fmri in cognition. Nueroimage. 2012; 62(2):1152–6. Ehrsson H, Fagergren A, Jonsson T, Westling G, Johansson R, Forssberg H. Cortical activity in precision-versus power-grip tasks: an fmri study. J Neurophysiol. 2000; 83(1):528–36. Mehta J, Verber M, Wieser J, Schmit B, Schindler-lvens S. A novel technique for examining human brain activity associated with pedaling using fmri. J Neurosci Methods. 2009; 179(2):230–9. Elhawary H, Zivanovic A, Rea M, Davies B, Besant C, McRobbie D, Desouza N, Young I, Lamperth M. A modular approach to mri-compatible robotics. IEEE Eng Med Biol Mag. 2008; 27(3):35–41. Tsekos N, Christoforou E, Ozcan A. A general-purpose mr-compatible robotic system: Implementation and image guidance for performing minimally invasive interventions. IEEE Eng Med Biol Mag. 2008; 27(3):51–8. Hidler J, Hodics T, Xu B, Dobkin B, Cohen L. Mr compatible force sensing system for real-time monitoring of wrist moments during fmri testing. J Neurosci Methods. 2006; 155(2):300–7. Tada M, Kanade T. Design of an mr-compatible three-axis force sensor. In: Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS). Edmonton: IEEE: 2005. p. 3505–10. Chapuis D, Gassert R, Sache L, Burdet E, Bleuler H. Design of a simple mri/fmri compatible force/torque sensor. In: Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS). Sendai: IEEE: 2004. p. 2593–9. Vigaru B, Sulzer J, Gassert R. Design and evaluation of a cable-driven fmri-compatible haptic interface to investigate precision grip control. IEEE Trans Haptics. 2016; 9(1):20–32. Rossignol S, Dubuc R, Gossard J. Dynamic sensorimotor interactions in locomotion. Physiol Rev. 2006; 86:89–154. Kroger S. Proprioceptoin 2.0: novel functions for muscle spindles. Neuromuscul Disord. 2018; 31(5):593–8. Biewener AA, Roberts TJ. Muscle and tendon contributions to force, work, and elastic energy savings: a comparative perspective. Exerc Sport Sci Rev. 2000; 28(3):99–107. Taylor CR, Abramson HG, Herr H. Low-latency tracking of multiple permanent magnets. IEEE Sensors J. 2019; 19(23):11458–68. Sikdar S, Wei Q, Cortes N. Dynamic ultrasound imaging applications to quantify musculoskeletal function. Exerc Sport Sci Rev. 2014; 42(3):126–35. DeJong AF, Mangum LC, Hertel J. Gluteus medius activity during gait is altered in individuals with chronic ankle instability: An ultrasound imaging study. J Gaitpost. 2019; 71:7–13. Franchi MV, Raiteri BJ, Longo S, Sinha S, Narici MV, Csapo R. Muscle architecture assessment: strengths, shortcomings and new frontiers of in vivo imaging techniques. J Ultrasmedbio. 2018; 44(12):2492–504. Bae G, Song J, Kim B. Imitation of human motion based on variable stiffness actuator and muscle stiffness sensor. In: Proc. IEEE/ASME Int. Conf. Intell. Mechatron. (AIM). Wollongong: IEEE: 2013. p. 1016–1020. Bo Z, Sundholm M, Cheng J, Cruz H, Lukowicz P. Measuring muscle activities during gym exercises with textile pressure mapping sensors. Pervasive Mob Comput. 2017; 38:331–45. Belbasis A, Fuss FK. Muscle performance investigated with a novel smart compression garment based on pressure sensor force myography and its validation against emg. Front Physiol. 2018; 9:1–13. Huxley HE. The mechanism of muscular contraction. Science. 1969; 164(3886):1356–65. Narici M, Binzoni T, Hiltbrand E, Fasel J, Terrier F, Cerretelli P. In vivo human gastrocnemius architecture with changing joint angle at rest and during graded isometric contraction. J Physiol. 1996; 496(1):287–97. Kong K, Tomizuka M. A gait monitoring system based on air pressure sensors embedded in a shoe. IEEE/ASME Trans Mechatron. 2009; 14(3):358–70. Dowling NE. Mechanical behavior of materials: engineering methods for deformation, fracture, and fatigue. 2nd. Englewood Cliffs: Pearson-Prentice Hall; 1999. Boyer G, Laquieze L, Le Bot A, Laquieze S, Zahouani H. Dynamic indentation on human skin in vivo: ageing effects. Skin Res Technol. 2009; 15(1):55–67. Khatyr F, Imberdis C, Vescovo P, Varchon D, Lagarde J. Model of the viscoelastic behaviour of skin in vivo and study of anisotropy. Skin Res Technol. 2004; 10(2):96–103. Hunter I, Korenberg M. The identification of nonlinear biological systems: Wiener and hammerstein cascade models. Biol Cybern. 1986; 55(2-3):135–44. Sun S, Deng Z. Multi-sensor optimal information fusion kalman filter. Automatica. 2004; 40(6):1017–23. Stover J, Hall D, Gibson R. A fuzzy-logic architecture for autonomous multisensor data fusion. IEEE Trans Ind Electron. 1996; 43(3):1017–23.