Tytuł artykułu
Autorzy
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Rehabilitation of post stroke patients with upper extremity motor deficits is typically focused on relearning of motor abilities and functionalities requiring interaction with physiotherapists and/or rehabilitation robots. In a point-to-point movement training, the trajectories are usually arbitrarily determined without considering the motor impairment of the individual. In this paper, we used an optimal control model based on arm dynamics enabling also incorporation of muscle functioning constraints (i.e. simulation of muscle tightness) to find the optimal trajectories for planar arm reaching movements. First, we tested ability of the minimum joint torque cost function to replicate the trajectories obtained in previously published experimental trials done by neurologically intact subjects, and second, we predicted the optimal trajectories when muscle constraints were modeled. The resulting optimal trajectories show considerable similarity as compared to the experimental data, while on the other hand, the muscle constraints play a major role in determination of the optimal trajectories for stroke rehabilitation.
Wydawca
Czasopismo
Rocznik
Tom
Strony
106--117
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wykr.
Twórcy
autor
- University Rehabilitation Institute, Republic of Slovenia
autor
- University Rehabilitation Institute, Republic of Slovenia
Bibliografia
- [1] Abend W, Bizzi E, Morasso P. Human arm trajectory formation. Brain 1982; 105: 331–48.
- [2] Morasso P. Spatial control of arm movements. Exp Brain Res 1981; 42: 223–7.
- [3] Flash T, Hogan N. The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 1985; 5: 1688–703.
- [4] Suzuki M, Yamazaki Y, Mizuno N, Matsunami K. Trajectory formation of the center-of-mass of the arm during reaching movements. Neuroscience 1997; 76: 597–610.
- [5] Nakano E, Imamizu H, Osu R, Uno Y, Gomi H, Yoshioka T, et al. Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. J Neurophysiol 1999; 81: 2140–55.
- [6] Biess A, Nagurka M, Flash T. Simulating discrete and rhythmic multi-joint human arm movements by optimization of nonlinear performance indices. Biol Cybern 2006; 95: 31–53.
- [7] Uno Y, Kawato M, Suzuki R. Formation and control of optimal trajectory in human multijoint arm movement. Biol Cybern 1989; 61: 89–101.
- [8] Wada Y, Kaneko Y, Nakano E, Osu R, Kawato M. Quantitative examinations for multi joint arm trajectory planning – using a robust calculation algorithm of the minimum commanded torque change trajectory. Neural Network 2001; 14: 381–93.
- [9] Nagasaki H. Asymmetric velocity and acceleration profiles of human arm movements. Exp Brain Res 1989; 74: 319–26.
- [10] Kim HK, Carmena JM, Biggs SJ, Hanson TL, Nicolelis MAL, Srinivasan MA. The muscle activation method: an approach to impedance control of brain–machine interfaces through a musculoskeletal model of the arm. Biomed Eng (NY) 2007; 54: 1520–9.
- [11] Chandler RF, Clauser CE, McConville JT, Reynolds HM, Young JW. Investigation of inertial properties of the human body. AMRL Technical Report; 1975. pp. 74–137.
- [12] Jensen RK. Changes in segment inertia proportions between four and twenty years. J Biomech 1989; 22: 529–36.
- [13] Hinricks RN. Regression equations to predict segmental moments of inertia from anthropometric measurements: an extension of the data of Chandler et al. (1975). J Biomech 1985; 18: 621–4.
- [14] Zatsiorsky VM, Seluyanov VN. The mass and inertia characteristics of the main segments of the human body. Biomechanics 1983; VIII-B: 1152–9.
- [15] Tahara K, Luo Z, Arimoto S, Kino H. Sensory-motor control of a muscle redundant arm for reaching movements – convergence analysis and gravity compensation. In: Intelligent Robots and Systems (IROS). 2005. pp. 517–22.
- [16] Bryson AE, Ho YC. Applied optimal control: optimization, estimation, and control. New York: Taylor & Francis; 1975.
- [17] Bernabucci, Conforto S, Capozza M, Accornero N, Schmid M, D'Alessio T. A biologically inspired neural network controller for ballistic arm movements. J Neuroeng Rehabil 2007; 4: 33.
- [18] Osu R, Uno Y, Koike Y, Kawato M. Possible explanations trajectory curvature in multijoint arm movements. J Exp Psychol Hum Percept Perform 1997;23:890–913.
- [19] Ohta MM, Svinin ZW, Luo S, Hosoe R, Laboissiere. Optimal trajectory formation of constrained human arm reaching movements. Biol Cybern 2004; 91: 23–36.
- [20] Wolpert DM, Ghahramani Z, Jordan MI. Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study. Exp Brain Res 1995; 103: 460–70.
- [21] Nelson WL. Physical principles for economies of skilled movements. Biol Cybern 1983; 46: 135–47.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-87aa7da1-50be-4fa1-a2f8-4e95e3826266