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21.9 Balance control during an arm raising movementin bipedal stance: Which biomechanical factor is controlled?
Chapter 21. Modeling approach of posture and gait I~
Sensorial input effects in postural control
G. Dietrich 1, M. Gilles 2, A.M. Wing 3. 1LAMA, University of Paris
5, eWorking Life Laboratory, INRS, Nancy, France," 3Behavioural Brain Sciences' Centre, Birmingham, UK Introduction: Since 1992 [1,2] effects of haptic input from the upper
$139
[2] Chang CC, Brown DR, Bloswick DS, Hsiang SM. Biomechanical simulation of manual lifting using spacetime optimization. J Biomech 2001; 34:527 532.
~
Nonlinear behaviours in human posture
R Fourcade 1, B.G. Bardy 1'2, S. Roudeix 1. 1Center for Research
limb were shown on posture and balance. In 2002 [3] a conceptual model was presented for studying the contribution of each leg to sideways stability of a four-link biped. The aim of this study is to analyze the effects of varying the height of the light touch contact point on sway during normal stance and to evaluate the contribution of biomechanical constraints by using an extended version of the previous model. Methods: Sway was measured during four conditions: i)Light touch support and hand raised up at shoulder level, ii) Light touch support and hand down at elbow height, iii) No light touch support and hand raised at shoulder level, and iv) No light touch support and hand down at elbow height. Recorded data were compared with simulations using a biomechanical model. Results: As all previous studies, we observed cross-correlation between postural sway and hand movement during the control situation. In the simulation similar cross-correlation was observed with maximum at a lag that depended on the stiffness ratio between the two controllers responsible for postural sway and arm position control. Discussion and conclusion: On the basis of these findings we suggest the hypothesis that the use of light touch in postural stabilisation uses two independent simple PD or PID controllers to achieve a functional "coordination unit". Coupling between controllers is achieved through sensorial information.
We present a mechanical model capturing the phase transitions observed in human posture [1], consisted of a double-inverted pendulum. The human body is modelled with two indeformable segments, one representing the head-trunk system rotating around the hips, the other representing the thigh-leg system rotating around the ankles. The oscillatory movement of the body is created by a torque, of constant amplitude, acting at the ankles. In order to keep constant the amplitude of the upper segment, a Van der Pol non-linear oscillator between the head and the vertical axis has been added. Simulations of the model for different movement frequencies (transition simulations) reveal two stable oscillation modes: an "in phase" mode for low frequencies (ankle-hip relative phase close to 0°), and an "anti-phase" mode for high frequencies (relative phase close to 180°). Simulations also show an hysteresis effect, with the between-modes frequency transition being higher when frequency is increased and lower when frequency is decreased. This mechanical model emphasizes the non-linear behaviour of human posture, and reveals that the postural system exhibits typical signatures are self-organized systems.
References
References
[1] Lackner JR. J. Vest Res 1992; 2:307 322. [2] Jeka JJ and Lackner JR. Exp. Brain Res 1994; 103:267 276. [3] Dietrich G, Wing A, Gilles M, Nimmo-Smith I. J. Appl. Biomech. 2002; 18:99 109.
[1] Bardy B.G., Oullier O., Bootsma R.J., Stoffregen T.A. Dynamics of human postural transitions. J Exp Psychol Hum Percept 2002; 28(3): 499 514.
I•
Balance control during an arm raising movement in bipedal stance: which biomechanical factor is controlled?
M. Ferry, L. Martin. Laboratoire Sport et Performance Motrice;
Universitd Grenoble 1- UFR-STAPS, France Introduction: With computational resources available today, large-
scale models of the body and techniques simulating human behavior [1,2] can provide data to support hypotheses on how musculoskeletal systems interact to produce movements. Methods: we performed an analysis of kinematics and dynamical aspects of arm raising movements by combining experimental work, biomechanical model of the body and dynamic optimization. We then performed an analysis based on computer simulations. Since keeping the CoP and the projection of the CoM inside the support area is essential for equilibrium, we modeled an arm raising movement where displacement of one or the other variable is limited. Results: The comparison between experimental and simulated joint kinematics showed that the minimum torque change model yielded realistic trajectories. The results show that: a) the choice of the regulated variable influences the strategy adopted by the system, and b) the system was not able to regulate the CoM for very fast movements without compromising its balance. Discussion and conclusion: The system is able to maintain balance while raising the arm by only controlling the CoP. This may be done mainly by using hip mechanisms and controlling net ankle torque.
References [1] Flash T, Hogan N. The co-ordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 1985; 5:1688 1703.
in Sport Sciences, University of Paris-Sud XL Orsay, 2Institut Universitaire de France, France
I•
Velocity-dependent stability of gait for patients with balance impairments can be explained by biomechanical stabilization
W. Ilg, H. Golla, M.A. Giese. Laboratory for Action Representation
and Learning~Department of Cognitive Neurology, Hertie Institute for Clinical Brain Research, University of Tiibingen, Germany Introduction: Recent studies report velocity-dependent stability of walking and running movements for patients with balance impairments caused by vestibular neuritis [1] or cerebellar stroke. This phenomenon has been explained by circuits in spinal cord, which take over control for higher velocities and inhibit vestibular and cerebellar inputs. However, this explanation leaves open why running could be less dependent from balance control than slow walking. We provide an alternative explanation that is based on the dynamical stability of bipedal locomotion. Methods: We present data from a cerebellar patient performing tandem walk. Kinematic analysis reveals less missteps, and significantly decreased lateral sway for higher walking speeds. In order to investigate possible biomechanical influences we studied the velocitydependent stability of a 3D passive walker, a simplified bipedal model that can walk down a slope without external control [2]. Results: We examined the robustness of this model against lateral disturbances as a function of velocity, providing no lateral control. The amount of lateral perturbations tolerated without loss of stability is substantially larger for higher walking speeds. Discussion and Conclusion: These results suggest that biomechanics provides an explanation for the increased stability in gait for higher velocities. This explains why running could be less dependent from vestibular information and therefore might be controlled mainly by circuits in the spinal cord. This hypothesis is consistent with a study [3] that indicates increased demands for postural control in slow walking.
Sensorial input effects in postural control
G. Dietrich 1, M. Gilles 2, A.M. Wing 3. 1LAMA, University of Paris
5, eWorking Life Laboratory, INRS, Nancy, France," 3Behavioural Brain Sciences' Centre, Birmingham, UK Introduction: Since 1992 [1,2] effects of haptic input from the upper
$139
[2] Chang CC, Brown DR, Bloswick DS, Hsiang SM. Biomechanical simulation of manual lifting using spacetime optimization. J Biomech 2001; 34:527 532.
~
Nonlinear behaviours in human posture
R Fourcade 1, B.G. Bardy 1'2, S. Roudeix 1. 1Center for Research
limb were shown on posture and balance. In 2002 [3] a conceptual model was presented for studying the contribution of each leg to sideways stability of a four-link biped. The aim of this study is to analyze the effects of varying the height of the light touch contact point on sway during normal stance and to evaluate the contribution of biomechanical constraints by using an extended version of the previous model. Methods: Sway was measured during four conditions: i)Light touch support and hand raised up at shoulder level, ii) Light touch support and hand down at elbow height, iii) No light touch support and hand raised at shoulder level, and iv) No light touch support and hand down at elbow height. Recorded data were compared with simulations using a biomechanical model. Results: As all previous studies, we observed cross-correlation between postural sway and hand movement during the control situation. In the simulation similar cross-correlation was observed with maximum at a lag that depended on the stiffness ratio between the two controllers responsible for postural sway and arm position control. Discussion and conclusion: On the basis of these findings we suggest the hypothesis that the use of light touch in postural stabilisation uses two independent simple PD or PID controllers to achieve a functional "coordination unit". Coupling between controllers is achieved through sensorial information.
We present a mechanical model capturing the phase transitions observed in human posture [1], consisted of a double-inverted pendulum. The human body is modelled with two indeformable segments, one representing the head-trunk system rotating around the hips, the other representing the thigh-leg system rotating around the ankles. The oscillatory movement of the body is created by a torque, of constant amplitude, acting at the ankles. In order to keep constant the amplitude of the upper segment, a Van der Pol non-linear oscillator between the head and the vertical axis has been added. Simulations of the model for different movement frequencies (transition simulations) reveal two stable oscillation modes: an "in phase" mode for low frequencies (ankle-hip relative phase close to 0°), and an "anti-phase" mode for high frequencies (relative phase close to 180°). Simulations also show an hysteresis effect, with the between-modes frequency transition being higher when frequency is increased and lower when frequency is decreased. This mechanical model emphasizes the non-linear behaviour of human posture, and reveals that the postural system exhibits typical signatures are self-organized systems.
References
References
[1] Lackner JR. J. Vest Res 1992; 2:307 322. [2] Jeka JJ and Lackner JR. Exp. Brain Res 1994; 103:267 276. [3] Dietrich G, Wing A, Gilles M, Nimmo-Smith I. J. Appl. Biomech. 2002; 18:99 109.
[1] Bardy B.G., Oullier O., Bootsma R.J., Stoffregen T.A. Dynamics of human postural transitions. J Exp Psychol Hum Percept 2002; 28(3): 499 514.
I•
Balance control during an arm raising movement in bipedal stance: which biomechanical factor is controlled?
M. Ferry, L. Martin. Laboratoire Sport et Performance Motrice;
Universitd Grenoble 1- UFR-STAPS, France Introduction: With computational resources available today, large-
scale models of the body and techniques simulating human behavior [1,2] can provide data to support hypotheses on how musculoskeletal systems interact to produce movements. Methods: we performed an analysis of kinematics and dynamical aspects of arm raising movements by combining experimental work, biomechanical model of the body and dynamic optimization. We then performed an analysis based on computer simulations. Since keeping the CoP and the projection of the CoM inside the support area is essential for equilibrium, we modeled an arm raising movement where displacement of one or the other variable is limited. Results: The comparison between experimental and simulated joint kinematics showed that the minimum torque change model yielded realistic trajectories. The results show that: a) the choice of the regulated variable influences the strategy adopted by the system, and b) the system was not able to regulate the CoM for very fast movements without compromising its balance. Discussion and conclusion: The system is able to maintain balance while raising the arm by only controlling the CoP. This may be done mainly by using hip mechanisms and controlling net ankle torque.
References [1] Flash T, Hogan N. The co-ordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 1985; 5:1688 1703.
in Sport Sciences, University of Paris-Sud XL Orsay, 2Institut Universitaire de France, France
I•
Velocity-dependent stability of gait for patients with balance impairments can be explained by biomechanical stabilization
W. Ilg, H. Golla, M.A. Giese. Laboratory for Action Representation
and Learning~Department of Cognitive Neurology, Hertie Institute for Clinical Brain Research, University of Tiibingen, Germany Introduction: Recent studies report velocity-dependent stability of walking and running movements for patients with balance impairments caused by vestibular neuritis [1] or cerebellar stroke. This phenomenon has been explained by circuits in spinal cord, which take over control for higher velocities and inhibit vestibular and cerebellar inputs. However, this explanation leaves open why running could be less dependent from balance control than slow walking. We provide an alternative explanation that is based on the dynamical stability of bipedal locomotion. Methods: We present data from a cerebellar patient performing tandem walk. Kinematic analysis reveals less missteps, and significantly decreased lateral sway for higher walking speeds. In order to investigate possible biomechanical influences we studied the velocitydependent stability of a 3D passive walker, a simplified bipedal model that can walk down a slope without external control [2]. Results: We examined the robustness of this model against lateral disturbances as a function of velocity, providing no lateral control. The amount of lateral perturbations tolerated without loss of stability is substantially larger for higher walking speeds. Discussion and Conclusion: These results suggest that biomechanics provides an explanation for the increased stability in gait for higher velocities. This explains why running could be less dependent from vestibular information and therefore might be controlled mainly by circuits in the spinal cord. This hypothesis is consistent with a study [3] that indicates increased demands for postural control in slow walking.