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Feedback gain scaling quantifies postural abnormality of Patients with Parkinson’s disease

Human Balance Research. Feedback gain scaling quantifies postural abnormality of Patients with Parkinson’s disease. Quantification of postural balance. Seyoung Kim, PhD.

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Feedback gain scaling quantifies postural abnormality of Patients with Parkinson’s disease

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  1. Human Balance Research • Feedback gain scaling quantifies postural abnormality of Patients with Parkinson’s disease • Quantification of postural balance • Seyoung Kim, PhD Seyoung Kim, Fay B. Horak, Patricia Carlson-Kuhta and Sukyung Park, “Postural Feedback Scaling Deficits in Parkinson’s disease”, Journal of Neurophysiology, Vol.102: 2910-2920, 2009

  2. Research background [ http://www.kerals.com ] • Scientific research into postural balance is required to provide solutions for fall prevention. • CoP does not explain the abnormality of elderly or patients. CoP RMS amplitude of Side by side / 45° • Elderly: 0.48 / 0.41* • PD: 0.45 / 0.34 • Outcome measure is not enough to represent postural balance. • e.g. Parkinson patients 1/3 of the elderly has a fall every year [www.mw.go.kr, 2008]. The medical expenses for the aged people have been steadily increased [www.nhic.or.kr, 2005]. [ Termoz et al., Gait & posture (2008) ]

  3. Systematic analysis with feedback control model [ Park et al. 2004 ] • Can this model be a good measurement for postural balance ?

  4. Research objective Normal PD Feedback gain K Perturbation Ankle  Hip strategy @ Neurological Sciences Institute • Objective : • We examined whether the “feedback gain and its scaling” may explain the deteriorated control adjustability of Parkinson patients. • Hypothesis : • The deteriorated control adjustability of PD patients might be reflected in feedback gain scaling.

  5. Experimental procedure @ Neurological Sciences Institute • Measurement • Joint angle captured by Motion capture system (Santa Rosa, California) • 200 Hz sampling rate • 5th Butterworth low-pass filter with cutoff frequency of 10 Hz • Moment & Ground reaction forces measured by a custom force plate • 400 Hz sampling rate • 5th Butterworth low-pass filter with cutoff frequency of 30 Hz • Subjects • Seven healthy elderly (63±7y) • Seven age-matched patients with PD (UPDRS: 23.8±10.2) • Protocols • Backward perturbation • (3-15cm in 275ms) X 5set * Activities-specific Balance Confidence scale

  6. Biomechanical model for human postural control K 2segment inverted pendulum model in sagittal plane Linearized Joint torques were calculated by inverse dynamics [ Kuo,1998 ]. Full-state feedback control model that represent CNS control Gain parameters calculated by optimization

  7. Calculation of feedback gains by model simulation [ Exp. data and Simulation ] Elderly : avg. 0.84±0.038 PD : avg. 0.80±0.036

  8. Results and Discussion Postural response to support translation Postural feedback gain and its scaling

  9. Postural response to support translation Normal Patient • CoP does not explain the abnormality of elderly or patients. • Can feedback gain diagnose the abnormality of postural balance ? [ S. Kim etal., Journal of Neurophysiology (2009) ] @ Neurological Sciences Institute

  10. Postural feedback gain scaling of elderly and PD [ S. Kim et al., Journal of Neurophysiology (2009) ]

  11. Conclusion • Postural adjustments in responses to increased perturbation magnitudes were quantified by the scaling of the feedback control gain. • The PD patients showed significantly different gain and gain scaling behavior from the healthy elderly. • The PD subjects showed much smaller ankle gain with low ankle gain scaling and a larger hip gain with slightly greater hip gain scaling. • Subjects with PD have significantly larger hip feedback gains than age-matched control subjects, leading to stiffer hip joints so that overall postural sway resembles an inverted pendulum with significantly smaller hip joint motion.

  12. Model limitations • Current model does not exclude the possibility of the nervous system’s selection of preprogrammed responses. • The violation of constraint followed by the initiation of step response was not explicitly modeled with current model. • Subjects were instructed to recover their upright posture without violating the flat-feet constraints. However, subjects would rather step when they encounter unexpected perturbation in real situation. • The current model misses various nonlinear, temporal aspects of postural physiology. • Dynamics of muscle mechanics • Short-latency responses from reflexes • Long-latencies from long-loop feedback

  13. Human Balance Research • Different postural response to support translation • between the young and the elderly • Quantification of postural balance • Seyoung Kim, PhD Seyoung Kim, Fay B. Horak, Patricia Carlson-Kuhta and Sukyung Park, “Postural Feedback Scaling Deficits in Parkinson’s disease”, Journal of Neurophysiology, Vol.102: 2910-2920, 2009

  14. Peak joint kinematics and kinetics [ S. Kim etal., Journal of Neurophysiology (2009) ]

  15. Feedback gain scaling of young and elderly [ S. Kim etal., Journal of Neurophysiology (2009) ]

  16. Parameter study : Peak joint angle as a function of body inertia and feedback gain Avg. upper body mass Young : 39.84±7.70 kg Elderly : 63.42±15.84 kg [ S. Kim etal., Journal of Neurophysiology (2009) ]

  17. Conclusion The differences in the measurements of joint motion and torques between the young and elderly groups may be attributed to altered system parameters such as feedback gains and body mass distributions and do not necessarily indicate changes in postural strategy. Smaller maximum allowable ankle joint torque in the elderly may be due to the tendency of initial forward leaning at their preferred upright posture.

  18. Acknowledgement • Funds • Postural control study was supported by a Basic Research Fund of the Korea Institute of Machinery and Materials, the second stage of the Brain Korea 21 Project, and a National Institute on Aging. • Walking research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (#2010-0013306) and the Unmanned Technology Research Center (UTRC) at the Korea Advanced Institute of Science and Technology (KAIST), originally funded by DAPA, ADD. • Collaborators • Fay B. Horak (Oregon Health & Science University) • Patricia Carlson-Kuhta (Oregon Health & Science University) • Chris G. Atkeson (Carnegie Mellon University)

  19. Special thanks to…

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