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

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

Model based balance & gait analysis Systematic analysis with feedback control model 3 [ Park et al ] Can this model be a good measurement for postural balance ?

Model based balance & gait analysis 4 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. Research Neurological Sciences Institute Perturbation Feedback gain K Ankle  Hip strategy Normal PD

Model based balance & gait analysis Experimental procedure Neurological Sciences Institute 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 SubjectAgeGender Height (cm) Weight (kg) Duration of PD (yrs) UPDRS Total Dyskinesia Score Side most affected H&Y Score ABC* Sub0153M R293 Sub0266M R291 Sub0365F R296 Sub0481M R395 Sub0567M R296 Sub0656M L1.599 Sub0765M18577N/A26.50R283 * Activities-specific Balance Confidence scale Measurement – Joint angle captured by Motion capture system (Santa Rosa, California)  200 Hz sampling rate  5 th Butterworth low-pass filter with cutoff frequency of 10 Hz – Moment & Ground reaction forces measured by a custom force plate  400 Hz sampling rate  5 th Butterworth low-pass filter with cutoff frequency of 30 Hz

Model based balance & gait Neurological Sciences Institute Backward perturbation profile: 7 conditions – (3-15cm in 275ms duration) X 5 times, in random order Instructed not to lift their heels off the ground while keeping balance 3.0cm : 0.12 m/s 4.5cm : 0.18 m/s 6.0cm : 0.24 m/s 7.5cm : 0.30 m/s 9.0cm : 0.36 m/s 12.0cm : 0.47 m/s 15.0cm : 0.59 m/s Experimental protocol 6

Model based balance & gait analysis Equipment & Measurement Motion capture system – Kinematics Joint angles – 8 IR cameras – 22 optical markers – Sampling frequency : 60Hz Custom made force plate – Ground reaction forces / moment Fx, Fy, Fz, Mx, My, Mz – Sampling frequency : 480Hz 7 [ Motion capture system, Santa Rosa ] [ Custom made dual force plate ] 7

Model based balance & gait analysis 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 Biomechanical model for human postural control 8 K

Model based balance & gait analysis Calculation of feedback gains by model simulation [ Exp. data and Simulation ] Elderly : avg. 0.84±0.038 PD : avg. 0.80±

Model based balance & gait analysis 10 Postural response to support translation Postural feedback gain and its scaling Results and Discussion

Model based balance & gait analysis Postural response to support translation 11 Normal Neurological Sciences Institute [ S. Kim et al., Journal of Neurophysiology (2009) ] CoP does not explain the abnormality of elderly or patients. Can feedback gain diagnose the abnormality of postural balance ?

Model based balance & gait analysis Postural feedback gain scaling of elderly and PD 12 [ S. Kim et al., Journal of Neurophysiology (2009) ]

Model based balance & gait analysis Complex plane trajectories of closed loop eigenvalues for gain variation: Elderly & PD 13

Model based balance & gait analysis 14 Linear regression slopes (feedback gain scaling) quantify how flexibly subjects change their postural strategy from ankle to hip and a formation of intercepts (feedback gains) describes the postural system’s parameter set for postural control. Quantification of postural balance Slope and intercept of scaled k 11 k 12 k 13 k 14 Elderly / / / / PD / / / / 0.13 Slope and intercept of scaled k 21 k 22 k 23 k 24 Elderly0.002 / / / / PD0.03 / / / / Linear regression slope / intercept

Model based balance & gait analysis 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. Conclusion 15

Model based balance & gait analysis 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 16

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

Model based balance & gait analysis 18 Peak joint kinematics and kinetics [ S. Kim et al., Journal of Neurophysiology (2009) ]

Model based balance & gait analysis 19 Feedback gain scaling of young and elderly [ S. Kim et al., Journal of Neurophysiology (2009) ]

Model based balance & gait analysis 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 20 [ S. Kim et al., Journal of Neurophysiology (2009) ]

Model based balance & gait analysis 21 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. Conclusion

Model based balance & gait analysis 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 (# ) 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) Acknowledgement 22

Model based balance & gait analysis 23 Special thanks to…

Model based balance & gait analysis 24