Humans sway. The significance of our research lies in our belief that normal postural detection and controller mechanisms are best studied by perturbation """"""""probes"""""""" that lay within the range of normal sway, rather than larger fall-initating pertubations. Thus, our approach is a significant new way to explore postural control in the elderly and those with diabetes. With our low-virbration Sliding Linear Investigative Platform for Analyzing Lower Limb Stability with Synced Tracking EMG and Pressure measurements (SLIP-FALLS-STEPm), our lab studies the psychophysical, biomechanical, and neurophysical responses to short horizontal moves that are at the edge of perception. We found negative-power-law, psychophysical trading relationships between acceleration detection threshold and forward displacement that vary predictably with age and the presence of diabetes. Pilot studies indicate that AP Center-of -Pressure, lower limb EMGs and foot sole pressure distribution are affected by supra-threshold but not sub-threshold perturbations. A Legacy Dataset of >40,000 observations is available for further analysis. Various analytical tools (phase-locked loops, analytic inverted pendulum equations, chaotic systems, fuzzy systems, artifical neural networks, and combinations of these) will look for relationships between detected moves and resultant reactions in measured variables. Further, the concept that initial conditions are important (i.e., the position and velocity of the COP just before a move) will be investigated, with the training output being a correct or incorrect detection. SLIP-FALLS- STEPm can entrain a subject to a sway pattern by oscillating the platform at a frequency near the subject's normal sway frequency and perturb at various phases of the sway cycle. Given the wealth of multi-dimen- sional Legacy data on >150 subjects, and how the data change with age and the presences of diabetes, given tantalizing hints at what control schemes might be used, and how these schemes seem to be affected by age, diabetes, obesity or peripheral neuropathy, we will proceed further by: 1) Comprehensively analyzing the existing Legacy date;2) Entraining the sway pattern of subjects so that the pre-perturbation initial conditions can be better specified;3) Increasing the amplitude of test acceleration to superthreshold levels in harnessed subject to compare with pre-existing dynamic posturography findings of others and to compare threshld and superthreshold responses in the same subjects to determine whether threshold measures are clinically predictive of superthreshold responses;4) Investigating various models of postural control based on Legacy time series data and on proposed entrainment experiments, by using synced marker tracking, EMG, foot pressure and other SLIP-FALLS measures;5) Determining how well neruophysiological and bio- mechancial responses (or lack thereof) to perturbations correlate and predict detection threshold. Baseline measures will be expanded to include a way to link our results with an index of falling behavior.
