Virtual Poster Session

Welcome to the Virtual Poster Session, a new and powerful tool for networking and information exchange. Here you can share your work, search though the poster library, and start a dialogue with others in your field. Each uploaded poster that pertains to force measurement and testing can currently be used to apply for an academic travel scholarship; please see the Scholarships page for application details and deadlines.

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Submitted by Nikita Kuznetsov

Fractal time series analysis methods are commonly used for analyzing center of pressure (COP) signals with the goal of revealing the underlying neuromuscular processes for upright stance control. The use of fractal methods is often coupled with the assumption that the COP is an instance of fractional Gaussian noise (fGn) or fractional Brownian motion (fBm). Our purpose was to evaluate the applicability of the fGn-fBm framework to the COP in light of several characteristics of COP signals revealed by a new method, adaptive fractal analysis (AFA; Riley et al., 2012). Our results showed that there are potentially three fractal scaling regions in the COP as opposed to one as expected from a pure fGn or fBm process. The scaling region at the fastest scale was anti-persistent and spanned ~30-90 msec, the intermediate was persistent and spanned ~200 msec-1.9 sec, and the slowest was anti-persistent and spanned ~5-40 sec. The intermediate fractal scaling region was the most clearly defined, but it only contributed around 11% of the total spectral energy of the COP signal, indicating that other features of the COP signal contribute more importantly to the overall dynamics. Also, more than half of the Hurst exponents estimated for the intermediate region were greater than the theoretically expected range [0,1] for fGn-fBm processes. These results suggest the fGn-fBm framework is not appropriate for modeling COP signals. ON-OFF intermittency might provide a better modeling framework for the COP, and multiscale approaches may be more appropriate for analyzing COP data.


Submitted by Tomislav Milekovic

Development of neuronal prosthetics, where neuronal activity is used to control artificial limbs, has so far relied on decoding kinematic parameters of movements, such as movement position or velocity. In addition to kinematic control, proper control of forces exerted by the prosthetic device is necessary for successful interaction with the environment. In our study, we analysed the possibility of classifying and decoding different grasp related forces during active grasping. Two macaque monkeys were trained to reach, grasp and pull an object in response to visual cues. Cues instructed the monkeys to grasp the object with one out of two grip types (precision or side grip) and pull the object with one of two different forces (0.5N or 2N). Monkeys obtained a food reward after successfully performing the instructed grip and pull. During the task execution, we recorded electrophysiological signals from the multielectrode arrays implanted intracortically in the hand and arm area of the monkey’s motor cortex. Six different parameters of the grip: four pressure forces on each side of the object, pull force on the object and the object displacement, were recorded simultaneously with the neuronal activity. Recorded neuronal activity was used to classify different grip types or loading forces, and to decode the continuous traces of different forces during the grip. Our results show that kinetic grip parameters can be decoded with high accuracy, thereby improving the feasibility of constructing fully functional anthropomorphic neuronal prosthesis that relies on kinetic (force) control.