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  • Author: Pietro G. Morasso x
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Pietro G. Morasso, Vittorio Sanguineti and Francesco Frisone

Although it is true that the specific research on grasping has been dominated in recent years by the canonical transport + grip model originally formulated by Jeannerod (1984), still one can find in the research on reaching a number of links and anticipations to the new view on grasping made explicit by the authors of the target article. This paper reviews some of the relevant concepts and outlines a modeling framework that aims at biological plausibility.

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Marco Jacono, Maura Casadio, Pietro G. Morasso and Vittorio Sanguineti

The sway-density curve (SDC) is computed by counting, for each time instant, the number of consecutive samples of the statokinesigram falling inside a circle of “small” radius R. The authors evaluated the sensitivity of the curve to the variation of R and found that in the range 3–5 mm the sensitivity was low, indicating that SDC is a robust descriptor of posturographic patterns. In addition, they investigated the relationship between SDC and the underlying postural stabilization process by decomposing the total ankle torque into three components: a tonic component (over 69% of the total torque), an elastic torque caused by ankle stiffness (about 19%), and an anticipatory active torque (about 12%). The last component, although the smallest in size, is the most critical for the overall stability of the standing posture and appears to be correlated with the SDC curve.

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Luigi Baratto, Pietro G. Morasso, Cristina Re and Gino Spada

In order to identify useful guidelines for the clinical practitioner as regards the use of static posturographic analysis, we collected a set of posrurograms from 3 groups of participants (normal participants. Parkinsonian patients, and osteoporotic patients), according to the Romberg test. From each posturogram, we extracted global parameters (in the time domain and frequency domain) and structural parameters (based on diffusion plots and sway-density plots), with a total of 38 parameters. The discriminative power of each parameter was evaluated by means of statistical analysis in relation to the condition effect (open vs. closed eyes) and the pathology effect (normal participants vs. patients). The initial set of 38 parameters was reduced to 24 by identifying clear redundancies, and then to 18 by eliminating the parameters that did not pass the condition effect with normal participants. These parameters were analyzed for reliability and discriminative power in the general framework of a biomechanic model of postural stabilization. At the end of this analysis, we suggested that a set of 4 parameters is particularly valuable in the clinical practice: 2 global parameters (sway-path and frequency band of the posturogram) and 2 structural parameters (mean value of peaks and mean inter-peak distance in the sway-density plots).