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M. De Cecco - Lucidi del corso di Measurement Systems and Applications Force Panel Measurement of Human Dexterity.

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Presentation on theme: "M. De Cecco - Lucidi del corso di Measurement Systems and Applications Force Panel Measurement of Human Dexterity."— Presentation transcript:

1 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Force Panel Measurement of Human Dexterity

2 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Data mining for the project EU FP7 VERITAS Dexterity parameters estimation Cognitive test Clinical assesment tool Smart interface Serious games Applications implemented –Discrete Tracking task –Continuous Tracking task –Fitts Law –Force Control –Human Transfer Function

3 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Point to point motion task 3 Measures the following: Reaction time Movement time Path deviation in point to point motion Movement speed Dwelling Percentage Time in Target Percentage of success

4 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Subject 1: mild hemiparesis [mm]

5 M. De Cecco - Lucidi del corso di Measurement Systems and Applications [mm] 5 Subject 3: severe hemiparesis not enough force

6 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Discrete tracking tasks

7 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Continuous tracking tasks 7 Measures the following: Percentage time in target Root Mean Square Error Mean Deviation to trajectory Mean speed Standard deviation speed Mean error to hold the position Standard deviation of holding position

8 M. De Cecco - Lucidi del corso di Measurement Systems and Applications 8 Subject 1: mild hemiparesis [mm]

9 M. De Cecco - Lucidi del corso di Measurement Systems and Applications 9 Subject 3: severe hemiparesis [mm]

10 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Continuous tracking tasks: results quantification Finger position Target position START Continuous tracking tasks

11 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Fitts law 11 Measures the following: A that is the reaction time. B that is the inverse of the index of performance IP A BIP Subject 3 : severe hemiparesis

12 M. De Cecco - Lucidi del corso di Measurement Systems and Applications In executing the task, subjects are asked to touch as fast as possible two circular markers. The starting marker is white and has always the same dimension, the final marker is red and has a randomly variable dimension and distance from the previous one. In order to achieve a statistically meaningful number of data at least 28 iterations are achieved The main criteria of trajectory planning is to minimise the variance of the limbs position. Variance is due to noise in the neural control signal (i.e. in the firing of motor neurons) that causes trajectories to deviate from the desired pathNoise in the neural control signal increases with the mean level of its signal.. These deviations, accumulated over the duration of a movement, lead to variability in the final position. This explanation of signal-dependent noise is consistent with psychophysical observations that the variability of motor errors increases with the magnitude and the velocity of the movement, as captured by the empirical Fitts law. Fitts Law

13 M. De Cecco - Lucidi del corso di Measurement Systems and Applications In the presence of such signal-dependent noise, moving as rapidly as possible requires large control signals, which would increase the variability in the final position. As the resulting inaccuracy of the movement may lead to task failure or require further corrective movements, moving very fast becomes counterproductive. Accuracy could be improved by having low control signals, but the movement will be slow. Thus, signal dependent noise inherently imposes a trade-off between movement speed and terminal accuracy Fitts Law

14 M. De Cecco - Lucidi del corso di Measurement Systems and Applications But there is another variable: Difficulty of the task

15 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Fitts Law The Fitts's law, proposed by Paul Fitts in 1954, is a heuristic model of human movement in human interaction which models the time required to move to a target area as a function of the distance to and the size of the target. Fitts's law is used to model the act of pointing, either by physically touching an object with a hand or finger, or virtually, by pointing to an object on a computer display using a pointing device. The resulting model of the Fitts law is inherently linked to the aim of minimizing the final positional variance for the specified movement duration and/or to minimize the movement duration for a specified final positional variance determined by the task.

16 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Fitts Law According to Fitts Law, the time to move and point to a target of width W at a distance A is a logarithmic function of the ration A/W, proportional to difficulty: MT = a + b log 2 (2A/W + c) Where: - MT is the movement time - a and b are empirically determined constants, that are device dependent. - c is constant and equal to 1 - A is the distance (or amplitude) of movement from start to target centre - W is the width of the target, which corresponds to accuracy The term log 2 (2A/W + c) is called the index of difficulty (ID). It describes the difficulty of the motor tasks. 1/b is also called the index of performance (IP), and measures the information capacity of the human motor system a is linked tot he reaction time

17 M. De Cecco - Lucidi del corso di Measurement Systems and Applications ParameterAccuracy a15 ms b5 ms / bit ID [bit] Time [ms] Fitts Law

18 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Fitts Law - comparative results

19 M. De Cecco - Lucidi del corso di Measurement Systems and Applications Position-Force tracking tasks 19 Measures the following: Position MSE Force MSE FFT

20 M. De Cecco - Lucidi del corso di Measurement Systems and Applications d

21 M. Kirchner, M. De Cecco, M. Confalonieri, M. Da Lio, A joint force-position measurement system for neuromotor performances assessment, accepted by MeMeA 2011 (IEEE International Symposium on Medical Measurements and Applications, Bari, Italy, May 2011)

22 M. De Cecco - Lucidi del corso di Measurement Systems and Applications


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