Transcript Continuous tracking tasks

Force Panel
Measurement of Human Dexterity
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
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Data mining for the project EU FP7 VERITAS
Dexterity parameters estimation
Cognitive test
Clinical assesment tool
Smart interface
Serious games
• Applications implemented
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Discrete Tracking task
Continuous Tracking task
Fitts Law
Force Control
Human Transfer Function
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Point to point motion task
Measures the following:
 Reaction time
 Movement time
 Path deviation in point to point motion
• Movement speed
 Dwelling Percentage Time in Target
• Percentage of success
3
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
[mm]
[mm]
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Subject 1: mild hemiparesis
[mm]
not enough force
5
[mm]
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Subject 3: severe hemiparesis
Subject 1
target 1
target 2
target 3
target 4
target 5
target 6
target 7
Subject 3
target 1
target 2
target 3
target 4
target 5
target 6
target 7
Time outside
target [ms]
Movement Reaction
Time [ms] Time [ms]
0
0
0
0
250
0
0
Time outside
target [ms]
1375
1484
1343
1405
1202
1375
1266
327
405
406
312
297
343
280
Movement Reaction
Time [ms] Time [ms]
983
0
0
452
110
31
702
1546
2638
2185
2295
2466
3496
2373
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Discrete tracking tasks
31
31
546
515
343
63
874
Path deviation
[mm]
2.8
6.3
4.9
8.9
2.3
4.1
4.3
Path deviation
[mm]
2.6
2.5
7.0
5.8
5.1
5.9
17.1
Continuous tracking tasks
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
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• Standard deviation of holding position
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
[mm]
8
[mm]
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Subject 1: mild hemiparesis
[mm]
9
[mm]
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Subject 3: severe hemiparesis
Continuous
tracking tasks:
results
quantification
START
Finger position
Target position
Subject
1
3
RMS deviation to
path [mm]
10.9
26.0
Mean target to finger
(trajectory) deviation
[mm]
2.2
4.2
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Continuous tracking tasks
Percentage of
time outside
the target [%]
9
36
Fitt’s law
Measures the following:
 A that is the reaction time.
 B that is the inverse of the index
of performance IP
Subject 3 : severe hemiparesis
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27.5.2015
27.5.2015
27.5.2015
27.5.2015
A
B
10:55:00 AM
0.65
0.45
11:15:00 AM
1.04
0.10
10:57:00
AM
M. De Cecco
- Lucidi del
corso di Measurement 2.30
Systems and Applications0.05
11:14:00 AM
1.15
0.04
IP
2.23
9.96
21.69 not injured hand
22.62 not injured hand
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
limb’s 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 Fitt’s law.
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Fitt’s Law
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
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Fitt’s Law
But there is another variable:
Difficulty of the task
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
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.
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Fitt’s 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 log2(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 log2(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
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Fitt’s Law
MT  a  b  ID
Time [ms]
D 
ID  log 2   1
W

1
IP 
b
ID [bit]
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Fitt’s Law
Parameter
Accuracy
a
15 ms
b
5 ms / bit
Patient
3
3
3
3
Date
26.5.2011
26.5.2011
26.5.2011
26.5.2011
Tim e
10:55:00 AM
11:15:00 AM
10:57:00 AM
11:14:00 AM
Intercept [s] Slope [s/bit]
IP [bit/s]
0.65
0.45
2.23
1.04
0.10
9.96
2.30
0.05
21.69 healty hand
1.15
0.04
22.62 healty hand
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
Fitt’s Law - comparative results
Position-Force tracking tasks
Measures the following:
• Position MSE
• Force MSE
• FFT
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M. De Cecco - Lucidi del corso di Measurement Systems and Applications
d
d  kF
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
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, 30-31 May 2011)
M. De Cecco - Lucidi del corso di Measurement Systems and Applications
M. De Cecco - Lucidi del corso di Measurement Systems and Applications