A man-machine human A man-machine human interface for a interface for a special device of special device of the pervasive the pervasive computing world computing world B. Apolloni, S. Bassis, A. B. Apolloni, S. Bassis, A. Brega, Brega, S. Gaito, D. Malchiodi, A.M. S. Gaito, D. Malchiodi, A.M. Zanaboni Zanaboni DSI - University of Milano DSI - University of Milano
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A man-machine human interface for a special device of the pervasive computing world
A man-machine human interface for a special device of the pervasive computing world. B. Apolloni, S. Bassis, A. Brega, S. Gaito, D. Malchiodi, A.M. Zanaboni DSI - University of Milano (I). Outline. A procedure detecting attention states in car driving - PowerPoint PPT Presentation
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A man-machine human A man-machine human interface for a special interface for a special
device of the pervasive device of the pervasive computing worldcomputing world
B. Apolloni, S. Bassis, A. Brega,B. Apolloni, S. Bassis, A. Brega,
S. Gaito, D. Malchiodi, A.M. ZanaboniS. Gaito, D. Malchiodi, A.M. Zanaboni
DSI - University of Milano (I)DSI - University of Milano (I)
OutlineOutline
A procedure detecting attention states in car A procedure detecting attention states in car drivingdriving
Fed by biologic input supplied through non Fed by biologic input supplied through non invasive sensorsinvasive sensors
Explains its output through a possibly Explains its output through a possibly interpretable ruleinterpretable rule
The dataThe data
One subject using a car driver simulatorOne subject using a car driver simulator Subjected to alternate attention demanding Subjected to alternate attention demanding
manoeuvres (fast lane exchange, pedestrian manoeuvres (fast lane exchange, pedestrian avoidance) and relaxed drivingavoidance) and relaxed driving
4 signals traced by a Biopac device (SKT, 4 signals traced by a Biopac device (SKT, GSR, ECG, RSP)GSR, ECG, RSP)
Collected by the School of Psychology, Collected by the School of Psychology, Queen’s University Belfast.Queen’s University Belfast.
PreprocessingPreprocessing
Extracted featuresExtracted features 8 conventional (from medical knowledge)8 conventional (from medical knowledge) FFT processing for ECGFFT processing for ECG Drift of the ECG signal from a neural predictionDrift of the ECG signal from a neural prediction SKT not considered (constant)SKT not considered (constant)
Feature processing IFeature processing I
15 Boolean values 15 Boolean values are extracted from a are extracted from a time-window of time-window of width 3width 3
t-1 t+1t
€
Ep = log zpj−zpj
j=1
n
∏ (1− zpj )−(1−zpj )
⎛
⎝ ⎜
⎞
⎠ ⎟
Result of a Boolean Result of a Boolean ICA through ICA through minimization of minimization of empirical entropyempirical entropy
Boolean values interpreted as propositional Boolean values interpreted as propositional variablesvariables
Minimal DNF and DNF on variables Minimal DNF and DNF on variables interpreted as symbolic waveletsinterpreted as symbolic wavelets
Begin
DNF=ø;
for each positive example u; DNF = DNF {m};return DNF;End
An obtained CNFAn obtained CNF(x1+x3+x6+x8+x10+x14)(x1+x2+x5+x6+x7+x9+x11+x12+x13)(x1+x2+x6+x7+x9+x10+x12+x14)(x1+x2+x5+x7+x8+x12+x13+x15)(x1+x3+x5+x11+x13+x15)(x3+x8+x10+x11+x13+x14+x15)(x1+x2+x4+x6+x7+x11+x12+x13+x14+x15)(x1+x2+x4+x8+x12+x13+x14+x15)(x1+x2+x3+x7+x8+x10+x13+x14+x15)(x3+x4+x8+x10+x13+x14+x15)(x1+x3+x6+x7+x8+x13+x14)(x3+x5+x7+x8+x13)(x2+x3+x6+x7+x10+x11+x12+x13+x14)(x1+x5+x6+x8+x9+x11+x12+x15)(x1+x5+x6+x8+x9+x11+x14+x15)(x3+x5+x7+x9+x10+x11)(x1+x3+x4+x7+x13+x14+x15)(x1+x2+x3+x8+x11+x13+x14+x15)(x2+x5+x6+x7+x9+x11+x12+x15)(x2+x3+x4+x6+x9+x10+x11+x12+x14)(x2+x4+x5+x7+x8+x13+x15)(x3+x4+x6+x7+x10+x13)(x3+x4+x6+x8+x10+x14)(x1+x4+x7+x8+x13+x15)(x1+x4+x5+x7+x8+x9+x15)(x4+x7+x8+x10+x15)(x4+x7+x8+x10+x14)(x2+x4+x5+x6+x7+x9+x10+x12+x13+x15)(x1+x3+x5+x8+x13+x15)(x1+x3+x6+x9+x10+x11+x14)(x1+x6+x9+x10+x11+x12+x14)(x1+x6+x8+x9+x11+x12+x14)(x1+x2+x4+x6+x7+x10+x11+x12+x13+x14)(x2+x4+x5+x8+x9+x10+x12+x13+x15)(x2+x4+x5+x7+x9+x10+x11+x12)(x1+x3+x6+x7+x8+x10+x13)(x3+x4+x5+x6+x8+x9+x10+x11)(x2+x5+x6+x8+x9+x10+x11+x12)(x2+x5+x6+x7+x8+x9+x11+x12)(x1+x2+x4+x5+x6+x7+x9+x12+x13)(x3+x4+x5+x7+x10+x11)(x1+x2+x5+x8+x10+x12+x13+x15)(x1+x2+x6+x7+x9+x10+x11)(x1+x2+x5+x7+x8+x9+x14)(x3+x4+x5+x8+x9+x10+x13+x15)(x3+x8+x9+x10+x11+x13)(x3+x9+x10+x11+x13+x14)(x1+x6+x7+x8+x9+x10+x12)(x1+x2+x3+x6+x11+x13+x14+x15)(x2+x4+x5+x6+x9+x11+x12+x14+x15)(x2+x6+x7+x10+x11+x12+x13+x15)(x2+x4+x6+x10+x11+x12+x14+x15)(x2+x5+x6+x9+x10+x11+x12+x14+x15)(x3+x4+x6+x8+x11+x14+x15)
Post processingPost processing
Simplification of the learnt rules through Simplification of the learnt rules through stochastic optimization of the coststochastic optimization of the cost