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.

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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  Fed by biologic input supplied through non invasive sensors  Explains its output through a possibly interpretable rule

The data  One subject using a car driver simulator  Subjected to alternate attention demanding manoeuvres (fast lane exchange, pedestrian avoidance) and relaxed driving  4 signals traced by a Biopac device (SKT, GSR, ECG, RSP)  Collected by the School of Psychology, Queen’s University Belfast.

Preprocessing  Extracted features  8 conventional (from medical knowledge)  FFT processing for ECG  Drift of the ECG signal from a neural prediction  SKT not considered (constant)

Feature processing I  15 Boolean values are extracted from a time-window of width 3 t-1t+1t  Result of a Boolean ICA through minimization of empirical entropy  253 connections (after pruning)

Feature processing II  Boolean values interpreted as propositional variables  Minimal DNF and DNF on variables interpreted as symbolic wavelets Begin DNF= ø ; for each positive example u ; DNF = DNF  {m} ; return DNF; End

An 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+x 3+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)(x 1+x5+x6+x8+x9+x11+x14+x15)(x3+x5+x7+x9+x10+x11)(x1+x3+x4+x7+x13+x14+x15)(x1+x 2+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)(x 2+x4+x5+x6+x7+x9+x10+x12+x13+x15)(x1+x3+x5+x8+x13+x15)(x1+x3+x6+x9+x10+x11+x 14)(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)(x 2+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)(x 3+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+x 15)(x2+x6+x7+x10+x11+x12+x13+x15)(x2+x4+x6+x10+x11+x12+x14+x15)(x2+x5+x6+x9+x 10+x11+x12+x14+x15)(x3+x4+x6+x8+x11+x14+x15)

Post processing  Simplification of the learnt rules through stochastic optimization of the cost  L: rule length,  :rule radius, :disregarded points

A simplified CNF (x6+x11+x1+x13)(x10+x12+x9+x6)(x1+x13+x11+x5)(x3+x8+x6)(x4+x1+x13)(x12+x6+x 7+x13)(x13+x8+x9+x4)(x1+x6+x8)(x12+x6+x7+x8)(x1+x8+x5+x7)(x4+x6+x7+x13)(x7+ x9+x10+x11+x3)(x1+x8+x13+x15)(x3+x8+x13+x15)(x4+x7+x8)(x1+x6+x9+x10+x11)(x2 +x6+x11+x12+x15)(x3+x5+x8+x13)(x4+x5+x7+x10+x11)(x3+x9+x10+x11+x13)  From 403 to 81 literals

Performance I DNFCNF LengthFPFNLengthFPFN AVG STDV  50 cross-validation test  FP: false positives; FN: false negatives

Performance II LengthTPFPFN DNF LengthTNFNFPCNF

Performance III