Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : Application to fNIRS signals

International audienc

Investigating Pilot’s Decision Making When Facing an Unstabilized Approach: An Eye-Tracking Study

Unstabilized approach has been identified to be a major causal factor of approach-and-landing accidents (e.g. off-runway touchdowns, hard landing, tail-strikes, etc).We conducted an experimentin order to analyze pilots’ performance during such approaches. Ten type-rated, commercial pilots flew each in a B737 full-flight simulator during anunstabilizedapproach at Hamburg airport. The Pilot Flyings’ (PF) eye gazes were collected. The results revealed that half of the pilots persisted in an erroneous landing decision. These latter pilots had higher dwell time on the attitude indicator/flight director whereas the group of pilots who perfomed the go-around exhibited more fixations on the navigation display prior to their final decision. These findings indicate that the decision whether to land or to go-around is taken considerably long before the respective task is executed, and that the use of heuristics impair pilot’s performance

The experiment was split into three successive phases.

<p>Data gathering (<i>phase D</i>) and classifier testing (<i>phase T</i>) consisted of 20 ATC instructions each. The pilot’s classifier was trained between these two phases (<i>phase L</i>). The time scale of the figure is illustrative.</p

ATC span task trial design.

<p>ATC span task trial design.</p

Machine learning result: WM Load level estimation accuracy for each participant.

<p>Machine learning result: WM Load level estimation accuracy for each participant.</p

Off-line estimated onset and offset latencies compared to the stimuli onset, in low WM load and high WM load conditions.

<p>Average for 20 trials per difficulty, on 19 pilots’ results. ***: p<0.001.</p

Illustration of the fNIRS based inference system.

<p>Pre-recorded ATC messages were sent to the pilot (1). The pilot’s prefrontal activity was measured with a fNIRS device (2). Output measures (3) were MACD-filtered and synchronized with the temporal design of the trial (4). During the entire session, the MACD-based state estimator detected whether the pilot’s state was <i>not-on-task</i> or <i>on-task</i> (5). When all of the required data were available for the trial, a request was sent to the pilot’s classifier to assess the WM load of the trial (6).</p

Trial timeline and computing latencies.

<p>The upper timeline shows ATC span task trial events duration (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121279#pone.0121279.g003" target="_blank">Fig. 3</a>). Bottom timeline illustrates duration constraints to get pilot’s estimated WM load: classifier’s response is available in the worst case less than 3.3<i>s</i> after pilot’s response window.</p

Example of real-time state estimation (performed on pilot 16).

<p>The upper graph shows MACD-filtered fNIRS signal and the signal line computed from the latter (dashed line). The two lower graphs show the participant’s state estimated from crossovers between MACD and signal lines and the operator’s actual state, respectively.</p

Activation maps according to the level of difficulty.

<p>Units are in <i>μmol</i>.<i>l</i>⁻¹. Both high and low load conditions elicit bilateral DLPFC activities. The high load minus low load subtraction map (High—Low) shows significantly greater activation of the right DLPFC. Activations shown 14 s post-stimulus onset. <i>p</i> < 0.001. fNIRSOFT® software (<a href="http://www.biopac.com/fNIR-Software-Professional-Edition" target="_blank">www.biopac.com/fNIR-Software-Professional-Edition</a>) was used to produce this figure.</p