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FUTURE, blocked by #223: Combine eye tracking, EDA and EEG streams with flight data

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Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
1/100
Tipo di issue
Funzionalità
Chiarezza
Da chiarire
Stato di attività
Attiva
Stack tecnologico
rust
Ambito
data-engineering

Direzione di ricerca

Do not start this work: the issue explicitly records future work blocked by #223 and says not to build or design it yet. Read docs/client_meeting_notes.md for the client context; the open decisions include aligning sensor streams, choosing a join key, and deciding how physiological features may be used. Work can begin only after the stated blocker is resolved and the client questions are answered.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

area:ml enhancement needs-info

FUTURE WORK. DO NOT START. This is blocked until a good working real-time model exists (#223, which needs the batch model from #216, targets from #213 and the data from #228 and #229). Nothing here should be built, designed in detail or used to change the telemetry format before then. It exists only to record what the client said so it is not lost.

Blocked by: #223

Is your feature request related to a problem? Please describe.
The client wants flight data, physiological data and (eventually) demographics combined before clustering is automated. Today inference/ only knows the flight telemetry. The client records three physiological sensors in iMotions: eye tracking, electrodermal activity (EDA) and EEG. They are stored separately from the flight data and the demographics.

Describe the solution you'd like
Nothing to build yet. This issue records the future work and what blocks it, so the telemetry design does not rule it out.

  • Decide how streams recorded at different rates are aligned. As transcribed, the sample rates are: simulator about 90 Hz, EEG 256 Hz, EDA 128 Hz, eye tracking 51 Hz. These are unconfirmed, and the Prepar3D export we have is 1 Hz. iMotions synchronizes the timestamps; the client is not sure a common rate exists for clustering.
  • Decide the join key, probably the timestamp plus pilot_id and flight_id (#211).
  • Keep physiological features out of the first clustering (flight data alone is the starting point, per the client), and decide later whether they form groups or only interpret them (compare ADR 0001 for pilot metadata).

Describe alternatives you've considered
Run the clustering on the iMotions computer (#223), where all streams are already synchronized, and read the physiological data from there.

Additional context
Open questions for the client:

  • Will we ever see the physiological data? The client said raw data is large and sampled at separate rates, so it is hard to share. For now the answer is "for starting the work, no".
  • Can instruction timestamps (the standardized in-flight instructions logged in iMotions) be exported on their own? They would give targets (#213) and leg boundaries (#229) without the physiological data. That, and later a model that detects the instruction automatically, is the most valuable piece.

Notes in docs/client_meeting_notes.md.

Lingua principale
Python
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