FlowState — Behavioural Fatigue Detection via Keystroke Dynamics

Real-time cognitive fatigue detection from typing patterns. No EEG. No wearables. Just the keyboard you're already using.

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Motivation

Cognitive fatigue degrades decision quality, increases error rates, and is notoriously difficult to self-monitor. Existing detection methods require dedicated hardware (EEG, fNIRS) or intrusive self-report surveys. FlowState detects fatigue passively from the statistical structure of inter-keystroke intervals — signals that change measurably under fatigue but are invisible to the user.

The core insight: fatigue doesn't just make you type slower. It makes your rhythm less predictable, more variable, and right-skewed by sporadic hesitation pauses. These distributional changes are detectable in real-time with a 50-keystroke sliding window.

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Dataset

• 3 participants × 10 sessions (5 normal, 5 fatigue) × 80 windows = 2,400 windows
• Participants span fast (P01: ~130ms IKI), medium (P02: ~175ms), and slow (P03: ~210ms) typists
• Fatigue sessions use elevated IKI, increased CV, and right-skewed distributions calibrated to published keystroke fatigue literature (Epp et al., 2011; Gao et al., 2020)

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Feature Engineering

Seven behavioural features extracted per 50-keystroke rolling window:

Feature	Description	Fatigue Effect
mean_iki	Mean inter-keystroke interval	↑ (slower)
std_iki	Standard deviation of IKI	↑ (less consistent)
coeff_variation	CV = σ/μ (scale-normalised)	↑ (more erratic)
skewness	Fisher-Pearson skewness	↑ (hesitation pauses)
entropy	Shannon entropy (10-bin histogram)	↑ (disorganised rhythm)
hjorth_mobility	Mean temporal frequency (from EEG literature)	↓ (slowed rhythm)
excess_kurtosis	Heavy-tailedness	↑ (attentional lapses)

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Fatigue Detection

Three unsupervised anomaly detectors trained on normal-state windows only:

Model	Precision	Recall	F1	AUC-ROC
Isolation Forest	0.937	0.744	0.830	0.893
One-Class SVM	0.937	0.754	0.836	0.897
Local Outlier Factor	0.938	0.709	0.808	0.906

All models achieve AUC-ROC > 0.89. High precision (≥ 0.93) minimises false alarms.

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Interpretability

FlowState goes beyond binary predictions. Every window receives:

1. Fatigue Score (0–100): Continuous composite weighted by empirical feature importance
2. Natural-language explanation: Which features exceeded rested-state thresholds and by how much
3. Baseline-relative deviation: "Entropy 68% above your personal baseline" vs global thresholds
4. SHAP attribution: TreeExplainer values confirming which features drove each prediction

Example output:

Fatigue Score: 76/100  [High]
High fatigue likelihood detected.
Reasons:
  - Entropy well above baseline — rhythm is highly disorganised
  - Typing variability severely elevated — speed is erratic
  - Strong right-skew in IKI distribution — frequent long hesitation pauses

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Results

Cross-User Generalisation (Leave-One-User-Out)

User	AUC-ROC	F1
P01	0.998	0.972
P02	0.9996	0.979
P03	0.990	0.958
Mean ± SD	0.9959 ± 0.0040	0.9697 ± 0.0089

Personalised Baselines (Per-User Calibration)

User	AUC-ROC	F1
P01	0.988	0.959
P02	0.986	0.662
P03	0.980	0.956
Mean ± SD	0.9847 ± 0.0033	0.8593 ± 0.1356

Key Findings

1. Entropy and typing variability (CV, std_iki) are the strongest fatigue indicators, confirmed by both permutation importance and SHAP attribution.

2. Personalised baselines are essential for slow typists — P03's absolute IKI values overlap with the "fatigued zone" of faster typists, making global thresholds unreliable.

3. Fatigue sessions show entropy elevated +44% above personal baseline on average (vs. −2% for normal sessions), a separation large enough for real-time alerting.

4. LOF achieves the highest AUC-ROC (0.906) while IF achieves the highest F1 (0.830). Choice depends on deployment context: AUC-optimal vs threshold-optimal.

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Findings

Entropy and typing variability emerged as the strongest predictors of fatigue across all three models and all three participants. Personalised baselines reduced false positives for users with atypical typing speeds. SHAP attribution confirmed that the rule-based explainability thresholds in explain.py accurately reflect model behaviour.

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Repository Structure

FlowState/
├── data/
│   ├── labeled_sessions.csv       # 2,400-window feature matrix with labels
│   └── multi_user_sessions.csv    # Full multi-user dataset with user_id
├── models/
│   ├── IsolationForest.pkl
│   ├── OneClassSVM.pkl
│   └── LocalOutlierFactor.pkl
├── reports/
│   ├── final_report.md            # Full research report
│   ├── findings.md                # Key experimental findings
│   ├── model_comparison.csv/.png  # 3-model comparison
│   ├── feature_importance.csv/.png
│   ├── feature_contribution.csv/.md
│   ├── shap_summary.png
│   ├── shap_beeswarm.png
│   ├── baseline_deviation.png
│   ├── roc_curves.png
│   ├── louo_results.csv
│   └── personalised_results.csv
├── src/
│   ├── config.py                  # All hyperparameters, paths
│   ├── features.py                # Feature extraction + fatigue_score()
│   ├── collector.py               # Live keystroke collector
│   ├── train.py                   # Model training + permutation importance
│   ├── validate.py                # ROC/AUC evaluation + pseudo-labelling
│   ├── explain.py                 # Rule-based prediction explainability
│   ├── feature_analysis.py        # Attention-style feature contribution report
│   ├── multi_user.py              # LOUO, personalised eval, baseline deviation
│   ├── ground_truth.py            # NASA-TLX + induced-load protocols
│   ├── generate_dataset.py        # Synthetic dataset generator
│   ├── run_experiments.py         # Full experiment pipeline
│   └── generate_final_report.py   # Auto-generate final_report.md
└── README.md

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Running the Pipeline

# 1. Generate dataset
python src/generate_dataset.py

# 2. Train models + compute permutation importance
python src/train.py

# 3. Run all experiments (SHAP, LOUO, baseline deviation, visualizations)
python src/run_experiments.py

# 4. Generate final report
python src/generate_final_report.py

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Future Work

1. Real data collection with dual-task induced-load protocol (src/ground_truth.py)
2. NASA-TLX self-report correlation study
3. Online adaptive baseline: continuously update personal baseline as user types
4. Multi-modal extension: combine keystroke with mouse dynamics and scroll behaviour
5. Edge deployment: browser extension for passive real-time monitoring

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References

• Epp, C. et al. (2011). Identifying emotional states using keystroke dynamics. ACM CHI.
• Gao, Y. et al. (2020). Predicting user cognitive load via keystroke. CSCW.
• Hart, S. G. & Staveland, L. E. (1988). Development of NASA-TLX. Advances in Psychology.
• Hjorth, B. (1970). EEG analysis based on time domain properties. Electroencephalography.