| --- |
| language: |
| - en |
| license: mit |
| task_categories: |
| - text-classification |
| task_ids: |
| - regression |
| tags: |
| - burnout |
| - android |
| - telemetry |
| - stress-detection |
| - keyboard-dynamics |
| pretty_name: Synthetic Burnout Telemetry |
| size_categories: |
| - n<1K |
| --- |
| |
| # FRIDAY Synthetic Burnout Telemetry |
|
|
| Synthetic Android telemetry dataset for training a lightweight burnout / urgency |
| regression model (e.g. RoBERTa fine-tune). Each row represents a single |
| keyboard + notification event captured on a simulated Android device. |
|
|
| ## Dataset structure |
|
|
| | Split | Rows | |
| |------------|------| |
| | train | 500 | |
| | validation | 62 | |
| | test | 63 | |
|
|
| ## Fields |
|
|
| | Field | Type | Description | |
| |--------------|---------|----------------------------------------------------------| |
| | `input_text` | string | Serialised signal string fed directly to the tokeniser | |
| | `label` | float32 | Burnout / urgency score in **[0, 1]** | |
| | `app` | string | Source application (Slack, Gmail, WhatsApp, Teams, System) | |
| | `wpm` | int32 | Typing speed in words per minute | |
| | `backspaces` | int32 | Correction / backspace count in the session | |
| | `hour` | int32 | Hour-of-day the event was captured (0–23) | |
| | `session_min`| int32 | Continuous screen-on duration in minutes | |
| | `notif_count`| int32 | Pending notifications at event time | |
| | `text` | string | Raw message or notification body | |
|
|
|
|
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{friday_burnout_2026, |
| title = {FRIDAY Synthetic Burnout Telemetry}, |
| author = {Your Name}, |
| year = {2026}, |
| note = {Synthetic dataset for mobile stress detection research} |
| } |
| ``` |
|
|
