{
  "title": "HESA-v1: Human Emotion + System Adaptation Dataset",
  "id": "wincode/hesa-v1-human-emotion-system-adaptation",
  "licenses": [
    {
      "name": "MIT"
    }
  ],
  "keywords": [
    "emotion recognition",
    "human computer interaction",
    "affective computing",
    "synthetic data",
    "adaptive AI",
    "stress detection",
    "cognitive load",
    "mental health",
    "productivity",
    "AGI",
    "intelligent tutoring",
    "NLP",
    "user behavior",
    "deep learning",
    "classification"
  ],
  "collaborators": [],
  "data": [
    {
      "description": "Full HESA-v1 dataset containing 500 synthetic samples of human emotion states, system telemetry, environmental context, and AI adaptive responses.",
      "name": "HESA_dataset_v1.json",
      "totalBytes": 1904937,
      "columns": [
        {
          "name": "sample_id",
          "description": "Unique sample identifier in format HESA-XXXXX"
        },
        {
          "name": "timestamp",
          "description": "ISO 8601 datetime of the simulated session"
        },
        {
          "name": "user_profile",
          "description": "Nested object: age, profession, country, personality_type, experience_level, sleep_hours, stress_level, mental_focus, social_energy"
        },
        {
          "name": "environment",
          "description": "Nested object: location_type, noise_level, lighting, temperature, time_of_day, weather"
        },
        {
          "name": "system_state",
          "description": "Nested object: CPU/RAM/battery usage, open apps, typing speed (derived), error frequency (derived), multitasking level"
        },
        {
          "name": "human_behavior",
          "description": "Nested object: emotion (28 categories), intensity, facial expression, voice tone, frustration signals, cognitive load, learning state"
        },
        {
          "name": "conversation_context",
          "description": "Nested object: user message, conversation goal, topic, urgency level, communication style"
        },
        {
          "name": "ai_system_response",
          "description": "Nested object: assistant response, UI adaptation, notification strategy, music recommendation, focus mode, wellness suggestion"
        },
        {
          "name": "long_term_memory",
          "description": "Nested object: historical behavior pattern, previous emotional state, productivity trend, burnout risk, learning progression"
        }
      ]
    }
  ]
}