wincode/HESA-v2

🧠 HESA-v1

Human Emotion + System Adaptation Dataset

A synthetic, research-grade dataset for training emotion-aware AI systems, adaptive operating systems, and human-centered computing platforms.

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📖 Dataset Description

HESA-v1 is a large-scale synthetic dataset designed to simulate the complex relationship between human emotional states, computer usage behavior, environmental context, and intelligent AI adaptive responses.

This dataset was engineered for training next-generation:

• 🤖 Emotion-aware Operating Systems
• 🧩 Adaptive AGI Systems
• 📚 Intelligent Tutoring Systems
• 💬 Emotionally Intelligent Assistants
• 🧠 Cognitive AI Systems
• 🖥️ Human-Centered Computing Platforms

Each sample captures a complete human-computer interaction snapshot — combining psychological state, environmental conditions, system telemetry, behavioral signals, and AI adaptive responses into one richly structured JSON record.

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🗂️ Dataset Structure

Top-level Fields

{
  "sample_id": "HESA-00001",
  "timestamp": "2026-03-14T22:47:00",
  "user_profile": { ... },
  "environment": { ... },
  "system_state": { ... },
  "human_behavior": { ... },
  "conversation_context": { ... },
  "ai_system_response": { ... },
  "long_term_memory": { ... }
}

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👤 user_profile

Field	Type	Description
age	int	User age (16–58)
profession	string	Job/role (30 unique professions)
country	string	Country of origin (25 countries)
personality_type	string	MBTI type (all 16 types)
experience_level	string	Beginner → Veteran
sleep_hours	float	Hours slept last night (3.0–10.0)
stress_level	string	Very Low → Critical
mental_focus	string	Scattered → Hyperfocused
social_energy	string	Depleted → Highly Social

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🌍 environment

Field	Type	Description
location_type	string	Home Office, Library, Coffee Shop, etc.
noise_level	string	Silent → Chaotic
lighting	string	Pitch Dark → Fluorescent Harsh
temperature	string	Cold (15°C) → Hot (31°C)
time_of_day	string	9 slots from Early Morning to Deep Night
weather	string	Clear Sunny, Rainy, Stormy, Snowing, etc.

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💻 system_state

Field	Type	Description
cpu_usage_percent	int	CPU load (8–97%)
ram_usage_percent	int	RAM utilization (25–92%)
battery_level_percent	int	Battery charge (4–100%)
network_quality	string	Offline → Gigabit Ethernet
open_applications	list	2–9 apps from 34 real applications
active_application	string	Currently focused app
typing_speed_wpm	int	WPM — derived from stress + sleep + focus
mouse_movement_pattern	string	Behavioral mouse pattern
scroll_behavior	string	Reading/skimming/jumping pattern
error_frequency_rate	float	Error rate 0.0–1.0 (derived)
multitasking_level	string	None → Extreme

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🧑‍💻 human_behavior

Field	Type	Description
emotion	string	28 emotion categories
emotion_intensity	string	Subtle → Overwhelming
facial_expression	string	Observable facial cues
voice_tone	string	Vocal behavioral signal
typing_pattern	string	Keystroke behavioral pattern
interaction_style	string	How user engages with system
frustration_signals	list	Observable frustration indicators
focus_duration_minutes	int	Minutes in current focus block
energy_level	string	Depleted → Peak
motivation_level	string	None → Intrinsic Drive
cognitive_load	string	Minimal → Overloaded
learning_state	string	Not Learning → Breakthrough Moment

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💬 conversation_context

Field	Type	Description
user_message	string	Realistic user query/request
conversation_goal	string	Intent behind the interaction
topic	string	Task or subject being worked on
urgency_level	string	Low → Deadline in under 1 hour
communication_style	string	Brief imperative, verbose, exploratory, etc.

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🤖 ai_system_response

Field	Type	Description
assistant_response	string	Context-aware AI response
response_tone	string	Warm, neutral, energizing, grounding, etc.
recommended_action	string	Adaptive productivity recommendation
ui_adaptation	string	UI/theme change triggered
notification_strategy	string	How notifications are managed
system_adjustments	list	OS-level changes applied
music_recommendation	string	Adaptive audio environment
learning_support	string	Educational strategy offered
focus_mode	string	Active focus/productivity mode
wellness_suggestion	string	Physical/mental wellbeing nudge

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🧬 long_term_memory

Field	Type	Description
historical_behavior_pattern	string	Pattern observed across sessions
previous_emotional_state	string	Prior emotion for trend analysis
productivity_trend	string	Multi-session productivity trajectory
burnout_risk	string	Negligible → Critical
learning_progression	string	Learning trajectory description

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📊 Dataset Statistics

Metric	Value
Total Samples	500
Unique Emotions	28
Countries Represented	25
Professions Covered	30
MBTI Personality Types	16 (all)
Age Range	16 – 58
Time Slots	9 (4 AM → 5 AM next day)
Unique App References	34
Total JSON Size	~1.86 MB
Splits	Train / Validation / Test

Emotion Distribution (Top 10)

Emotion	Count
sad	28
calm	25
anxious	25
curious	24
lonely	22
optimistic	21
determined	20
mentally_exhausted	20
restless	20
insecure	20

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🚀 Quick Start

Load with Hugging Face datasets

from datasets import load_dataset

dataset = load_dataset("wincode/HESA-v1")
print(dataset)
# DatasetDict({
#     train: Dataset({features: [...], num_rows: 400}),
#     validation: Dataset({features: [...], num_rows: 60}),
#     test: Dataset({features: [...], num_rows: 40})
# })

Load raw JSON

import json

with open("HESA_dataset_v1.json", "r") as f:
    data = json.load(f)

samples = data["samples"]
print(f"Total samples: {len(samples)}")

# Access first sample
s = samples[0]
print(f"Emotion: {s['human_behavior']['emotion']}")
print(f"Stress: {s['user_profile']['stress_level']}")
print(f"AI Response: {s['ai_system_response']['assistant_response']}")

Example: Filter by emotion

burned_out = [s for s in samples if s["human_behavior"]["emotion"] == "burned_out"]
print(f"Burned-out samples: {len(burned_out)}")

# Get AI wellness suggestions for burned-out users
for s in burned_out[:3]:
    print(s["ai_system_response"]["wellness_suggestion"])

Example: Stress vs Typing Speed analysis

import pandas as pd

rows = [{
    "stress": s["user_profile"]["stress_level"],
    "typing_wpm": s["system_state"]["typing_speed_wpm"],
    "error_rate": s["system_state"]["error_frequency_rate"],
    "sleep": s["user_profile"]["sleep_hours"]
} for s in samples]

df = pd.DataFrame(rows)
print(df.groupby("stress")[["typing_wpm", "error_rate"]].mean())

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🎯 Intended Use Cases

✅ Recommended Uses

• Emotion classification — Train models to detect user emotional state from behavioral signals
• Adaptive UI/UX systems — Learn context-aware interface adaptation rules
• Intelligent tutoring — Model learning state transitions and educational support strategies
• Burnout prediction — Detect early burnout signals from multi-session patterns
• Cognitive load estimation — Infer workload from typing speed, error rate, and multitasking signals
• Affective computing research — Study emotion-behavior-environment relationships
• Synthetic data augmentation — Supplement real HCI datasets
• AGI training — Human-grounded context for general-purpose assistants

❌ Out-of-Scope Uses

• Real identity inference — This is fully synthetic data; no real persons represented
• Surveillance systems — Not intended for real-time monitoring applications
• Clinical diagnosis — Not a substitute for clinically validated instruments

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⚙️ Generation Methodology

HESA-v1 was generated using a Python-based synthetic data engine with:

• Psychologically consistent derivation rules — typing speed, error frequency, and frustration signals are mathematically derived from stress level, sleep hours, and emotional state
• Realistic variability — Gaussian distributions for focus duration, weighted distributions for sleep hours
• Cross-field coherence — Emotion states correlate with UI adaptations, notification strategies, and wellness responses
• Diverse seeding pools — 30 professions, 25 countries, 16 MBTI types, 28 emotion categories, 34 real applications
• Temporal realism — Timestamps span full year with all time-of-day slots represented

Derived relationships implemented:

• stress_level + sleep_hours + mental_focus → typing_speed_wpm
• stress_level + sleep_hours + emotion → error_frequency_rate
• emotion + emotion_intensity → frustration_signals
• emotion + stress + focus → ai_system_response

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📁 Repository Structure

wincode/HESA-v1/
├── README.md                    ← This file
├── HESA_dataset_v1.json         ← Full raw dataset (500 samples)
├── data/
│   ├── train.json               ← 400 training samples
│   ├── validation.json          ← 60 validation samples
│   └── test.json                ← 40 test samples
└── notebooks/
    └── explore_HESA.ipynb       ← Exploration notebook (optional)

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📜 Citation

If you use HESA-v1 in your research or projects, please cite:

@dataset{hesa_v1_2026,
  title     = {HESA-v1: Human Emotion and System Adaptation Dataset},
  author    = {Shajahan, Ahmad},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/wincode/HESA-v1},
  note      = {Synthetic research-grade dataset for emotion-aware AI systems}
}

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📄 License

This dataset is released under the MIT License. Free for academic, research, and commercial use with attribution.

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👤 Author

Ahmad Shajahan (wincode)

• 🤗 HuggingFace: huggingface.co/wincode
• 🐙 GitHub: github.com/ahmadshajahan
• 🏢 Zerath Labs / The Art of Engineering — Kerala, India

KTU S4 CSE Student | AI/ML Researcher | PyTorch Content Creator

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