---
language:
- en

license: mit

task_categories:
- text-classification
- text-generation
- feature-extraction

task_ids:
- sentiment-analysis
- intent-classification

pretty_name: HESA-v1 Human Emotion and System Adaptation Dataset

size_categories:
- 1K<n<10K

tags:
- emotion-ai
- adaptive-ai
- human-centered-ai
- cognitive-ai
- ai-operating-system
- synthetic-dataset
- emotion-detection
- productivity-ai
- agi
- system-adaptation
dataset_info:
  features:
    - name: sample_id
      dtype: string
    - name: timestamp
      dtype: string
    - name: user_profile
      dtype: string
    - name: environment
      dtype: string
    - name: system_state
      dtype: string
    - name: human_behavior
      dtype: string
    - name: conversation_context
      dtype: string
    - name: ai_system_response
      dtype: string
    - name: long_term_memory
      dtype: string
  splits:
    - name: train
      num_examples: 400
    - name: validation
      num_examples: 60
    - name: test
      num_examples: 40
  download_size: 1904937
  dataset_size: 1904937
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.json
  - split: validation
    path: data/validation.json
  - split: test
    path: data/test.json
---

<div align="center">

# 🧠 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.*

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Samples](https://img.shields.io/badge/Samples-500-blue.svg)]()
[![Version](https://img.shields.io/badge/Version-1.0.0-green.svg)]()
[![HuggingFace](https://img.shields.io/badge/🤗-HuggingFace-orange.svg)](https://huggingface.co/wincode)
[![Kaggle](https://img.shields.io/badge/Kaggle-wincode-20BEFF.svg)](https://kaggle.com/wincode)

</div>

---

## 📖 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.

---

## 🗂️ Dataset Structure

### Top-level Fields

```json
{
  "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": { ... }
}
```

---

### 👤 `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 |

---

### 🌍 `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. |

---

### 💻 `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 |

---

### 🧑‍💻 `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 |

---

### 💬 `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. |

---

### 🤖 `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 |

---

### 🧬 `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 |

---

## 📊 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 |

---

## 🚀 Quick Start

### Load with Hugging Face `datasets`

```python
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

```python
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

```python
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

```python
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())
```

---

## 🎯 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

---

## ⚙️ 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`

---

## 📁 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)
```

---

## 📜 Citation

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

```bibtex
@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}
}
```

---

## 📄 License

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

---

## 👤 Author

**Ahmad Shajahan** (wincode)
- 🤗 HuggingFace: [huggingface.co/wincode](https://huggingface.co/wincode)
- 🐙 GitHub: [github.com/ahmadshajahan](https://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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