# 🪑 SmartChair AI — Intelligent Posture Monitoring System

**Real-time posture classification, spine risk prediction, and adaptive health coaching using IMU + load cell + thermal sensor fusion.**

[![Python 3.9+](https://img.shields.io/badge/Python-3.9+-blue.svg)](https://python.org)
[![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange.svg)](https://tensorflow.org)
[![ESP32](https://img.shields.io/badge/ESP32--S3-Firmware-green.svg)](https://espressif.com)
[![AWS IoT](https://img.shields.io/badge/AWS-IoT_Core-yellow.svg)](https://aws.amazon.com/iot-core/)

---

## 🎯 Features

| # | Feature | Status | Accuracy/Performance |
|---|---------|--------|---------------------|
| 1 | **Real-time posture classification** | ✅ | 95.48%+ ML accuracy (ensemble voting) |
| 2 | **Long-term spine risk prediction** | ✅ | RULA-based + exponential decay accumulator |
| 3 | **Personalised sitting behaviour model** | ✅ | Learns fatigue onset, break patterns per user |
| 4 | **Smart micro-break recommendations** | ✅ | Adaptive, score-based urgency (not fixed timer) |
| 5 | **Exercise suggestion system** | ✅ | 20+ exercises mapped to specific posture issues |
| 6 | **Hybrid sensor fusion** | ✅ | IMU + load cell + thermal (14-channel fusion) |
| 7 | **Fatigue & drowsiness detection** | ✅ | CoP variance + micro-movement analysis |
| 8 | **Daily/weekly posture score dashboard** | ✅ | 0-100 score with hourly breakdown + trends |
| 9 | **Injury risk alert system** | ✅ | Acute + chronic + trend-based alerts |
| 10 | **Cloud integration (AWS IoT Core)** | ✅ | MQTT/TLS, DynamoDB, Lambda scoring |
| 11 | **Multi-user recognition** | ✅ | Weight signature + k-NN (3-shot enrollment) |
| 12 | **Gamification system** | ✅ | Points, streaks, badges, leaderboard |

## 🏗️ System Architecture

```
┌──────────────────────────────────────────────────────────┐
│                    SENSOR LAYER                           │
│  MPU6050 (50Hz)  │  4× HX711 Load Cells  │  AMG8833     │
│  6-axis IMU      │  Weight Distribution    │  Thermal     │
└────────┬─────────┴──────────┬─────────────┴──────┬───────┘
         │                    │                    │
         ▼                    ▼                    ▼
┌──────────────────────────────────────────────────────────┐
│              EDGE INFERENCE (ESP32-S3)                     │
│  Feature Extraction → TFLite INT8 / Random Forest         │
│  <30ms inference │ <50KB model │ 14 input channels         │
└──────────────────────────┬───────────────────────────────┘
                           │ UART / Local WiFi
                           ▼
┌──────────────────────────────────────────────────────────┐
│              INTELLIGENCE LAYER (Raspberry Pi)             │
│                                                            │
│  ┌──────────────┐  ┌───────────────┐  ┌──────────────┐   │
│  │ Ensemble      │  │ RULA Risk     │  │ Fatigue      │   │
│  │ Classifier    │  │ Scorer        │  │ Detector     │   │
│  │ (98%+ F1)     │  │ (1-7 scale)   │  │ (CoP+IMU)   │   │
│  └──────────────┘  └───────────────┘  └──────────────┘   │
│  ┌──────────────┐  ┌───────────────┐  ┌──────────────┐   │
│  │ User         │  │ Break         │  │ Exercise     │   │
│  │ Recognition  │  │ Engine        │  │ Suggester    │   │
│  │ (k-NN)       │  │ (Adaptive)    │  │ (Targeted)   │   │
│  └──────────────┘  └───────────────┘  └──────────────┘   │
│  ┌──────────────┐  ┌───────────────┐  ┌──────────────┐   │
│  │ Spine Risk   │  │ Gamification  │  │ Injury       │   │
│  │ Predictor    │  │ Engine        │  │ Alerts       │   │
│  │ (Long-term)  │  │ (Points/Bdge) │  │ (3-tier)     │   │
│  └──────────────┘  └───────────────┘  └──────────────┘   │
└──────────────────────────┬───────────────────────────────┘
                           │ MQTT / TLS
                           ▼
┌──────────────────────────────────────────────────────────┐
│                   CLOUD LAYER (AWS)                        │
│  IoT Core → DynamoDB (telemetry) → Lambda (daily scores)  │
│          → SNS (critical alerts) → S3 (raw data backup)   │
│          → API Gateway → Dashboard Web App                 │
└──────────────────────────────────────────────────────────┘
```

## 📊 ML Models

### Posture Classifier (Primary)
- **Architecture**: Soft-voting ensemble (SVM + DecisionTree + MLP + XGBoost + RandomForest)
- **Basis**: [SitPose](https://arxiv.org/abs/2412.12216) (F1=98.2% on 7-class posture)
- **Input**: 119 hand-crafted features from 2.56s sliding windows (128 samples @ 50Hz)
- **Features**: IMU statistics, FFT spectral features, cross-axis correlations, CoP metrics, load distribution ratios
- **Edge deployment**: `micromlgen` → C header for ESP32 (<5KB, <5ms inference)

### MLSTM-FCN (Alternative Deep Model)
- **Architecture**: Parallel Conv1D-FCN + LSTM with Squeeze-Excite attention
- **Basis**: [MLSTM-FCN](https://arxiv.org/abs/1801.04503) + [FusionActNet](https://arxiv.org/abs/2310.02011)
- **Input**: (128, 14) sequential tensor (14 sensor channels × 128 timesteps)
- **Edge deployment**: TFLite INT8 quantization (<50KB, <30ms on ESP32-S3)

## 🔧 Hardware (~$110-165 total)

See [HARDWARE_BOM.md](HARDWARE_BOM.md) for complete bill of materials and wiring diagram.

Key sensors:
- **IMU**: MPU6050 (6-axis, I2C, 50Hz) — mounted under seat center
- **Load Cells**: 4× 50kg half-bridge with HX711 ADC — at chair corners
- **Thermal**: AMG8833 8×8 IR grid — on backrest for presence detection
- **MCU**: ESP32-S3 (dual-core 240MHz, 8MB PSRAM, WiFi/BLE)
- **Gateway**: Raspberry Pi 4B (complex ML + cloud)

## 🚀 Quick Start

### 1. Install Dependencies
```bash
pip install numpy scipy scikit-learn xgboost tensorflow matplotlib pandas joblib
```

### 2. Run Full Demo (Synthetic Data)
```bash
python -m smart_chair.main
```

### 3. Train Production Model
```python
from smart_chair.ml_models.posture_classifier import EnsemblePostureClassifier
from smart_chair.data_collection.synthetic_data_generator import generate_dataset, subject_based_split

dataset = generate_dataset(n_subjects=20, samples_per_posture_per_subject=500)
train, test = subject_based_split(dataset)

clf = EnsemblePostureClassifier()
clf.train(train["imu_data"], train["load_data"], train["labels"],
          test["imu_data"], test["load_data"], test["labels"])
clf.save("posture_model.joblib")
```

## 📁 Project Structure

```
smart_chair/
├── config/settings.py              # All system constants and parameters
├── utils/feature_engineering.py    # Feature extraction pipeline (14 channels)
├── data_collection/
│   ├── synthetic_data_generator.py # Physics-based synthetic data
│   └── protocol.py                 # Real data collection procedure
├── ml_models/
│   ├── posture_classifier.py       # Ensemble + MLSTM-FCN classifiers
│   ├── spine_risk_predictor.py     # RULA scorer + fatigue detector + alerts
│   ├── user_recognition.py         # Multi-user recognition + personalized model
│   ├── break_recommendation.py     # Adaptive break engine + exercise suggester
│   └── gamification.py             # Points, badges, streaks, leaderboard
├── cloud/aws_iot_integration.py    # MQTT client + AWS infrastructure
├── firmware/esp32_firmware.ino     # ESP32-S3 Arduino firmware
├── HARDWARE_BOM.md                 # Parts list + wiring diagram
└── main.py                         # System orchestrator (Raspberry Pi)
```

## 📚 Research References

| Paper | Contribution | ArXiv |
|-------|-------------|-------|
| SitPose | Ensemble posture classification (F1=98.2%) | [2412.12216](https://arxiv.org/abs/2412.12216) |
| MLSTM-FCN | Multivariate time series architecture | [1801.04503](https://arxiv.org/abs/1801.04503) |
| FusionActNet | Static/Dynamic dual-expert IMU classification | [2310.02011](https://arxiv.org/abs/2310.02011) |
| SSL-Wearables | Self-supervised pre-training for HAR | [2206.02909](https://arxiv.org/abs/2206.02909) |
| UniMTS | Foundation model for IMU (zero-shot) | [2410.19818](https://arxiv.org/abs/2410.19818) |
| DULA/DEBA | Differentiable ergonomic assessment | [2205.03491](https://arxiv.org/abs/2205.03491) |
| TinyNav | TFLite Micro on ESP32 (<30ms) | [2603.11071](https://arxiv.org/abs/2603.11071) |
| AuthentiSense | Few-shot biometric user authentication | [2302.02740](https://arxiv.org/abs/2302.02740) |
| Edge Impulse | TinyML MLOps platform | [2212.03332](https://arxiv.org/abs/2212.03332) |

## 📄 License

MIT License — Free for personal and commercial use.