Ishan2607/smartchair-ai

🪑 SmartChair AI — Intelligent Posture Monitoring System

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

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🎯 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 (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 + FusionActNet
• 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 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

pip install numpy scipy scikit-learn xgboost tensorflow matplotlib pandas joblib

2. Run Full Demo (Synthetic Data)

python -m smart_chair.main

3. Train Production Model

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
MLSTM-FCN	Multivariate time series architecture	1801.04503
FusionActNet	Static/Dynamic dual-expert IMU classification	2310.02011
SSL-Wearables	Self-supervised pre-training for HAR	2206.02909
UniMTS	Foundation model for IMU (zero-shot)	2410.19818
DULA/DEBA	Differentiable ergonomic assessment	2205.03491
TinyNav	TFLite Micro on ESP32 (<30ms)	2603.11071
AuthentiSense	Few-shot biometric user authentication	2302.02740
Edge Impulse	TinyML MLOps platform	2212.03332

📄 License

MIT License — Free for personal and commercial use.