Update README.md

README.md

@@ -3,7 +3,6 @@ license: cc-by-4.0
 task_categories:
 - tabular-classification
 - robotics
-- time-series-forecasting
 tags:
 - imu
 - biomechanics
@@ -18,4 +17,91 @@ tags:
 pretty_name: NinaPro DB5 — S2S Physics Certified
 size_categories:
 - 1K<n<10K
----
+---
+
+# NinaPro DB5 — S2S Physics Certified (v1.7.0)
+
+Physics-certified windows from NinaPro DB5 forearm EMG+IMU dataset.
+Each window validated against **8 biomechanical laws** using [S2S](https://github.com/timbo4u1/S2S).
+
+**Bad training data costs you months. S2S finds it in milliseconds.**
+
+## What this adds
+
+| Column | Description |
+|---|---|
+| `tier` | GOLD / SILVER / BRONZE / REJECTED |
+| `score` | 0–100 physics compliance score |
+| `laws_passed` | Which of 8 laws passed |
+| `verdict` | Human-readable quality statement |
+| `recommendation` | Actionable engineering guidance |
+| `wavelet_signal_type` | biological / mechanical_synthetic / random_noise |
+| `wavelet_cv` | Energy drift CV (real human: 0.15–1.5) |
+| `wavelet_entropy` | Spectral entropy (biological: 0.75–0.96) |
+| `issues` | Specific failures with hardware fix suggestions |
+
+## Statistics
+
+| Metric | Value |
+|---|---|
+| Windows | 1,500 |
+| Subjects | 10 |
+| Sample rate | 2000Hz |
+| Window size | 500 samples (250ms) |
+| GOLD | 109 (7.3%) |
+| SILVER | 1,388 (92.5%) |
+| BRONZE | 3 (0.2%) |
+| REJECTED | 0 |
+
+## Quick start
+
+```python
+import pandas as pd
+
+df = pd.read_csv("ninapro_db5_certified.csv")
+
+# High quality windows only
+train = df[df['tier'].isin(['GOLD', 'SILVER'])]
+
+# Confirmed biological signal
+bio = df[df['wavelet_signal_type'] == 'biological']
+
+# Score threshold
+high = df[df['score'] >= 70]
+```
+
+## The 8 Physics Laws
+
+| Law | What it catches |
+|---|---|
+| Newton F=ma | EMG-acceleration timing mismatch |
+| Segment Resonance | Non-physiological tremor frequency |
+| Rigid Body Kinematics | Decoupled accelerometer/gyroscope |
+| Ballistocardiography | Missing heartbeat signal in IMU |
+| Joule Heating | EMG-thermal mismatch |
+| Motor Control Jerk | Superhuman motion (>500 m/s³) |
+| IMU Consistency | Independent signal generators |
+| Inter-window Continuity | Teleportation / data splices |
+
+## Source dataset
+
+Original NinaPro DB5: https://ninapro.hevs.ch/instructions/DB5.html
+
+10 subjects, forearm EMG (16ch) + accelerometer (3ch) at 2000Hz.
+
+## Certification engine
+
+```bash
+pip install s2s-certify
+```
+
+```python
+from s2s_standard_v1_3.s2s_physics_v1_3 import PhysicsEngine, audit_report
+
+engine = PhysicsEngine()
+result = engine.certify(imu_raw=window, segment='forearm')
+report = audit_report(result)
+print(report['verdict'])
+```
+
+github.com/timbo4u1/S2S | DOI: 10.5281/zenodo.18878307