| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| - robotics |
| tags: |
| - imu |
| - biomechanics |
| - motion-capture |
| - physical-ai |
| - prosthetics |
| - robotics |
| - sensor-certification |
| - emg |
| - ninapro |
| - gesture-recognition |
| 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 |
