s2s-certify 1.6.6

Physics-certified motion data toolkit for Physical AI training

S2S — Physics Certification for Motion Data

Bad robot and human motion training data costs you months. S2S finds it in seconds.

Using S2S on your data? Open a GitHub Discussion — I will personally help you integrate it with your dataset for free. Looking for the first 5 research partners.

from s2s_standard_v1_3 import S2SPipeline

pipe = S2SPipeline(segment="forearm")
result = pipe.certify(
    imu_raw={"timestamps_ns": ts, "accel": acc, "gyro": gyro},
    instruction="pick up the cup",   # optional: text intent
    video_frame=jpeg_bytes,          # optional: JPEG from camera/VR/sim
)

print(result["tier"])          # GOLD / SILVER / BRONZE / REJECTED
print(result["score"])         # 0–100
print(result["source_type"])   # HIL_BIOLOGICAL
print(result["intent"])        # "pick object"  (0.887 similarity)
print(result["next_motion"])   # 8-dim next action prediction
print(result["clip_sim"])      # scene-instruction visual match

---

Why this exists

Most motion datasets contain bad data — corrupted recordings, synthetic signals that violate physics, mislabeled actions. You cannot see it by looking at the numbers. Your model trains on it anyway.

S2S asks: does this data obey the physics of human movement? A perfect statistical fake fails if it violates Newton's Second Law, segment resonance, or rigid body kinematics.

S2S does not replace existing AI systems. It adds a physics reality-check to any visual or physical AI pipeline. Camera, VR render, AR overlay, simulation frame — all go through the same 7-law certification before becoming training data.

For robotics and embodied AI pipelines, S2S covers the full data trust checklist: synchronized stream alignment (±50ms enforcement), physical consistency (7 laws), provenance (Ed25519 signing), biological origin validation (Hurst exponent), segment-level quality control (GOLD/SILVER/BRONZE/REJECTED), and rejection of fake or corrupted windows.

Validated results across 7 datasets:

Dataset	Hz	Sensors	Result
UCI HAR	50Hz	IMU	+2.51% F1 vs corrupted baseline
PAMAP2	100Hz	IMU ×3	+4.23% F1 kinematic chain vs single sensor
WISDM 2019	20Hz	IMU	+1.74% F1 vs corrupted baseline
WESAD	700Hz	IMU + BVP	+3.1% F1 stress classification
RoboTurk Open-X	15Hz	Robot arm	21.9% of teleoperation data rejected as physically invalid
NinaPro DB5	2000Hz	Forearm EMG+IMU	9,552 windows certified, Law 1 EMG validated 78%
OPPORTUNITY	30Hz	Body IMU ×7	25.7% rejection rate found with real gyro
PhysioNet PTT-PPG	500Hz	Wrist PPG+IMU	1,164 windows certified

---

Real-time performance

Certified on real NinaPro DB5 data (2000Hz, 500-sample windows):

Metric	Value
Mean latency	2.8ms
Window duration	250ms
CPU overhead	1.1%
Real-time feasible	✅ Yes (89× faster than real-time)
Prosthetics safety threshold	50ms — S2S is 18× below

Laws run on IMU-only data (no gyro hardware):

• resonance_frequency: RUNS (conf=26)
• rigid_body_kinematics: RUNS (conf=45, zero gyro = low coupling)
• jerk_bounds: RUNS (conf=92) ← primary detector at 2000Hz
• imu_internal_consistency: RUNS (conf=45)
• newton_second_law: skipped (needs EMG)
• ballistocardiography: skipped (needs PPG)
• joule_heating: skipped (needs thermal)

With full sensor stack (IMU + EMG + PPG + gyro): all 7 laws run.

Install

pip install s2s-certify
pip install "s2s-certify[ml]"        # with PyTorch — enables Layers 4 and 5
pip install "s2s-certify[dashboard]" # with Streamlit

Layer 5 visual understanding also requires:

pip install git+https://github.com/openai/CLIP.git
pip install sentence-transformers

---

Full 7-layer demo output

Run on a real DROID robot manipulation episode:

python3.9 s2s_demo.py --droid ~/droid_data/droid_100/1.0.0

════════════════════════════════════════════════════════════
  S2S — Full Chain Demo  (v1.6.4)
  7 Layers: Physics → Biology → Motion → Visual
════════════════════════════════════════════════════════════

  S2SPipeline(segment=forearm,
    Layer1(Physics) + Layer2(BioSession) + Layer4a(NextAction) +
    Layer4b(GapFill) + Layer4c(Intent) + Layer3(Retrieval) + Layer5(CLIP))

[INPUT] Instruction: 'Put the marker inside the silver pot'
        Video frame: 31,964 bytes JPEG

  ───────────────────────────────────────────────────────
  LAYER 1 — Physics Certification
  ───────────────────────────────────────────────────────
  tier                   SILVER
  score                  66/100
  source_type            HIL_BIOLOGICAL
  laws_passed            ['resonance_frequency', 'rigid_body_kinematics',
                          'jerk_bounds', 'imu_internal_consistency', ...]
  laws_failed            []

  ───────────────────────────────────────────────────────
  LAYER 2 — Biological Origin (Session)
  ───────────────────────────────────────────────────────
  biological_grade       NOT_BIOLOGICAL
  hurst                  0.4917       (synthetic test signal, <0.70)
  bfs_score              0.7468
  n_windows              9
  recommendation         REJECT
  note                   HUMAN grade requires real session data (NinaPro r=0.929)

  ───────────────────────────────────────────────────────
  LAYER 3 — Semantic Motion Retrieval
  ───────────────────────────────────────────────────────
  match_1                1.0000  Put the marker inside the silver pot
  match_2                0.7268  Put the marker inside the blue cup
  match_3                0.6658  Take the marker from the bowl and put it on the table

  ───────────────────────────────────────────────────────
  LAYER 4a — Next Action Prediction
  ───────────────────────────────────────────────────────
  next_motion_8dim       [-0.846, 1.354, 2.997, -0.908, 1.326, 2.946, -0.143, -0.400]
  note                   pos_xyz + vel_xyz + jerk_rms + smoothness

  ───────────────────────────────────────────────────────
  LAYER 4b — Gap Filling (3 intermediates)
  ───────────────────────────────────────────────────────
  t=1/4                  [5.717, 4.739, 11.777, 0.345]
  t=2/4                  [5.888, 4.883, 11.695, 0.384]
  t=3/4                  [5.956, 4.943, 11.316, 0.406]

  ───────────────────────────────────────────────────────
  LAYER 4c — Intent Recognition
  ───────────────────────────────────────────────────────
  intent                 Put the marker inside the silver pot
  confidence             1.0000
  method                 text query override

  ───────────────────────────────────────────────────────
  LAYER 5 — Visual Understanding (CLIP)
  ───────────────────────────────────────────────────────
  clip_sim               0.2253
  visual_input           31,964 byte JPEG
  instruction            Put the marker inside the silver pot

════════════════════════════════════════════════════════════
  CHAIN SUMMARY
════════════════════════════════════════════════════════════
  Physics tier:       SILVER (score 66/100)
  Biological grade:   NOT_BIOLOGICAL (Hurst 0.4917, synthetic test signal)
  Top intent:         Put the marker inside the silver pot (1.0)
  Next motion:        ✓ predicted
  Gap fill:           ✓ 3 intermediates
  Scene similarity:   0.2253

  Full chain: sensor → physics → biology → intent → motion → visual
════════════════════════════════════════════════════════════

Layer 5 — Visual discrimination stress test

Same scene, three different instructions — proves the system is not random:

Instruction	Similarity	vs correct
"Put the marker inside the silver pot" (correct)	0.2253	baseline
"pick up the blue cup and place on shelf" (wrong object)	0.2108	−7%
"walking down the street" (completely irrelevant)	0.1327	−41%

An irrelevant command scores 41% lower on the same scene. Zero-shot, no fine-tuning.

---

Reproducible benchmark

Run in one command:

pip install s2s-certify
git clone https://github.com/timbo4u1/S2S
cd S2S
python3.9 run_benchmark.py

Expected output:

real_human (NinaPro/PAMAP2/WESAD): 20/21 certified (95%)
corrupted_spikes (NinaPro+injected): 3/3 correctly downgraded to BRONZE
pure_synthetic (Gaussian noise):     1/5 rejected (Gaussian can satisfy laws by chance)
Overall: 27/32 (84%) — hard negatives added, adaptive window fix applied April 2026

Note: WESAD/NinaPro run 3/7 laws (no gyro). PAMAP2 runs 7/7 laws.

PAMAP2 without S2S filtering: baseline F1
PAMAP2 with S2S filtering:    +4.23% F1
WESAD stress classification:  +3.1% F1
RoboTurk teleoperation data:  21.9% rejected as physically invalid

Full results: experiments/s2s_reference_benchmark.json

Status & Roadmap

The core 7-layer pipeline is complete and working. Next development direction depends on what real users need.

If you are using S2S on your data — even just experimenting — open a GitHub Discussion or email [e-mail]. One sentence about your use case helps more than you think.

Current planned work without user input:

• Layer 6: LLM semantic reasoning (jerk limits from natural language)
• CLIP fine-tuning on DROID (0.23 → 0.6+ scene similarity)
• Amputee-specific physics thresholds (Issue #5)

---

Architecture

Layer 1  Physics Certification    7 biomechanical laws, GOLD/SILVER/BRONZE/REJECTED
Layer 2  Biological Origin        Hurst exponent H≥0.70, HUMAN/NOT_BIOLOGICAL
Layer 3  Motion Retrieval         text → certified motion, 11,246 windows, 6 datasets
Layer 4a Next Action Prediction   Transformer, mean r=0.929, 21,896 training pairs
Layer 4b Gap Filling              +5.5% over linear interpolation, Flash & Hogan 1985
Layer 4c Intent Recognition       top-5 75.9%, 71 labels, sentence-transformers
Layer 5  Visual Understanding     CLIP ViT-B/32, frame-synced at 15Hz

---

Layer 1 — Physics Certification

7 biomechanical laws validated at runtime:

Law	Equation	Requires	What it catches
Newton's Second Law	F = ma	IMU + EMG	EMG force must lead acceleration by ~75ms
Segment Resonance	ω = √(K/I)	IMU	Physiological tremor 8–12Hz for forearm
Rigid Body Kinematics	a = α×r + ω²×r	IMU + gyro	Gyro and accel must co-vary on a rigid body
Ballistocardiography	F = ρQv	IMU + PPG	Heartbeat recoil visible in wrist IMU at PPG rate
Joule Heating	Q = 0.75×P×t	EMG + thermal	EMG bursts must produce thermal elevation
Motor Control Jerk	d³x/dt³ ≤ 500 m/s³	IMU	Human motion limit (Flash & Hogan 1985)
IMU Internal Consistency	Var(accel) ~ Var(gyro)	IMU + gyro	Independent generators produce zero coupling

Missing sensors are skipped — they do not penalise the score.

Body segment parameters

Segment	I (kg·m²)	K (N·m/rad)	Tremor band (Hz)
forearm	0.020	1.5	8–12
upper_arm	0.065	2.5	5–9
hand	0.004	0.3	10–16
finger	0.0003	0.05	15–25
head	0.020	1.2	3–8
walking	10.0	50.0	1–3

Tier system

Tier	Condition
GOLD	score ≥ 75 AND passed ≥ n_laws − 1
SILVER	score ≥ 55
BRONZE	score ≥ 35
REJECTED	>30% laws failed OR score < 35

---

Layer 2 — Biological Origin

pe = PhysicsEngine()
for window in session_windows:
    pe.certify(imu_raw=window, segment="forearm")

verdict = pe.certify_session()
print(verdict["biological_grade"])   # HUMAN / LOW_BIOLOGICAL_FIDELITY / NOT_BIOLOGICAL
print(verdict["hurst"])              # ≥0.70 = biological motor control
print(verdict["recommendation"])     # ACCEPT / REVIEW / REJECT

---

Layer 3 — Motion Retrieval

pipe = S2SPipeline(segment="forearm")
matches = pipe.query_intent("pick up the cup", top_k=3)
# [("pick object", 0.484), ("drinking", 0.411), ("Put the marker inside the blue cup", 0.379)]

---

Layer 4 — Action Sequencing

4a — Next action prediction (mean r=0.929)

result = pipe.certify(imu_raw=window)
print(result["next_motion"])  # 8-dim: pos_xyz + vel_xyz + jerk_rms + smoothness

4b — Gap filling (+5.5% over linear interpolation)

gaps = pipe.fill_gap(start_features, end_features, n_steps=3)

Position	Neural r	Linear r	Improvement
t=0.33	0.944	0.889	+0.055
t=0.50	0.960	0.909	+0.051
t=0.67	0.945	0.889	+0.056

4c — Intent recognition (top-5 accuracy 75.9%)

result = pipe.certify(imu_raw=window, instruction="pick up cup")
print(result["intent"])       # "pick object"
print(result["intent_sim"])   # 0.887

---

Layer 5 — Visual Understanding

Frame-synchronized CLIP ViT-B/32 at 15Hz. Each motion window pairs with the video frame at that exact timestep. Accepts any visual input: camera, VR render, AR overlay, simulation frame.

result = pipe.certify(
    imu_raw=window,
    instruction="Put the marker inside the silver pot",
    video_frame=jpeg_bytes,
)
print(result["clip_sim"])   # 0.2253

---

All 6 sensor certifiers

from s2s_standard_v1_3 import PhysicsEngine
result = PhysicsEngine().certify(imu_raw={...}, segment="forearm")

from s2s_standard_v1_3.s2s_emg_certify_v1_3 import EMGStreamCertifier
ec   = EMGStreamCertifier(n_channels=8, sampling_hz=1000.0)
cert = ec.push_frame(ts_ns, [ch0..ch7])

from s2s_standard_v1_3.s2s_ppg_certify_v1_3 import PPGStreamCertifier
pc   = PPGStreamCertifier(n_channels=2, sampling_hz=100.0)
cert = pc.push_frame(ts_ns, [red, ir])

from s2s_standard_v1_3.s2s_lidar_certify_v1_3 import LiDARStreamCertifier
lc   = LiDARStreamCertifier(mode='scalar')
cert = lc.push_frame(ts_ns, [distance_m])

from s2s_standard_v1_3.s2s_thermal_certify_v1_3 import ThermalStreamCertifier
tc   = ThermalStreamCertifier(frame_width=32, frame_height=24)
cert = tc.push_frame(ts_ns, flat_pixels)

from s2s_standard_v1_3.s2s_fusion_v1_3 import FusionCertifier
fc = FusionCertifier(device_id="glove_v2")
fc.add_imu_cert(imu_cert)
fc.add_emg_cert(emg_cert)
result = fc.certify()
print(result["human_in_loop_score"])   # 0–100

---

Cryptographic signing

from s2s_standard_v1_3.s2s_signing_v1_3 import CertSigner, CertVerifier

signer, verifier = CertSigner.generate()
signer.save_keypair("keys/device_001")
signed = signer.sign_cert(cert_dict)
ok, reason = verifier.verify_cert(signed)

---

Device registry

from s2s_standard_v1_3.s2s_registry_v1_3 import DeviceRegistry

reg = DeviceRegistry("registry.json")
reg.register(
    device_id="glove_v2_001",
    sensor_profile="imu_9dof",
    owner="[e-mail]",
    expected_jitter_ns=4500.0,
    public_key_pem=signer.export_public_pem(),
    trust_tier="PROVISIONAL",
)
ok, reason, device = reg.validate_cert(cert_dict)
reg.promote("glove_v2_001")

---

REST API

python3 -m s2s_standard_v1_3.s2s_api_v1_3 --port 8080

Method	Path	Description
POST	/certify/imu	Batch IMU certification
POST	/certify/emg	Batch EMG certification
POST	/certify/lidar	Batch LiDAR
POST	/certify/thermal	Batch thermal frames
POST	/certify/ppg	Batch PPG certification
POST	/certify/fusion	Fuse 2–5 stream certs
POST	/stream/frame	Push frame to persistent session
GET	/health	Health + active sessions

---

LeRobot / Hugging Face Integration

Certify any LeRobot dataset episode directly:

from s2s_standard_v1_3.adapters.lerobot import certify_lerobot_dataframe
import pandas as pd

df = pd.read_parquet('episode_000000.parquet')
result = certify_lerobot_dataframe(df, hz=30.0, segment='forearm')

print(result['pass_rate'])     # 0.847
print(result['summary_tier'])  # SILVER
print(result['rejected'])      # windows with physics violations

For datasets with real IMU/acceleration streams. Standard simulation datasets (PushT, ALOHA) use joint positions — pass accel_cols explicitly for those. See adapters/lerobot.py for details.

Real-Time Safety Gate

Monitor sensor data quality in real-time — 2.8ms latency at 2000Hz:

from s2s_standard_v1_3 import RealTimeSafetyGate

gate = RealTimeSafetyGate(segment="forearm", strikes=3)

for ts_ns, accel, gyro in sensor_stream:
    is_safe, reason, cert = gate.push(ts_ns, accel, gyro)
    if not is_safe:
        print(f"UNSAFE: {reason}")
        # halt pipeline / alert operator / log event

States:

• SAFE — SILVER or GOLD, data is physically trustworthy
• DEGRADED — BRONZE, quality reduced but not dangerous
• UNSAFE — 3 consecutive REJECTED windows, action required

Latency: 2.8ms per window at 2000Hz (1.1% CPU overhead). Three-strike logic prevents false triggers from single noise samples.

Roadmap — Layer 6: Semantic Reasoning

The current 7 layers certify that motion is physically real and visually consistent. Layer 6 adds semantic reasoning — bridging natural language intent to physics constraints:

User says:   "Feed the baby"
Layer 5:     sees spoon, bowl, baby face
Layer 6:     translates to physics constraints:
               jerk ≤ 50 m/s³  (gentle)
               speed ≤ 0.3 m/s
               trajectory toward face
             Layer 1 validates before robot executes

Requires LLM cross-attention to physical trajectory space. Planned after first external users.

---

Quick CLI

s2s-certify yourfile.csv
s2s-certify yourfile.csv --output report.json --segment forearm

Zero-config Python API — auto-detects columns, Hz, and units:

from s2s_standard_v1_3.adapters.column_detect import certify_file

# Works on any CSV or space-delimited sensor file
# No column names needed — detects accel/gyro from data statistics
result = certify_file("your_sensor_data.csv", segment="forearm")

print(result["tier"])             # SILVER
print(result["pass_rate"])        # 0.93
print(result["detected_hz"])      # 30.3 (auto-detected from timestamps)
print(result["detected_columns"]) # {"accel": [2,5,17], "gyro": [1,16,39]}

Auto-detect is best for unknown datasets without documentation. For documented datasets, specify columns explicitly for highest accuracy.

---

Live demos

→ IMU Demo · → Physical AI Demo · → Live API · → Paper PDF · DOI: 10.5281/zenodo.18878307

---

Support this project

S2S is free for research. If it saved you time or helped your pipeline:

Crypto donations (any amount):

Network	Address
USDT TRC20	TXRSTigHsk5QgAiuN2JJyByRuXmawVxdBP
ETH ERC20	0x6E7B9d022DC49bfc21C3011E15A0bF2868ec9c26
SOL	4gVZxGec3GefmkME4UXpn1CmrzvsiHguASqArxnSjSHY

Or open a GitHub Discussion — feedback is worth more than money at this stage.

License

BSL-1.1 — free for research and non-commercial use. Apache 2.0 from 2028-01-01.