s2s-certify 1.6.1

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.

from s2s_standard_v1_3 import PhysicsEngine

result = PhysicsEngine().certify(
    imu_raw={
        "timestamps_ns": timestamps,
        "accel": accel_data,   # [[x,y,z], ...] m/s²
        "gyro":  gyro_data,    # [[x,y,z], ...] rad/s
    },
    segment="forearm"
)

print(result["tier"])                  # GOLD / SILVER / BRONZE / REJECTED
print(result["physical_law_score"])    # 0–100
print(result["laws_passed"])
print(result["laws_failed"])

---

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.

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
PhysioNet PTT-PPG	500Hz	Wrist PPG+IMU	1,164 windows certified

---

Install

pip install s2s-certify
pip install "s2s-certify[ml]"        # with PyTorch
pip install "s2s-certify[dashboard]" # with Streamlit

Zero runtime dependencies. Pure Python 3.9+.

---

Quick CLI

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

---

Architecture — how it all fits together

S2S has two parts: a production system (Layers 1–4) and a research scaffolding (Modules 1–4) that was used to build and validate it.

WHAT IT DOES (Layers)          HOW IT WAS BUILT (Modules)
─────────────────────────────────────────────────────────

Layer 1: Physics Certification  ← Module 1: Corruption Fingerprinter
  7 biomechanical laws             mapped which corruptions break which law
  GOLD/SILVER/BRONZE/REJECTED      proved thresholds are not arbitrary

Layer 2: Quality Prediction     ← Module 2: Frankenstein Mixer
  Dual-head AI on certified data   found exact physics boundary (binary search)
  smoothness r=0.941               proved S2S finds a real data quality cliff

                                ← Module 3: Curriculum Generator
                                   generated training data at all quality levels

                                ← Module 4: Cloud Trainer
                                   trained quality predictor on curriculum

Layer 3: Motion Retrieval          (no separate module — built directly)
  "pick up cup" → certified motion
  10,686 certified windows

Layer 4: Action Sequencing         (no separate module — built directly)
  4a predict next  r=0.958
  4b gap filling   +5.5% vs linear
  4c intent        top-5 75.9%

Layer 5: Scenario Understanding    ❌ planned
  video + language input

---

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 — Quality Prediction

Dual-head model trained on 3,487 certified pairs (quality head 72.2%, smoothness r=0.941):

from s2s_standard_v1_3.s2s_ml_interface import S2SFeatureExtractor, physics_loss

extractor = S2SFeatureExtractor(segment="forearm")
features  = extractor(imu_raw)   # 15-dim: 7 pass/fail + 7 scores + 1 overall

# Physics-informed training regularisation
loss = task_loss + physics_loss(scores, lambda_phys=0.1)

---

Layer 3 — Motion Retrieval

10,686 certified windows across 6 datasets, semantic text-to-motion search:

# "pick up cup" → nearest GOLD certified motions
# Ranking: semantic 0.30 + embedding cosine 0.25 + physics 0.25 + smoothness 0.15 + tier 0.05

---

Layer 4 — Action Sequencing

4a — Next action prediction (mean r=0.958)

Trained on 21,896 consecutive pairs from 5 datasets:

from experiments.layer4_sequence_model import predict_next
next_features = predict_next(current_features)

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

Trained on 87,529 quadruplet samples at t=1/3, 1/2, 2/3. Captures the non-linear bell-shaped velocity profile of human arm motion (Flash & Hogan 1985):

from experiments.layer4b_gap_filling import fill_gap
intermediates = 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%)

from experiments.layer4c_intent_recognition import query_by_text, classify_motion
matches = query_by_text("pick up cup", top_k=5)
# Returns: "pick object" (0.484), "drinking" (0.411), "hand grasp" (0.287)

---

All 6 sensor certifiers

IMU — PhysicsEngine

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

EMG — EMGStreamCertifier

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])

PPG — PPGStreamCertifier

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])
print(cert["vitals"]["heart_rate_bpm"])
print(cert["vitals"]["hrv_rmssd_ms"])

LiDAR — LiDARStreamCertifier

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

Thermal — ThermalStreamCertifier

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)  # 768 floats in °C
print(cert["human_presence"]["human_present"])

Multi-sensor Fusion — FusionCertifier

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)
fc.add_ppg_cert(ppg_cert)
result = fc.certify()
print(result["human_in_loop_score"])   # 0–100
fc.reset()                             # clear for reuse

---

Biological origin detection

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"])              # ≥0.7 = biological motor control
print(verdict["recommendation"])     # ACCEPT / REVIEW / REJECT

---

REST API

python3 -m s2s_standard_v1_3.s2s_api_v1_3 --port 8080 \
    --sign-key keys/server.private.pem \
    --registry registry.json

Method	Path	Description
POST	/certify/imu	Batch IMU certification
POST	/certify/emg	Batch EMG certification
POST	/certify/lidar	Batch LiDAR (scalar or pointcloud)
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

---

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")

---

Full project structure

s2s_standard_v1_3/               ← Production package (pip install s2s-certify)
  s2s_physics_v1_3.py            ← PhysicsEngine — 7 laws, zero dependencies
  s2s_emg_certify_v1_3.py        ← EMG certifier (≥500Hz)
  s2s_ppg_certify_v1_3.py        ← PPG — HR, HRV, breathing
  s2s_lidar_certify_v1_3.py      ← LiDAR (1D scalar + 3D pointcloud)
  s2s_thermal_certify_v1_3.py    ← Thermal — human body heat detection
  s2s_fusion_v1_3.py             ← 5-sensor fusion, Human-in-Loop score 0–100
  s2s_stream_certify_v1_3.py     ← Real-time streaming (stdin / TCP :9876)
  s2s_api_v1_3.py                ← REST API server (stdlib, zero deps)
  s2s_signing_v1_3.py            ← Ed25519 signing + HMAC-SHA256 fallback
  s2s_registry_v1_3.py           ← Device registry — trust tiers, revocation
  s2s_ml_interface.py            ← PyTorch Dataset, feature extractor, physics loss
  convert_to_s2s.py              ← CSV → .s2s binary
  cli.py                         ← s2s-certify CLI entry point
  constants.py                   ← TLV registry, tiers, sensor profiles

experiments/
  ── Research Modules (built and validated Layers 1–2) ──
  module1_corruption_fingerprinter.py  ← maps which corruptions break which law
  module2_frankenstein_mixer.py        ← binary search for exact physics boundary
  module3_curriculum_generator.py      ← generates training data at all quality levels
  module4_cloud_trainer.py             ← trains quality predictor on curriculum

  ── Layer validation experiments ──
  corruption_experiment.py             ← quality floor proof (Layer 1)
  level2_pamap2_curriculum.py          ← Layer 2 validation on PAMAP2
  level2_pamap2_adaptive_tiers.py      ← adaptive tier thresholds
  level3_pamap2_kalman.py              ← Layer 3 retrieval on PAMAP2
  level3_uci_kalman.py                 ← Layer 3 retrieval on UCI HAR
  level3_wisdm_kalman.py               ← Layer 3 retrieval on WISDM
  level4_multisensor_fusion.py         ← multi-sensor kinematic chain
  level5_dualhead.py                   ← dual-head quality+motion model
  level5_weighted.py                   ← semi-supervised weighted training
  level5_full.py                       ← full Level 5 training pipeline

  ── Layer 4 (action sequencing) ──
  extract_sequences.py                 ← extract consecutive certified pairs
  extract_sequences_pamap2_wesad.py    ← add PAMAP2+WESAD to training data
  layer4_sequence_model.py             ← 4a: next action prediction (r=0.958)
  layer4b_gap_filling.py               ← 4b: gap filling (+5.5% vs linear)
  layer4c_intent_recognition.py        ← 4c: motion→intent (top-5 75.9%)

  ── Retrieval ──
  step3_retrieval.py                   ← text-to-motion retrieval v1
  step3_retrieval_v2.py                ← retrieval with semantic embeddings
  auto_router.py                       ← automatic dataset routing

  ── Results ──
  results_layer4.json                  ← 4a: r=0.958
  results_layer4b.json                 ← 4b: +5.5% vs linear
  results_layer4c.json                 ← 4c: top-5 75.9%
  results_wesad_f1.json                ← WESAD +3.1% F1
  results_level4_pamap2.json           ← multi-sensor +5.0% F1
  results_level5_dualhead.json         ← quality 72.2%, smoothness 0.941
  roboturk_audit.json                  ← RoboTurk 78.1% pass rate

Dataset adapters (root level)
  wesad_adapter.py                     ← WESAD (chest+wrist+BVP)
  wesad_f1_benchmark.py                ← F1 comparison certified vs all
  wisdm_adapter.py                     ← WISDM 2019 (51 subjects, 20Hz)
  amputee_adapter.py                   ← EMG amputee (200Hz, 17 movements)
  s2s_dataset_adapter.py               ← UCI HAR, PAMAP2, Berkeley MHAD
  certify_roboturk.py                  ← RoboTurk Open-X certification
  s2s_pipeline.py                      ← NinaPro full pipeline CLI

tests/
  test_physics_laws.py                 ← 110/110 tests passing
  test_emg_ppg.py
  test_fusion.py

model/
  s2s_quality_model_pytorch.pkl        ← trained quality predictor (Module 4)
  s2s_domain_classifier.json           ← motion domain classifier

docs/
  demo_physical_ai.html
  paper/S2S_Paper_v5.pdf

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Live demos

→ IMU Demo · → Physical AI Demo · → Live API

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Paper

→ PDF · DOI: 10.5281/zenodo.18878307

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License

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