Re-run OpenSim ID + static optimization (Rajagopal2016): fix knee/left-hip sign conventions, 6 Hz GRF filtering, per-leg validity masks, updated README

README.md

@@ -13,42 +13,71 @@ tags:
 
 **MuscleMimic paper:** [Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale](https://arxiv.org/abs/2603.25544).
 
-This dataset contains per-subject, per-trial `.npz` files exported from processed OpenSim inverse dynamics results for level walking.
+This dataset contains per-subject, per-trial `.npz` files exported from OpenSim inverse dynamics results for level walking. It also has muscle activations and forces from static optimization.
 
 The files are intended for comparing policy inference outputs against OpenSim ID torques. Each exported trial uses the kinematics timeline as the master timeline, with time and timestep reset to start from zero.
 
 ID torques are normalized by the subject's weight in kg from `Subject_Demographics.xlsx`. GRF force components are normalized by body weight in Newtons, computed as `subject_weight_kg * 9.80665`.
 
-## Processing Notes
-
-The `.npz` files are exported from the processed OpenSim ID outputs in `OpenSim_ID/batch_id_results`.
+**Source data:** Gait120 (Koo, Boo, Seo, Kim, 2025), [doi:10.6084/m9.figshare.27677016](https://doi.org/10.6084/m9.figshare.27677016), CC BY 4.0.
 
-Before inverse dynamics, the source pipeline applies the following trial-level processing:
+**Coverage:** 419 trials from 101 subjects (S011–S120). This is every trial with both `Step01` and `Step02`. S001–S010 have no force-plate data in Gait120, so they are not included.
 
-- `Step01` and `Step02` kinematics and GRF files are combined into one `Step01_Step02` window.
-- Kinematics are low-pass filtered before ID.
-- Subject-specific scaled OpenSim models are used when available, preferring `OpenSim_ID/scaled_models/<subject>/<subject>_scaled.osim`.
-- Trials are processed only when both step GRF files pass the double-peak vertical GRF check used by the batch ID script.
-- GRF point columns are converted from millimeters to meters when needed.
-- GRF force channels are assigned to `calcn_r` or `calcn_l` using TRC heel/toe marker proximity during stance. If TRC marker matching is unavailable, the script falls back to the COP-based side heuristic.
-- A per-trial, per-force-channel COP offset is estimated when the COP point is far from the assigned foot marker segment. This corrects force-plate point locations before ID, for example when one channel's COP is shifted by approximately a force-plate-length offset.
-- Trials are skipped during batch ID if the corrected COP has too little early-to-late displacement along the assigned foot's heel-to-toe marker segment, too little early-to-late movement in the lab frame, or remains too far from the assigned foot after correction. These checks are normalized by the subject's heel-to-toe marker length, so the QC is not based on an absolute foot size.
+## Processing Notes
 
-The exported `computed_grf` therefore reflects the corrected GRF file used for inverse dynamics. Force-vector columns are normalized by body weight in Newtons in the `.npz`; COP and ground-torque columns remain in their corrected physical units.
+- **Model.** The generic OpenSim `Rajagopal2016.osim` is scaled per subject. Gait120 has no static
+  trial, so segment scale factors come from median marker distances over all walking frames. Hip
+  joint centres use the Harrington regression. Mass comes from `Subject_Demographics.xlsx`.
+- **Windows.** `Step01` and `Step02` kinematics and GRF are combined into one `Step01_Step02` window.
+- **Sign conventions.** The Gait120 joint angles come from a modified Rajagopal model with two
+  differences from Rajagopal2016:
+  - knee extension is positive;
+  - the left hip adduction and rotation axes are mirrored.
+
+  Both are converted before ID. This was checked against the markers: the left leg sits 8–17 cm
+  from its markers with the values as given, and ~2 cm after the correction. Every
+  `joint_torque` column is reported in the same sign convention as its `joint_angle` column.
+- **Locked coordinates.** Coordinates locked in Rajagopal2016 (subtalar, mtp, wrist) are held at 0
+  during ID. `joint_angle` keeps the raw Gait120 values.
+- **Filtering.** Kinematics and GRF are both low-pass filtered at 6 Hz. GRF force and moment are
+  filtered, and the COP is then rebuilt.
+- **Force plates.** Each plate contact is assigned to `calcn_r` or `calcn_l` by its COP distance to
+  the TRC heel/toe markers. A contact is rejected (both feet on one plate, or a partial hit) when
+  its mean COP is more than 10 cm from the assigned foot.
+- **Validity.** Gait120 has only two force plates, so a foot often lands off them. Foot contact is
+  detected from heel/toe marker speed. `joint_torque` is NaN wherever it is not valid, judged per
+  column:
+  - leg columns are valid when that foot is in swing or fully on a plate;
+  - pelvis columns are valid only when every grounded foot is measured;
+  - lumbar columns are always valid.
+
+  A leg's joint moments depend only on that leg's segments and its own GRF, so one leg can be
+  valid while the other foot is down off-plate.
+- **QC.** The median RMS pelvis residual in fully measured frames is 2.3 % BW (ML), 5.4 % BW
+  (vertical) and 3.2 % BW (AP). Per-trial flags are in the `qc_notes` key. Two are worth knowing:
+  - S036's GRF suggests the recorded body mass is too low, so its per-kg values are inflated.
+  - S100 Trial02 has a partial plate hit on the left foot.
+
+The exported `computed_grf` is the filtered GRF used for inverse dynamics, in per-plate columns.
+Force-vector columns are normalized by body weight in Newtons in the `.npz`; COP and ground-torque
+columns remain in physical units.
 
 ## Folder Structure
 
 ```text
 S011/
   S011_Trial01_Step01_Step02.npz
+  S011_Trial01_Step01_Step02_muscle_activation.sto
+  S011_Trial01_Step01_Step02_muscle_force.sto
+  S011_Trial01_Step01_Step02_muscle_activations.png
   S011_Trial02_Step01_Step02.npz
   ...
 S012/
-  S012_Trial01_Step01_Step02.npz
   ...
 ```
 
-Each `.npz` file corresponds to one subject/trial/step window.
+Each `.npz` file corresponds to one subject/trial/step window. Every trial also has three muscle
+files (see [Muscle Activations and Forces](#muscle-activations-and-forces)).
 
 ## Arrays
 
@@ -65,22 +94,56 @@ Each file contains:
 | `original_kinematics_time_sec` | `(T,)` | Original OpenSim kinematics timestamps before zero-shifting. |
 | `id_source_time_sec` | `(N_id,)` | Original ID timestamps shifted relative to the first kinematics frame. |
 | `original_id_source_time_sec` | `(N_id,)` | Original OpenSim ID timestamps before zero-shifting. |
-| `joint_angle` | `(T, N_angle)` | Joint angle / coordinate values from kinematics, on the kinematics timeline. |
+| `joint_angle` | `(T, N_angle)` | Joint angle / coordinate values from the Gait120 kinematics, on the kinematics timeline. Degrees for rotations, metres for pelvis translations. |
 | `joint_angle_columns` | `(N_angle,)` | Column names for `joint_angle`. |
-| `joint_torque` | `(T, N_torque)` | ID torques from the processed OpenSim ID run, aligned onto the kinematics timeline and divided by `subject_weight_kg`. Missing rows are `NaN`. |
+| `joint_torque` | `(T, N_torque)` | ID torques aligned onto the kinematics timeline and divided by `subject_weight_kg`, in the same sign convention as `joint_angle`. Values are `NaN` wherever that column is not valid (see Processing Notes). |
 | `joint_torque_columns` | `(N_torque,)` | Column names for `joint_torque`. |
-| `joint_torque_valid` | `(T,)` | Boolean mask where `joint_torque` has valid ID values. |
+| `joint_torque_valid` | `(T,)` | Boolean mask of rows where every `joint_torque` column is valid. |
+| `joint_torque_column_valid` | `(T, N_torque)` | Boolean per-column validity. Use it to keep a leg's torques when only the other foot is unmeasured. |
+| `joint_torque_sign_convention` | scalar string | Sign convention of `joint_torque`. |
 | `joint_torque_normalization` | scalar string | Normalization applied to `joint_torque`, currently `divided_by_subject_weight_kg`. |
 | `joint_torque_units` | scalar string | Normalized torque units, currently `original_ID_units_per_kg`. |
 | `subject_weight_kg` | scalar float | Subject weight used for normalizing `joint_torque`. |
 | `subject_body_weight_n` | scalar float | Subject body weight in Newtons, computed as `subject_weight_kg * 9.80665`. |
-| `computed_grf` | `(T, N_grf)` | Corrected GRF values used by ID, interpolated onto the kinematics timeline. Force-vector columns are divided by `subject_body_weight_n`; COP and torque columns remain in corrected physical units. |
+| `computed_grf` | `(T, N_grf)` | GRF values used by ID, interpolated onto the kinematics timeline. Force-vector columns are divided by `subject_body_weight_n`; COP and torque columns remain in physical units. |
 | `computed_grf_columns` | `(N_grf,)` | Column names for `computed_grf`. |
 | `computed_grf_valid` | `(T,)` | Boolean mask where `computed_grf` has valid values. |
 | `computed_grf_normalization` | scalar string | Normalization applied to `computed_grf`, currently `force_columns_divided_by_subject_body_weight_n`. |
-| `computed_grf_units` | scalar string | Force columns are in body weight units; COP and torque columns remain in corrected physical units. |
+| `computed_grf_units` | scalar string | Force columns are in body weight units; COP and torque columns remain in physical units. |
+| `qc_notes` | scalar string | Quality-control flags for this trial; empty when there are none. |
 | `source_files` | `(3,)` | Source kinematics, ID torque, and GRF file paths used for export. |
 
+## Muscle Activations and Forces
+
+| File | Contents |
+| --- | --- |
+| `*_muscle_activation.sto` | Activations (0–1) of the 80 Rajagopal2016 muscles. |
+| `*_muscle_force.sto` | Muscle forces (N), then reserve and residual actuators, then the applied external loads. |
+| `*_muscle_activations.png` | Eight key muscles, right vs left. |
+
+- **Method:** OpenSim static optimization with the same scaled model, joint angles and GRFs as the
+  inverse dynamics. The cost is the sum of squared activations, with force-length-velocity on.
+- **Actuators added:**
+  - strong pelvis residuals;
+  - torque actuators for the lumbar and arms, which have no muscles in this model;
+  - weak (1 Nm) leg reserves, so muscles carry the leg moments.
+- **Time window and NaN frames:** the whole trial, on the `.npz` timeline. A leg's muscles and
+  reserves are `NaN` where that leg's torques are invalid. The PNGs shade those frames grey.
+- **Muscle names** are Rajagopal2016 names, e.g. `glmed1_r`, `recfem_r`, `gasmed_r`. Earlier versions
+  of this dataset used gait2392 names, e.g. `glut_med1_r`, `rect_fem_r`, `med_gas_r`.
+- **Rigid-tendon effect:** static optimization assumes a rigid tendon, which tends to overstate
+  plantarflexor activation at push-off. Gastrocnemius often reaches 1.0 there.
+- **Muscle strength** is the generic Rajagopal2016 value and is not scaled to body mass.
+  - At about 78–84 % of stance, just before toe-off, the hip flexors and abductors saturate in 44
+    of 101 subjects, mostly the heavier ones.
+  - There, the leg reserves (`reserve_*` columns) supply part of the hip moment. Their median peak
+    is under 1 Nm, but the worst 5 % of trials reach about 40 Nm.
+  - Check `reserve_*` before relying on hip muscle forces in late stance.
+- **EMG check.** Compared with the measured Gait120 right-leg EMG over 838 gait cycles:
+  - r = 0.68–0.90 for tibialis anterior, gastrocnemii, vasti and hamstrings;
+  - r ≈ 0.5 for soleus and peroneus longus, which peak ~10 % of the cycle after EMG;
+  - rectus femoris does not match (r = −0.22).
+
 ## Policy Comparison
 
 Run policy inference using `joint_angle`. The policy output should have the same first dimension `T`.
@@ -103,4 +166,10 @@ predicted_torque = predicted_torque / subject_weight_kg
 error = predicted_torque[valid] - target_torque[valid]
 ```
 
-Rows where `joint_torque_valid` is false correspond to kinematics frames without an ID result and should be skipped for torque comparison.
+Rows where `joint_torque_valid` is false have at least one invalid column. To compare one leg's torques
+while the other foot is down off the force plates, mask per column with `joint_torque_column_valid`:
+
+```python
+col_valid = data["joint_torque_column_valid"]
+error = np.where(col_valid, predicted_torque - target_torque, np.nan)
+```

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