--- license: cc-by-4.0 language: - en pretty_name: ForceBody size_categories: - 10K/trial_.npz ├── trials_skelfit_only/ 1,734 trials. SKEL + measured GRF + tau (no Monte Carlo) │ └── /trial_.npz ├── manifest.csv 10,386 rows, one per trial (full) ├── manifest_train.csv 9,649 train rows ├── manifest_test.csv 737 test rows ├── subjects.csv 378 rows, one per subject_id_raw ├── ForceBody_sample.tar.gz 100-trial reviewer sample (157 MB) └── README.md ``` Folder names use a study-prefix when needed to disambiguate identical subject names across source datasets (`Moore2015__subject7` vs `Uhlrich2023__subject7`). Subjects unique within their source dataset (e.g. `P002_split0` from Carter2023) appear without the prefix. The study prefix is also applied where two source studies use the same subject identifier under different capitalisation, so that the layout is safe on case-insensitive filesystems (Windows, macOS default). ## Quick start for reviewers (small sample) The full release is roughly 19 GB. For a fast first look, download the 100-trial sample (157 MB), which carries the same per-trial schema as the full release. Direct download URL: ``` https://huggingface.co/datasets/ForceBody/ForceBody_ano/resolve/main/ForceBody_sample.tar.gz ``` Or with `wget` / `curl`: ```bash wget https://huggingface.co/datasets/ForceBody/ForceBody_ano/resolve/main/ForceBody_sample.tar.gz tar -xzf ForceBody_sample.tar.gz ls ForceBody_sample/ ``` The sample is stratified across all 10 source studies: 72 trials with Monte Carlo uncertainty and 28 skelfit-only trials, drawn with numpy seed 42. A `SAMPLE_README.md` inside the archive describes the selection. The schema documented in this README applies verbatim to every npz in the sample. `subject_id_raw` carries a study prefix where it is needed to disambiguate identical subject names across source datasets (`Moore2015__subject7` vs `Uhlrich2023__subject7`). Subjects unique within their source dataset (e.g. `P002_split0` from Carter2023) appear without the prefix. ## Composition 10,386 trials. 26.92 hours of motion. 140 subjects, defined as the unique pair (study, subject) after collapsing the `_splitN` rolling-window suffix used by Carter2023. Concretely, `Carter2023/P002_split0..2` count as one subject; `Moore2015/subject7` and `Uhlrich2023/subject7` count as two. The split column on `manifest.csv` is the subject-level partition inherited from AddBiomechanics. There is no leakage between train and test. | Split | Trials | Subjects (study, base) | Hours | |-------|-------:|-----------------------:|------:| | train | 9,649 | 121 | 24.93 | | test | 737 | 19 | 1.99 | | total | 10,386 | 140 | 26.92 | The Monte Carlo subset is a strict subset of these 10,386 trials. Of the released trials, 8,086 train and 566 test trials carry `sigma_tau`. The remaining 1,734 trials carry SKEL fit, measured GRF, and the deterministic inverse-dynamics torque, but no Monte Carlo annotation. Most of the 1,734 are excluded from the Monte Carlo pass because their GRF coverage falls below 50%. There is no validation split. If you need one, derive it from the train trials by hashing on `trial_uid`. ## Per-trial NPZ schema Each `trial_.npz` is a NumPy archive. `T` is the trial frame count after resampling to 100 Hz (also stored as `num_frames`). `n_c` is the number of contact bodies (`n_contacts`), which is 2 (left and right foot) in 10,316 trials and 3 (left foot, right foot, chair) in 70 sit-to-stand trials from Falisse2017. The schema below applies to every trial. Trials in `trials_skelfit_with_uncertainty/` carry six additional fields marked "MC only" below, for a total of 38 keys; trials in `trials_skelfit_only/` have 32 keys. ### Identity | Field | dtype | shape | |------------------|-------|--------| | `subject_id_raw` | str | scalar | | `subject_pure` | str | scalar | | `study` | str | scalar | | `trial_idx` | int32 | scalar | ### SKEL parameters | Field | dtype | shape | Notes | |---------|---------|----------|-------| | `poses` | float32 | `[T,46]` | SKEL pose, axis-angle per joint | | `betas` | float32 | `[10]` | SKEL shape, constant within a trial | | `trans` | float32 | `[T,3]` | Global translation, world frame, meters | ### Kinematics, AddB Rajagopal (37 DOF) | Field | dtype | shape | Notes | |----------|---------|----------|-------| | `q_gt` | float32 | `[T,37]` | Joint angles, radians for revolute and meters for prismatic | | `qd_gt` | float32 | `[T,37]` | Joint velocities | | `qdd_gt` | float32 | `[T,37]` | Joint accelerations | `dof_names` lists the 37 DOFs in order. The first 6 DOFs are the floating-base pelvis. `direct_dof_mask` is True on the 20 DOFs at the 14 anatomical joints (bilateral hip, knee, ankle, MTP, elbow, radioulnar, wrist) that have a direct correspondence to SKEL. Together with the pelvis these give the 15 directly matched joints used for SKEL fitting (Fig. 3 of the paper). ### Torques `tau_gt_det` is present in every trial. The Monte Carlo fields are present only when the trial lives in `trials_skelfit_with_uncertainty/`. | Field | dtype | shape | Always present | Notes | |---------------------|---------|-------------|:--------------:|-------| | `tau_gt_det` | float32 | `[T,37]` | yes | Inverse-dynamics torque inherited from AddB, deterministic | | `mu_tau` | float32 | `[T,37]` | MC only | Monte Carlo mean torque | | `sigma_tau` | float32 | `[T,37]` | MC only | Monte Carlo standard deviation, the released uncertainty | | `percentiles` | float32 | `[7,T,37]` | MC only | Per-DOF Monte Carlo percentiles | | `percentile_values` | float32 | `[7]` | MC only | Percentile levels: 1, 5, 25, 50, 75, 95, 99 | Torques are in N·m. To match the mass-normalized reporting used in the paper (Tables 2, 4, 5), divide by `mass_kg`. ### Forces, measured ground reaction | Field | dtype | shape | Notes | |------------------|---------|---------------|-------| | `grf` | float32 | `[T,n_c,3]` | 3D ground reaction force per foot, world frame, Newtons | | `cop` | float32 | `[T,n_c,3]` | 3D center of pressure per foot, world frame, meters | | `contact_torque` | float32 | `[T,n_c,3]` | Contact moment per foot | | `n_contacts` | int32 | scalar | Typically 2 | | `grf_valid_mask` | bool | `[T]` | False on frames where AddB marks the GRF as missing | ### Optical markers Raw mocap marker trajectories from the source recording. `M` is per-trial: marker sets differ across source studies (e.g. Carter2023 uses 56 markers with names like `RFT1`, `LCAL`; Tiziana2019 uses 29 markers with names like `LxAsis`). Names are kept as-is rather than remapped to a common skeleton. NaN indicates marker dropout in the original recording. World frame, meters, resampled to the trial's 100 Hz timeline by nearest-neighbor in the index domain. | Field | dtype | shape | Notes | |----------------|---------|-------------|-------| | `markers` | float32 | `[T, M, 3]` | 3D marker positions, world frame, NaN on dropout | | `marker_names` | str | `[M]` | Per-trial marker names | | `n_markers` | int32 | scalar | `M` for this trial | ### Trial metadata | Field | dtype | Notes | |-------------------|-------------|-------| | `num_frames` | int32 | `T` | | `fps` | float32 | 100 Hz throughout | | `dt` | float32 | `1 / fps` | | `mass_kg` | float32 | Subject mass at the time of recording | | `height_m` | float32 | Subject height at the time of recording | | `dof_names` | str[37] | Names of the 37 Rajagopal DOFs | | `direct_dof_mask` | bool[37] | True on the 20 direct DOFs | ### Subject metadata, snapshotted into each trial | Field | dtype | Notes | |------------------|---------|-------| | `activity_class` | str | running, walking, jumping, sit_to_stand, standing, stairs, others, or unknown | | `sex` | str | male, female, or unreported | | `age` | float32 | Years, NaN if unreported | | `bmi` | float32 | kg/m², NaN if unreported | ### Provenance | Field | dtype | Always present | Notes | |----------------|---------|:--------------:|-------| | `mc_source` | str | yes | Monte Carlo source pass, one of `v3`, `b2`, `b3`, or `skelfit_only` | | `b3d_path` | str | yes | Path to the source AddBiomechanics b3d file | | `sigma_q_deg` | float32 | MC only | RMS of the input-side angle noise used for Monte Carlo. 1.6° throughout, matching the AddB IK angle RMSE on synthetic walking data | | `n_mc_samples` | int32 | MC only | Number of Monte Carlo samples per frame. 1000 throughout | ## manifest.csv One row per trial. The columns mirror the most useful per-trial scalars on disk and add a few precomputed conveniences for filtering and splitting. | Column | Notes | |-------------------|-------| | `trial_uid` | `__trial`, unique key, safe to hash on | | `subject_id_raw` | identity | | `subject_pure` | identity within the source dataset | | `subject_base` | `subject_pure` with the `_splitN` suffix collapsed, used for the 121/19 subject count | | `study` | source dataset | | `split` | `train` or `test` | | `trial_idx` | int | | `has_uncertainty` | 1 if the trial lives in `trials_skelfit_with_uncertainty/`, else 0 | | `mc_source` | `v3`, `b2`, `b3`, or `skelfit_only` | | `activity_class`, `sex`, `age`, `mass_kg`, `height_m`, `bmi` | subject metadata | | `num_frames`, `fps`, `dt`, `duration_s` | trial length | | `n_grf_valid`, `grf_ratio` | per-trial GRF coverage | | `mpjpe_mm` | SKEL fitting error, mean per-joint | | `npz_path` | relative path. Load with `np.load(ROOT / row["npz_path"])` | `subjects.csv` aggregates by `subject_id_raw` and adds `n_trials`, `n_with_uncertainty`, `total_hours`, and an `activities` summary. ## Quickstart ```python import csv from pathlib import Path import numpy as np ROOT = Path("/path/to/ForceBody") manifest = list(csv.DictReader(open(ROOT / "manifest.csv"))) print(len(manifest), "trials") print(sum(1 for r in manifest if r["has_uncertainty"] == "1"), "with uncertainty") r = manifest[0] d = np.load(ROOT / r["npz_path"], allow_pickle=True) print(d.files) print("poses:", d["poses"].shape, "tau:", d["tau_gt_det"].shape, "grf:", d["grf"].shape) if int(r["has_uncertainty"]): print("median sigma_tau:", float(np.nanmedian(d["sigma_tau"]))) ``` ### PyTorch dataset A minimal lazy-loading dataset. Set `direct=True` to return only the 20 direct-DOF channels used in Tables 4 and 5 of the paper. ```python import csv from pathlib import Path import numpy as np import torch from torch.utils.data import Dataset class ForceBody(Dataset): def __init__(self, root, split="train", uncertainty_only=False, direct=False): self.root = Path(root) rows = list(csv.DictReader(open(self.root / "manifest.csv"))) self.trials = [ r for r in rows if r["split"] == split and (not uncertainty_only or r["has_uncertainty"] == "1") ] self.direct = direct def __len__(self): return len(self.trials) def __getitem__(self, i): r = self.trials[i] d = np.load(self.root / r["npz_path"], allow_pickle=True) sel = d["direct_dof_mask"] if self.direct else slice(None) item = { "poses": torch.from_numpy(d["poses"]), "betas": torch.from_numpy(d["betas"]), "trans": torch.from_numpy(d["trans"]), "q_gt": torch.from_numpy(d["q_gt"][:, sel].copy()), "qd_gt": torch.from_numpy(d["qd_gt"][:, sel].copy()), "qdd_gt": torch.from_numpy(d["qdd_gt"][:, sel].copy()), "tau": torch.from_numpy(d["tau_gt_det"][:, sel].copy()), "grf": torch.from_numpy(d["grf"]), "cop": torch.from_numpy(d["cop"]), "valid": torch.from_numpy(d["grf_valid_mask"]), "mass_kg": float(d["mass_kg"]), } if int(r["has_uncertainty"]): item["sigma_tau"] = torch.from_numpy(d["sigma_tau"][:, sel].copy()) item["mu_tau"] = torch.from_numpy(d["mu_tau"][:, sel].copy()) return item ``` Trials have variable length. Crop or pad them in your collator. ### Common usage patterns Joint torque regression. Predict `tau_gt_det` from `(q_gt, qd_gt, qdd_gt, grf, cop)`. Train on the 9,649-trial train split. Uncertainty-weighted training. Restrict to the 8,086 train trials with `has_uncertainty == 1` and weight the per-frame loss by `1 / sigma_tau²`. GRF and GRM regression from kinematics. Predict `(grf, cop, contact_torque)` from `(q_gt, qd_gt, trans)`. `grf_valid_mask` selects supervisable frames. Marker-based pose fitting. Use `markers` and `marker_names` to retrain a marker-to-pose model and compare against the released SKEL fit. Subject-level evaluation. Group test trials by `subject_id_raw` (or by the `(study, subject_base)` pair) and report subject-mean metrics so that a single long trial does not dominate. ## Joints used for evaluation The paper benchmarks torque prediction on the 15 directly matched joints between Rajagopal and SKEL (Fig. 3). These break down as: - 14 anatomical joints with a direct SKEL correspondence: bilateral hip, knee, ankle, MTP, elbow, radioulnar, and wrist. These contribute the 20 direct DOFs flagged by `direct_dof_mask`. - 1 floating-base pelvis (DOFs 0..5). Reported separately because its inverse-dynamics output re-expresses ground reaction rather than a muscle-driven joint torque. Tables 4 and 5 of the paper report Upper mean (elbow, radioulnar, wrist) and Lower mean (hip, knee, ankle, MTP), each averaged across left and right. ## Anonymous release This release is shared anonymously to support double-blind review of the accompanying submission. Author names, affiliations, and contact information are omitted. ## Attribution ForceBody is a derivative of the AddBiomechanics dataset, with motion re-expressed on the SKEL body model and accompanied by Monte Carlo torque uncertainty. Users should attribute the upstream AddBiomechanics dataset and the original source studies listed in the `study` column of `subjects.csv` (e.g. Carter2023, Moore2015, Hammer2013, Falisse2017, Fregly2012, Han2023, Li2021, Tiziana2019, Uhlrich2023, vanderZee2022). ## License The motion and force annotations in ForceBody inherit the CC BY 4.0 license of the upstream AddBiomechanics dataset. Redistribution and modification are permitted with attribution. The SKEL parameters released here (`poses`, `betas`, `trans`) are derived numerical fits and depend on the SKEL body model. The SKEL model itself is distributed under a separate non-commercial research license by its authors and is not redistributed here. Users who need to instantiate meshes from the released SKEL parameters must obtain the SKEL model directly from its authors and accept their license terms. Commercial use of ForceBody additionally requires permission from the SKEL licensors for any pipeline that loads the SKEL model.