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ForceBody: dataset details

Release 1.3.0 (2026-10-29). The documentation was updated on 2026-11-01 and on 2026-11-02 (CHANGELOG.md). No trial, model or table file changed, except the description column of SCHEMA_v1.3.csv (2026-11-02).

ForceBody pairs the SKEL body model with measured ground reaction forces (GRF), inverse-dynamics joint torques and Monte Carlo torque uncertainty for 10,386 motion trials (9,689,808 frames, 26.89 h, duration = num_frames / fps) of 140 persons (75 male, 55 female, 10 sex not reported) from 10 studies (AddBiomechanics). Every trial is one NumPy .npz file. You do not need nimblephysics, OpenSim or AddBiomechanics tooling to read it.

Since version 1.2, ForceBody ships two SKEL pose sets per trial (one fitted to the kinematics-only AddBiomechanics pass, one to the force-aware dynamics pass), cleaned GRF, recomputed torques and uncertainty for every trial, one body shape per person, corrected metadata, frame-level quality masks, and fixed splits. Read "Choosing a pose source" and "Leakage policy" before training anything that predicts or scores forces.

study persons trials
Carter2023 50 7,006
Falisse2017 11 70
Fregly2012 4 78
Hamner2013 10 40
Han2023 7 240
Li2021 1 65
Moore2015 12 1,019
Tiziana2019 25 619
Uhlrich2023 10 160
vanderZee2022 10 1,089
total 140 10,386

Study ids follow the first author and year of the source study (study_citation gives the citation key, Tiziana2019 = Lencioni et al. 2019). Release 1.3.0 renamed Hammer2013 to Hamner2013 (Hamner and Delp 2013). The 40 trial keys, folders and person ids changed (uid_map_v12_to_v13.csv, manifest column trial_uid_v12). Fields that point into the AddBiomechanics source (b3d_path, subject_id_raw, trial_uid_v1, the models/ file names) keep the AddBiomechanics folder spelling Hammer2013.

Layout

This Hugging Face repository holds the data only. The loader (forcebody_io.py), the renderer (visualize.py, VISUALIZATION.md), the benchmark card, cache builder and scorer (benchmark/) and the baseline code are in the GitHub repository https://github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026. The v1.1 provenance files and the Monte Carlo torque percentiles are not part of this upload.

Downloaded as in the Quick start of the dataset card (--local-dir ForceBody), the folder holds:

ForceBody/
  trials/<study>/S<NN>/<trial_uid>.npz        core arrays, 547 fields (SCHEMA_v1.3.csv)
  manifest.csv, manifest_train.csv, manifest_test.csv   one row per trial (every scalar field except the four JSON records
                                              head_fit_json*, skel_fit_metrics_json*, + metadata)
  uid_map_v12_to_v13.csv                     the 40 trial keys and paths renamed in 1.3.0 (Hammer2013 -> Hamner2013)
  name_map.csv, name_map.json                trial_uid -> AddBiomechanics source file and trial (`split` = the AddBiomechanics source split, not split_addb)
  persons.csv                                one row per person: betas, sex, height, masses, SHAPY measures, splits
  subjects.csv                               per-person metadata table (v1.1 layout, n_with_uncertainty is the v1.1
                                              count, see below)
  gait_events.csv                            GRF heel strikes and toe-offs (walking / running)
  models/                                    AddB pass-0 and pass-2 scaled OpenSim models per b3d file (.osim.gz),
                                              models_index.csv (model file -> source b3d file and processing pass),
                                              body_segments.csv (segment masses, local COM)
  marker_vertex_map.json                     marker name -> SKEL skin vertex used by the fit
  SCHEMA_v1.3.csv                            field, dtype, shape, unit, frame, version_added, description
  README.md                                  dataset card
  DETAILS.md                                 this file
  CHANGELOG.md                               version history
  MD5SUMS.txt                                md5 of the data files (with SCHEMA_v1.3.csv) and of README.md,
                                              DETAILS.md, CHANGELOG.md (not croissant.json, LICENSE.md, assets/)
  croissant.json                             Croissant metadata with Responsible AI fields
  LICENSE.md                                 license and attribution terms
  assets/                                    images of the dataset card

subjects.csv keeps the v1.1 column layout. Its n_with_uncertainty column is the v1.1 count (8,652 trials in total). For release 1.3.0 use has_uncertainty in manifest.csv (10,382 trials). Ten subjects.csv columns have no row in SCHEMA_v1.3.csv. sex_v12 equals sex. n_trials is the number of trials of the person, n_b3d_files the number of AddBiomechanics source files and n_mass_values the number of distinct mass_kg values. activities and activity_names list the activity_name values, and activity_classes lists the activity_recovered values ('|'-joined). usable_frac and eligible_eval do not match the current trial flags. Do not use them to select persons. Use the trial flags and split columns of manifest.csv.

The .osim.gz files in models/ store the internal gzip name <name>.osim.gz.writing_tmp. Decompress them with gunzip or Python gzip. Tools that use the stored name (7-Zip, gunzip -N) write <name>.osim.gz.writing_tmp. Rename such a file to <name>.osim.

Some SCHEMA_v1.3.csv descriptions carry short build tags in parentheses (for example S19 F3 or D20). They are internal issue labels of the build and need no lookup.

Choosing a pose source

Each trial has two SKEL fits. Both have every field and every QC mask. Pick one with pose_source.

pose_source fields fitted to default use
pass0 (alias kin) poses, trans, joints, q_kin, qd_kin, qdd_kin, addb_joints_kin AddB kinematics pass (pass 0) + the 30 Hz 2nd-order Butterworth filter that AddB applies for pass 1 (see below). Never saw the force plates force and torque estimation, physics critics p(tau | motion), any metric that scores forces from motion
pass2 (alias dyn) poses_dyn, trans_dyn, joints_dyn, q_gt, qd_gt, qdd_gt, addb_joints_dyn AddB dynamics pass (pass 2). Consistent with the GRF and torque labels text-to-motion (motion only, or motion + GRF + torque), T2M evaluators 
# run in ForceBody-NeurIPS-ED-2026/code (Quick start step 3 of the dataset card)
import forcebody_io as fio
d = fio.load_trial("../../ForceBody/trials/Moore2015/S10/Moore2015_S10_walk_014.npz", pose_source="pass0")
# common keys: poses, trans, joints, q, qd, qdd, addb_joints, pose_usable, pose_source
# plus every GRF / torque / mask / metadata field of the file

pose_source is required (ValueError otherwise). pose_usable is always True for pass0 and equals poses_dyn_usable for pass2 (False in 15 trials: force/motion-capture desync). What pass 2 changes, from the AddB documentation and our audit. AddB moves the pelvis so the centre-of-mass path matches the measured GRF, then refits poses and body scales. COM-GRF Newton consistency improves (r 0.9947 -> 0.9998), but stance-foot sliding rises (0.064 -> 0.079 m/s) and the p90 marker error rises (26.8 -> 36.0 mm). Joint centres differ by a median 9.6 mm, mostly a constant bone-rescaling offset. Pass 2 also changes the motion on frames without valid GRF (grf_valid_mask False on 1,348,203 of the 9,689,808 release frames, 13.9%), except in trials with qc_pass2_equals_pass1.

q_kin is AddB pass 0 filtered with the same 2nd-order 30 Hz zero-lag Butterworth filter that AddB applies for pass 1. q_kin reproduces the stored AddB pass 1 in 8,218 trials (q_kin_pass1_match). q_kin_pass1_mismatch_reason gives the reason for the other 2,168 trials (for example the arm re-solve of 875 trials, see the next section).

Re-timed trials (27, skel_fit_target_dyn == "kin_retimed"). The force stream was shifted in time to match the motion capture (grf_retimed_ms). Their pass-2 fit had been bent to the misaligned force, so poses_dyn, trans_dyn, joints_dyn and addb_joints_dyn equal the pass-0 fields, q_gt, qd_gt, qdd_gt and tau_gt_det are not usable (q_gt_usable, tau_gt_det_usable False), and tau_selfconsistent, mu_tau and sigma_tau are computed on q_kin with the pass-2 skeleton (tau_sc_kinematics, mc_kinematics). load_trial(..., "pass2") returns q_kin as q for these trials (q_source).

SKEL fit versions and fix rounds

Both pose sets come from one refit code base that went through several fix rounds before this release. skel_fit_code_version / skel_fit_code_version_dyn and skel_fit_source_run / skel_fit_source_run_dyn record per trial which code produced each fit.

fit code version trials
poses v1.2-fixround-2026-10-03c 10,291
poses v1.2-fixround-2026-10-04d 95
poses_dyn v1.2-fixround-2026-10-03c 8,940
poses_dyn v1.2-fixround-2026-10-07e 9
poses_dyn v1.2-fixround-2026-10-08i 815
poses_dyn v1.2-fixround-2026-10-09l 622

What the rounds changed (all trials unless a count is given):

  • scapula: the right scapula elevation bound of SKEL kin_skel.py has the wrong sign. It is replaced by the mirror of the left bound ([0.1, 0.4] rad). The bound sizes (0.1, 0.4 rad) have no published source (SKEL issue #19) and elevation sits on them in many fits (see Known issues).
  • marker handling: aliases for trunk / shoulder markers (Han2023, Uhlrich2023) and acromion vertices moved to the acromion tip (marker_vertex_map.json).
  • kinematics target (q_kin): where the AddB pass-0 left arm parks on its joint limit or hops (> 30 deg per frame) and where the elbow parks at 0, the arm is re-solved by inverse kinematics to the markers before the 30 Hz filter. A window is kept only if the marker error does not rise (hop windows: by at most 1 mm). 875 trials (q_kin_source == "addb_pass0_armfix_lp30"). Inside full-body hop windows the leg and trunk target also changes.
  • fit: a continuity guard repairs fit-only branch flips of arm, leg and root DOF groups. Stage-2 joint weights of femur and tibia raised from 5 / 3 to 10 (our choice) so the knee follows the target. An extra lower-body stage (2f) is kept when it passes the whole-body error gate. Trade-off: the pelvis joint error rises (median about 2 mm) because the SKEL femur is shorter than the AddB femur.
  • head: the head is pinned during the main fit and fitted afterwards to the head markers (orientation only, neck limits of bsm.osim). 1,948 trials have head_postfit. The others (qc_head_untracked) keep head DOFs 23-25 at 0, so the head follows the thorax.

Dynamics-pass target repair (poses_dyn is not pure pass 2 everywhere)

poses_dyn is fitted to the AddB dynamics pass (q_gt) except where that target was repaired. skel_fit_target_dyn names the target, dyn_target_source [T,37] marks per frame and AddB DOF which values replaced q_gt (0 = pure pass 2), dyn_target_modified_frac is the fraction of modified frames, and addb_joints_dyn are the joint centres of the target actually fitted. q_gt itself is unchanged, so in the flagged frames poses_dyn and q_gt disagree.

skel_fit_target_dyn trials target
addb_pass2 8,939 pure pass 2 (q_gt)
addb_pass2_armsplice 9 pass 2 with the kinematics-target (re-solved) arm spliced in inside the accepted arm windows, cosine ramps
addb_pass2_repaired_v3a 791 pass 2 with the kinematics target (whole body, aligned at the window edges) in frames without valid GRF, the kinematics root on GRF-valid frames where the pass-2 pelvis deviates by more than 50 mm, kinematics values on pass-0 IK outlier frames, left-arm splice. 0.25 s cosine ramps
addb_pass2_repaired_v4s 620 v3a plus a position-based left-arm IK on the pass-2 skeleton and the AddB acceleration-minimizing filter on all 37 DOFs (removes 6-30 Hz wobble of pass 2)
kin_retimed 27 poses_dyn = poses (re-timed trials, below)

Bits of dyn_target_source: 1 kinematics value (GRF-invalid window or pass-2 root deviation), 2 left-arm splice, 4 pass-0 IK outlier repair, 8 left-arm position IK, 16 acceleration-minimizing filter, 32 other difference from q_gt (arm splice). 1,392 trials have a modified frame (median modified fraction 0.83). 1,048,169 of 9,689,808 frames. dyn_arm_spliced [T,2] marks frames whose dyn-target arm comes from the kinematics or IK arm (622 trials, qc_dyn_arm_spliced). The repair trades Newton consistency for a plausible root and arms where pass 2 had no force support: on the repaired trials the root path follows the kinematics target in GRF-invalid windows, so GRF-free frames of poses_dyn are no longer force-aware there. For a force-aware motion source with the arm and root fixes, use poses_dyn with its masks. For pure AddB pass-2 kinematics use q_gt.

Leakage policy

AddBiomechanics warns, "If you use the output of the dynamics pass, you will be training your model on data that has already been fine tuned to match the force plate data, and your model will overfit to the clues that the optimizer inserts into the motion", and for GRF prediction to "use as input features only the kinematics pass" (nimblephysics documentation, working with AddBiomechanics data). GaitDynamics uses the kinematics pass "to prevent the leakage of force data ... into the kinematics". InferBiomechanics (AddB benchmark code) takes inputs from the kinematics pass and labels from the last pass.

The effect is large here. Recovering the total GRF from the centre-of-mass acceleration alone, with no learning, has a median error of 2.56 N/kg on pass-1 kinematics and 0.2 N/kg on pass-2 kinematics.

The rule is an allowlist. A model that predicts or scores forces or torques may take as input only the pass-0 pose set (poses, trans, joints, q_kin, qd_kin, qdd_kin, addb_joints_kin, or load_trial(..., "pass0")), betas, skel_gender, mass_kg, height_m, sex and age (these fields are forcebody_io.ALLOWED_INPUT_FIELDS), and person-free text metadata. Every other field is excluded, in particular the dynamics-pass pose set (poses_dyn, trans_dyn, joints_dyn, addb_joints_dyn, q_gt, qd_gt, qdd_gt), the labels (tau_*, mu_tau, sigma_tau, percentiles, grf*, cop*, contact_*, grf_valid_mask) and the fields derived from the GRF: mass_kg_grf, mass_kg_grf_stand, mass_dyn_scale, mass_dyn_used_kg, mass_kg_dynamics*, cadence_spm_grf, stride_time_s_med, duty_factor_*, gait_events.csv, support_side, support_side_grf_share, caption_support_side, floor_height_m, floor_tilt_*, speed_belt_grf_mps, addb_belt_speed_mps, speed_mps (running), tilt_deg and the frame array chair_contact, and the masks and flags mc_frame_ok, kin_frame_ok, eval_*, t2m_*, s8b_*, the usability flags (for example q_gt_usable, usable_provisional), the qc_* fields and every *_dyn / dyn_* field (dyn_target_source is placed by the GRF-valid windows). forcebody_io.LEAKY_INPUT_FIELDS is only a safety net. It flags many of these fields, but it is not guaranteed to be complete. The allowlist above defines the valid inputs. The benchmark cache builder refuses every input that is not a benchmark input. Results computed with pass2 inputs must be labelled leaky and are not comparable with the official benchmark numbers. The body shape (betas) was fitted to pass-0 bone lengths, the recorded height and a person mass target. For Moore2015 that target is the GRF calibration-pose mass (a static, person-level weight).

Force benchmark

The force benchmark (joint torques T-tau and per-foot GRF T-GRF from motion) is defined in code/benchmark/BENCHMARK.md of the GitHub repository https://github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026 with the machine-readable benchmark/spec_v12.json, a cache builder (benchmark/build_bench_cache_v12.py) and the reference scorer (benchmark/score_v12.py). The scripts import forcebody_io.py from the code/ folder of the GitHub repository. The baselines retrained on 1.2.1 (three seeds, the numbers of the camera-ready paper, see Paper numbers) are in the Benchmark section of the GitHub README. The benchmark data of 1.3.0 are identical to 1.2.1 except the 40 renamed Hamner2013 keys.

Conventions

item convention
frames addb_lab: raw AddB lab frame, y up (grf, cop, contact_torque, markers, q_*, tau_*). skel_world: flip_xz(addb_lab) (x -> -x, z -> -z, y unchanged): poses, trans, joints, addb_joints_*, *_aligned, cop_aligned_foot. Every *_aligned field is exactly flip_xz of its raw field (max difference 0)
gravity -y in every study
contacts index 0 = right foot (calcn_r), 1 = left foot (calcn_l), 2 = pelvis (Falisse2017 chair)
trans SKEL root translation (trans_frame == "skel_world"). The pelvis joint is trans + j0(betas), not trans. Here j0 is about (0.0095, -0.236, 0.103) m for a male rest pose, so rotating trans alone misplaces the body by 0.12-0.16 m
joints joints[T,24,3] in joint_names order (SKEL / OpenSim: 0 pelvis, 1 femur_r, ...)
DOF names skel_pose_names (46 SKEL pose DOFs. skel_dof_valid False = pinned to 0 in the main fit, namely head DOFs 23-25 and wrist DOFs 34, 35, 44, 45. The head post-fit then sets the head DOFs where head_postfit is True (1,948 trials). The wrist DOFs stay 0), dof_names = dof_names_opensim (37 AddB DOFs, <OpenSim joint>_<coordinate index> names, see below)
time t_i = i / fps_true_grid. fps is the nominal rate. The true grid differs slightly (61 Hz: 64, 99 Hz: 470, 100 Hz: 9,640, 101 Hz: 76, 201 Hz: 111, 250 Hz: 25 trials by rounded rate)
heading use heading_canonical_deg (SKEL frame) only
mirroring swapping left/right SKEL DOFs with a sign table is not an exact mirror (MPJPE 32 mm, feet 45-77 mm). Mirror the mesh or the joint positions instead

AddB DOF order. dof_names and dof_names_opensim hold the same 37 names. They label the columns of q_*, tau_*, mu_tau and sigma_tau. The names are <OpenSim joint>_<coordinate index> (for example hip_r_0, walker_knee_r_0), not OpenSim coordinate names. Index k is the k-th coordinate of that joint in the AddB OpenSim model (models/, the Rajagopal coordinate set):

columns names OpenSim coordinates
0-5 ground_pelvis_0-5 pelvis_tilt, pelvis_list, pelvis_rotation, pelvis_tx, pelvis_ty, pelvis_tz
6-12 hip_r_0-2, walker_knee_r_0, ankle_r_0, subtalar_r_0, mtp_r_0 hip_flexion_r, hip_adduction_r, hip_rotation_r, knee_angle_r, ankle_angle_r, subtalar_angle_r, mtp_angle_r
13-19 same for the left leg (_l) same coordinates, _l
20-22 back_0-2 lumbar_extension, lumbar_bending, lumbar_rotation
23-29 acromial_r_0-2, elbow_r_0, radioulnar_r_0, radius_hand_r_0-1 arm_flex_r, arm_add_r, arm_rot_r, elbow_flex_r, pro_sup_r, wrist_flex_r, wrist_dev_r
30-36 same for the left arm (_l) same coordinates, _l

In tau_selfconsistent, tau_gt_det, mu_tau and sigma_tau, columns 0-2 are root residual moments (N m) and columns 3-5 are root residual forces (N). Columns 6-36 are joint torques (N m).

Body mass fields

field meaning use
mass_kg recorded mass (unchanged from v1.1) model and critic inputs
mass_kg_dynamics AddB pass-2 skeleton mass times mass_dyn_scale (already applied) the only mass for normalizing GRF, torque and sigma labels (N/kg, N m/kg, BW)
mass_kg_dynamics_v11, mass_dyn_scale v1.1 dynamics mass and the approved per-file rescale provenance. Never multiply mass_kg_dynamics by mass_dyn_scale again. mass_kg_dynamics_v11 stays in the core file (the only _v11 value there besides the QC count qc_grf_endpoint_valid_v11)
mass_kg_grf per-trial GRF-implied mass estimate diagnostic
caption_mass_kg, shape_mass_target_kg, mass_source person mass for captions and for the shape fit (recorded. Moore2015 = GRF calibration pose) text, shape

mass_dyn_scale differs from 1 in 491 trials (14 b3d files), where the AddB link masses were inconsistent with the measured GRF.

Quality masks

Nothing is deleted. Problems are flagged. Frame masks ([T] or [T,2]): grf_frame_ok (GRF validity: GRF losses and statistics), grf_frame_strict_ok (whole frame without zero-force or misassigned feet), grf_foot_ok (per foot, columns right, left, 1.2.1), tau_frame_ok (torque losses, use eval_tau_frame_ok for evaluation), mc_frame_ok (use of sigma_tau, equal to tau_frame_ok and kin_frame_ok and has_uncertainty), kin_frame_ok (pelvis not flipped), cop_frame_ok (per foot), grf_contact_state (per foot: 1 = in contact on a grf_frame_ok frame, 0 = not in contact on a grf_frame_ok frame, -1 = unknown: no measured force, or a measured frame outside grf_frame_ok), eval_tau_frame_ok (= tau_frame_ok and kin_frame_ok), eval_grf_frame_ok (= grf_frame_ok and kin_frame_ok).

GRF validity and CoP trust (1.2.1). grf_frame_ok is GRF validity only: grf_valid_mask, not an endpoint or a blanked tail, and a consistent force label on every used contact (grf_contact_ok). A loaded contact with contact_status 3 (force valid, CoP projected on the foot and not trusted) counts as valid. CoP trust gates only cop_frame_ok (trusted CoP status 1, 2 or 9 on that foot) and tau_frame_ok (no used contact with status 3, because the torque depends on the CoP). In 1.2.0 the status-3 frames were not in grf_frame_ok, so on fixed-point-CoP trials (qc_cop_fixed_point, Carter2023) the GRF masks kept only unloaded frames (CHANGELOG, erratum). In 1.2.1 170,158 status-3 frames of 1,158 trials are in grf_frame_ok. The QC scalars are still computed on grf_frame_ok without the status-3 frames, as in 1.2.0, so their values did not change, except the mask summaries (grf_frame_ok_frac, grf_frame_strict_ok_frac, qc_grf_contact_bad_frames) and the evaluation flags.

Per-foot GRF mask (1.2.1). grf_frame_strict_ok drops the whole frame when either foot is zero-force grounded, force-in-air, unmeasured in stance or has its CoP off the foot. grf_foot_ok drops only the zero-force-grounded foot itself. The other three flags point to a likely plate misassignment (the force of the other foot may be on the wrong plate), so they still drop both feet of the frame (grf_foot_ok.all(1) equals grf_frame_strict_ok). It keeps 854,888 loaded foot-frames (vertical GRF > 20 N) that the strict mask removes.

Masked values are not NaN. Outside a mask the arrays keep their raw values. cop and contact_torque are 0 where the force is 0, the GRF of the AddB endpoint frames is 0, and grf / grf_aligned can be non-zero on frames outside grf_valid_mask (force readings that AddB marked as missing). A 0 is not a measurement, so apply the mask.

Trial flags:

flag trials True
force_label_usable 10,372
torque_label_usable 10,067
eval_motion_trial_ok 9,697
eval_grf_trial_ok 9,769
eval_torque_trial_ok 8,256
qc_pass2_equals_pass1 1,249
qc_skel_grf_newton_mismatch 244
qc_grf_single_channel 37
qc_mass_dyn_vs_grf_flag 20
qc_overlap_pair_drop 8
qc_overlap_motion_drop 200

Evaluation flags. eval_grf_trial_ok and eval_torque_trial_ok require usable force labels and exclude single-channel force plates (qc_grf_single_channel: one plate carries both feet, so per-foot GRF and the foot assignment of the torques are wrong, also excluded from torque_label_usable), byte-identical duplicates (qc_duplicate_drop), the Han2023 trimmed copies of a re-exported capture (qc_overlap_pair_drop = eval_exclude_duplicate and not qc_duplicate_drop), static calibration, transition and pause segments, and trials whose dynamics mass disagrees with the GRF-implied mass by more than 5% (qc_mass_dyn_vs_grf_flag). eval_torque_trial_ok also requires a root residual force of at most 0.1 BW. Since 1.2.1 both also require at least one loaded evaluation frame (qc_grf_loaded_eval_frames > 0: a foot vertical GRF above 20 N on an eval_grf_frame_ok frame, for torques also qc_tau_loaded_eval_frames > 0). 83 trials with usable force labels have none (most have grf_valid_mask False on every frame). eval_motion_trial_ok applies the same duplicate / segment exclusions and in addition drops one member of each overlapping Tiziana2019 capture pair (qc_overlap_motion_drop: the member with fewer GRF-valid frames). The Tiziana2019 pairs share no GRF-valid frame, so both members stay in force evaluation. Always combine a trial flag with its frame mask.

force_label_usable is False where force and motion capture are out of sync and could not be re-timed (14 trials). Every force, torque, MC, contact-state and CoP frame mask is then False. grf_contact_ok, the per-contact label-consistency input of grf_frame_ok, is not cleared. Never use it without grf_frame_ok. qc_skel_grf_newton_mismatch marks trials whose SKEL root path does not follow the GRF without any time-lag evidence. Their force labels are kept. Pathological persons are never excluded. eval_stratum reports them separately. Every QC field is described in SCHEMA_v1.3.csv.

Falisse2017 chair channel (contact index 2, contact_bodies = pelvis). chair_contact [T] marks the frames where the subject sits on the instrumented chair (the grf_contact_label hysteresis on the chair vertical force, not restricted to grf_frame_ok, most seated frames are outside grf_valid_mask), qc_chair_channel_active marks trials with any such frame, and qc_extra_channel_zero marks trials whose chair channel carries no force at all (walk, stairs). On those trials grf_contact_ok[:, 2] is False on every frame and grf_frame_ok ignores the channel. The trial-level manifest column chair_contact (protocol metadata) is a different field from the per-frame npz array of the same name.

Pose masks of the fix rounds

These masks describe the SKEL pose sets (True = usable). Each exists for both pose sets (_kin = poses, _dyn = poses_dyn). AND them into the loss mask of the listed DOFs. Masks are dilated by 0.1 s (our choice).

field shape False on trials with a False frame frames
arm_event_ok_kin [T,2] (left, right) arm discontinuities (arm-jump diagnosis of 2026-10-07 and a re-scan of the final fits). Arm DOFs 272 231,278
arm_event_ok_dyn [T,2] same for poses_dyn 316 217,726
trunk_event_ok_kin [T] AddB pass-0 trunk shake (class A3). Lumbar / thorax DOFs 17-22 143 200,423
trunk_event_ok_dyn [T] same for poses_dyn 155 200,116
dyn_root_jump_ok [T] pass-2-only whole-body jumps (class A6): drop the whole poses_dyn frame 108 2,858
fit_event_ok_kin [T,7] temporal events of the final fit per joint group (fit_event_groups: pelvis, leg_r, leg_l, spine, head, arm_r, arm_l): fit-only spikes, spikes inherited from the target, one-frame DOF steps, and Euler branch flips (one-frame step > pi) 1,820 251,010
fit_event_ok_dyn [T,7] same for poses_dyn, measured against its own (repaired) target 1,808 254,087
kin_hop_edge_frame_ok_r [T] right-arm frames at a hop-window edge of the arm re-solve where two Euler branches are joined (qc_kin_hop_edge_branch_blend) 3 75

Also per trial: addb_limit_mask_kin / addb_limit_mask_dyn [T,11] (the fit target sits on an AddB joint limit, the dyn mask is the union of the pass-2 and pass-0 on-limit frames, addb_limit_mask_names), qc_arm_event_classes_kin / _dyn (diagnosis classes present), and the trial flags below. Where qc_upper_body_unobserved is True (no torso / arm marker at all, e.g. vanderZee2022 S09) the arm, trunk and head masks are False on every frame of both pose sets: the SKEL arms, trunk and head are unconstrained there.

trial flag trials True meaning
qc_upper_body_unobserved 133 no upper-body marker. Arms / trunk / head masked
qc_head_untracked 8,438 fewer than 2 head markers. Head DOFs 23-25 are 0
qc_kin_armfix_edge 20 arm re-solve window-edge artefact of the kinematics target (frames in arm_event_ok_kin)
qc_kin_hop_edge_branch_blend 3 hop-window edge joins two right-arm Euler branches
qc_upper6_rise10_kin 13 upper-body joint error of poses rose by > 10 mm in the fix rounds (our threshold). Arm-marker error is better in almost all. Flag, not exclusion
qc_upper6_rise10_dyn 18 same for poses_dyn
qc_heading_rms3_kin 3 pelvis heading lost > 0.1 correlation and detrended heading RMS > 3 deg (our thresholds)
qc_heading_rms3_dyn 2 same for poses_dyn
qc_dyn_arm_spliced 622 poses_dyn arms follow the kinematics / IK arm in dyn_arm_spliced frames
qc_tau_sc_q_kin_pre_armfix 0 re-timed trial whose torque labels were computed on q_kin before the arm re-solve (mc_kinematics == "q_kin_pre_armfix")
qc_pose_wrap_n_kin > 0 1 Euler branch flip in poses (masked, not unwrapped)
qc_pose_wrap_n_dyn > 0 1 same in poses_dyn
qc_head_at_limit_frac_* > 0.05 11 head post-fit at a neck limit on > 5% of frames. Do not use the head orientation there

Added in 1.2.1 (T2M audit, flags and masks only, no pose refit):

field trials meaning
qc_q_kin_wrap 68 q_kin (fit target of poses) beyond 2 pi on a shoulder DOF (an AddB Euler branch. qc_q_kin_abs_gt_2pi_dofs). The frames of that arm are False in arm_event_ok_kin, and in arm_event_ok_dyn on kin_retimed trials. Not unwrapped
knee_clamp_frame_kin / _dyn [T,2] (right, left), qc_knee_clamp_frac_* 4,870 (kin, any frame). 1,061 kin / 1,294 dyn above 10% of frames SKEL knee on its 0 bound while the AddB target knee is flexed 3 deg or more (our threshold). Flag only. forcebody_io.load_trial returns the mask of the chosen set as knee_clamp_frame
qc_belt_speed_change / qc_belt_speed_change_suspected 4 / 8 treadmill belt labelled constant whose speed changes in the trial (both pose sets / one pose set)
label_conflict_codes contains SEGMENT_MOTION_MISMATCH 28 segment label and motion disagree (Carter2023 static-trial stand segments with a leg lift, Han2023 pause / sit / short action segments). Labels and captions not changed

Uncertainty

mu_tau and sigma_tau ship in the core file for all 10,386 trials, the torque percentiles (with percentile_values) are kept in the internal v1.1 provenance archive and are not part of this upload. has_uncertainty is True for 10,382 (False: the trial is shorter than one period of the 6 Hz noise band, sigma is 0). has_uncertainty_v11 (manifest) marks the 8,652 trials that had MC in the version of the submitted paper. sigma_tau is the kinematic sensitivity of the torque to 1.6 deg band-limited joint-angle noise on q_gt (pass 2, q_kin on re-timed trials) with the measured GRF held fixed. It is not the uncertainty of poses, and not a label-reliability score. Normalize it by mass_kg_dynamics only, and use it only where mc_frame_ok and torque_label_usable hold. Per-trial ratios to the sigma of the submitted-paper version have p5-p95 0.85-1.20. The sigma statistics of the paper use the trials with has_uncertainty and grf_valid_frac >= 0.5 (8,732 trials).

Time alignment

item status
release grid one grid per trial for kinematics, forces and MC (fps_true_grid)
AddB endpoints frames 0 and T-1 carry no GRF. They are never in grf_frame_ok
duration duration_s = num_frames / fps (nominal rate, v1 convention). The time span of the grid is (num_frames - 1) / fps_true_grid, about one frame shorter
force/mocap re-timing 27 trials re-timed and validated against independent marker-based gait events. Applied shift median 277 ms (range -85 to 320 ms)
partial invalidation 1 trial: force invalid from force_invalid_from_frame
lag diagnostics qc_grf_kin_toeoff_lag_ms, qc_grf_kin_onset_lag_ms, qc_grf_newton_seg_lag_ms, qc_cop_foot_shift_ms (positive = kinematics later than force)

Floor and gait events

  • floor_height_m: median centre-of-pressure height (SKEL frame, y) over trusted firm-stance frames (contact_status 1, foot vertical force > 0.5 BW of mass_kg_dynamics, grf_valid_mask). NaN with fewer than 30 such frames. It comes from the force plates. On stairs and step activities it is the height of the loaded step surface, not of the floor.
  • No floor normal is shipped. A plane fitted to the SKEL sole height over the CoP positions tilts by 2-3 deg on level treadmills (heel-to-toe roll). The QC fields floor_tilt_x_deg / floor_tilt_z_deg (plane through the firm-stance CoPs) and floor_tilt_x_kin_deg / floor_tilt_z_kin_deg (SKEL sole height at the same samples) are diagnostics only. Protocol inclines are in incline_grade_pct / incline_label.
  • gait_events.csv: heel strikes (grf_contact_state 0 -> 1) and toe-offs (1 -> 0) of each foot on walking and running trials with usable force labels. frame on the release grid, time_s = frame / fps_true_grid. stride_time_s_med is the median same-foot heel-strike interval (0.4-2.5 s, strides with an unknown (-1) frame inside are not used), cadence_spm_grf = 120 / stride_time_s_med, duty_factor_r / duty_factor_l the median stance fraction of a stride.
  • Consistency gate: where cadence_spm_grf differs from the kinematic cadence_spm (metadata) by more than 10% (our threshold), qc_gait_event_inconsistent is True, the four GRF gait scalars are NaN and the trial has no rows in gait_events.csv (28 trials).
  • Coverage: 8,940 of the 9,700 walking or running trials with usable force labels have rows. The other 760 trials have none. 28 fail the consistency gate. 732 have no 0 -> 1 or 1 -> 0 transition of grf_contact_state, among them all 609 Tiziana2019 walking and running trials.
  • Carter2023 running. In most trials grf_valid_mask also covers the flight frames (median grf_valid_frac 1.00). In some trials AddB marks the flight frames as missing GRF, and there GRF labels, T-GRF scores and grf_contact_state (-1) cover stance frames only and strides that contain such frames give no GRF gait scalars. On fixed-point-CoP trials (qc_cop_fixed_point) every loaded frame has contact_status 3. Since 1.2.1 these frames are in grf_frame_ok and grf_contact_state, so these trials have GRF gait events, but they stay out of cop_frame_ok and tau_frame_ok. (1.2.0 stated the opposite: its GRF masks kept only the unloaded frames of these trials, CHANGELOG erratum.)

Metadata

Every metadata column is in manifest.csv and the per-trial ones also in each trial file. SCHEMA_v1.3.csv gives the definition of each column. Groups: identity and capture structure (person_id, subject_base, capture_id, segment_index, n_segments_in_capture, segment_role, qc_overlap_of, eval_exclude_duplicate), activity (activity_name, activity_recovered, activity, activity_sequence, activity_modifier, gait_style, source_task, setting, setting_motion, path, support_side), protocol (protocol_condition, condition_key, speed_*, incline_*, belt_*, cadence_*, step_*), person (sex, age, bmi, health_status, health_detail, footwear) and captions (caption_*). The measured values (speed_mps, cadence_spm, step_width_m) come from forward kinematics of the AddB model. The running treadmill speed uses GRF stance frames, and support_side_grf_share and speed_belt_grf_mps use the GRF (Leakage policy).

Body shape

betas are fitted once per person (identical in all trials of a person, persons.csv) to the pass-0 bone lengths, the recorded height and the person mass target, and skel_gender selects the SKEL model (skel_gender_source: height_rule 10, sex_label 130 persons). persons.csv reports SHAPY-style measurements of the shaped mesh (height = |y(HeadTop 411) - y(HeelLeft 3466)|, mass = volume x 985 kg/m^3, chest / waist / hip girths at vertices 6489 / 3501 / 1210). The girths are almost fully determined by height and mass (R^2 0.987-0.994 for a linear fit on the recorded height and the person mass target within each SKEL gender model, and 0.31 for the hip girth when both genders are pooled). 41 (female) / 52 (male)% of the betas variance is bone proportion that height and mass do not explain. Keep claims about body shape at the height / mass level.

Splits

The Hugging Face configuration loads manifest_train.csv (split train, which holds the split_addb_val train and val persons, select val with split_addb_val == "val") and manifest_test.csv (split test).

split_addb (force benchmark train / test, person level) with split_addb_val (validation persons drawn from the train persons), and split_t2m (text-to-motion train / val / test / patho, person level). No person is in two folds of one split.

All three are drawn with a stratified, seeded person-level search (2026-10-09): test and val persons cover the height x body-mass tertile cells of the healthy persons and the bottom / top 10% of height and mass, use different studies within a cell where possible, and cover as many activities as possible, while train keeps every activity, study and body-size cell that any other fold has. Test and val persons are drawn only from persons with at least 90% usable trials (split_addb test and val also need at least 90% trials with eval_grf_trial_ok). Pathological persons are split_t2m=patho and keep their earlier split_addb (separate eval_stratum). split_addb therefore no longer equals the split shipped with the AddBiomechanics source data. Splits of earlier versions are not compatible: caches or checkpoints trained on an earlier split may contain current test persons.

split_addb persons trials
test 19 914
train 121 9,472
split_addb_val persons trials
test 19 914
train 109 8,374
val 12 1,098
split_t2m persons
patho 5
test 35
train 83
val 17

The split_t2m walking test set has 4 persons with a body mass target of 85 kg or more. The running test set has 0 (running is not evaluable at high body mass).

Health status and sensitive information

health_status persons trials
TKA 4 78
healthy 135 10,243
stroke 1 65

The data include persons after total knee arthroplasty and after stroke (health_status, health_detail). This is health information. trial_name keeps the source names: 240 Han2023 names contain a capture date and the Fregly2012 names (trials: 78) contain subject initials. Ages range from 18 to 86 years (unknown in 200 trials). Do not try to re-identify persons.

Intended and out-of-scope uses

Intended: research on motion-to-force and force-aware motion models, text-to-motion with physical labels, biomechanics method development. Out of scope: clinical decisions or diagnosis, identifying or profiling persons, and human mesh recovery from images or video (there are no images or video in ForceBody).

Paper numbers

The benchmark numbers of the camera-ready paper (the ForceBody rows of Table 3, and Tables 4, 5, 14 and 15) are the mean and standard deviation over three seeds of the baselines retrained on release 1.2.1. The benchmark inputs, labels, masks and splits of 1.2.1 are identical in 1.3.0. Checkpoints were selected on the 12 validation persons (split_addb_val == "val", 1,098 trials), and the 19 test persons (914 trials, healthy and pathological strata) were scored once. The Benchmark section of the GitHub README gives Tables 3, 4 and 5. The AddB rows of Table 3 are copied from the reviewed build and serve as a reference, not as a matched comparison. They used dynamics-pass (pass 2) kinematics as input (see Leakage policy). The submitted version of the paper used the reviewed build 1.0.0 with the v1 labels. The torque labels moved between versions. Scoring the v1.1 tau_selfconsistent against the reviewed-build labels gives RMSE / MAE / PCC 0.287 / 0.087 / 0.721, the same order as the model errors, so numbers from different versions must not be mixed in one table.

Known issues

  • vanderZee2022. Force-plate cross-talk remains after the plate re-zero (swing-foot readings depend on the other plate load). The root residual force is higher than in other studies.
  • Carter2023 downhill. AddB did not dynamics-process these kinematics. The root residual stays high and the torque labels are flagged. grf_valid_mask_plate is offered for GRF supervision.
  • Carter2023 trials whose raw plate CoP is unusable keep a fixed-point CoP (qc_cop_fixed_point, qc_tau_cop_unreliable). Exclude them from CoP and torque evaluation. Their forces are valid. Since 1.2.1 the loaded frames are in grf_frame_ok, while cop_frame_ok and tau_frame_ok exclude them.
  • Han2023 yoga / dance: a few extreme-torque frames of kinematic origin (tau_frame_ok excludes them).
  • vanderZee2022 S09 has no upper-body markers (qc_upper_body_unobserved). AddB pass 0 and pass 2 arms differ by about 200 mm there and the SKEL arms, trunk and head are unconstrained (masked).
  • The scapula DOFs are pinned at their bounds in most fits (qc_skel_scapula_pinned). Scapula elevation sits on its +-0.1 rad bound (size unsourced, SKEL issue #19) in many kinematics fits. Mask scapula DOFs in losses.
  • SKEL left ankle axis (SKEL issue #45, kept upstream): ankle_angle_l / subtalar_angle_l are not the mirror of the right DOFs and the fit compensates with subtalar_l. Do not mirror motions by swapping L/R SKEL DOFs. Compare left and right ankles with the AddB q (q_kin, q_gt). Read the poses with official SKEL (a SKEL with the issue #45 fix moves the left toes by about 22 mm at p95).
  • Joint-limit bounds of the fit are left/right asymmetric for shoulder_y and pro_sup (unsourced kin_skel values, kept).
  • Pelvis joint error is higher than in the first v1.2 fit (median about 2 mm) after the knee weight change. The SKEL femur is shorter than the AddB femur (shape-step limitation).
  • Head post-fit. A few trials sit on a neck limit for many frames (qc_head_at_limit_frac_*). Trials without head markers keep the head rigid with the thorax (neck line 3-10 deg forward of the markers in head-marker studies).
  • Non-arm fit-only spikes (head, spine) exist in a minority of trials. They are masked per joint group in fit_event_ok_*, not repaired. Head post-fit steps > 0.5 rad per frame are masked in the head column.
  • Dynamics-pass target repair. On repaired trials the Newton consistency of poses_dyn with the GRF dropped by more than 0.02 (correlation) in a minority of trials and the lower-body error rose by more than 1 mm in some. Accepted. qc_skel_vs_qgt_err_mm compares with q_gt, not with the repaired target.
  • Some dyn-pass arm events listed by the 2026-10-07 diagnosis refer to the dyn fit before the 2026-10-08/09 target repair. They are kept in arm_event_ok_dyn (conservative), together with the events of the re-scan of the final fits.
  • Carter2023 running. In some trials AddB marks the flight frames as missing GRF (grf_valid_mask False), so force labels and the T-GRF benchmark cover stance frames only there (most trials keep the flight frames valid with zero force).
  • floor_height_m is the loaded step height on stairs / step activities. No floor normal is shipped.
  • visualize.py (GitHub repository, code/) needs the public SKEL package, the SKEL model files and an EGL-capable renderer (see code/VISUALIZATION.md). Reading the data needs only numpy.
  • Release verification left small deviations. In one trial the fit joint error recomputed from the stored float32 joints / addb_joints_kin differs from the stored qc_skel_fit_err_mm by 0.054 mm (tolerance 0.05 mm, so one of 10,386 trials fails the independent reproduction check). The median lag between GRF and kinematic toe-off events is -74.5 ms for Uhlrich2023 (all other studies -50 to 0 ms), just outside the -70 to +10 ms window used as a sanity range.
  • No acceleration filter was applied to the kinematics-pass target: poses keep a small frame-to-frame wobble on trials whose AddB pass-0 inverse kinematics is noisy. The dynamics-pass target is smoother (AddB dynamics pass, repaired targets use the AddB acceleration-minimizing filter).
  • Name clash. chair_contact in manifest.csv is the trial-level protocol flag (a chair is used in the trial), while chair_contact inside the trial file is the frame-level [T] seated mask of the Falisse2017 chair channel (all False without a chair channel). Use qc_chair_channel_active for the trial-level summary of the frame mask.
  • CoP placement at low load. Frames where a foot channel is loaded (vertical force above 5% BW) and its CoP lies more than 0.15 m from both feet are not all flagged. A scan of the release found 3,227 GRF-valid such frames in 664 trials with cop_frame_ok True and cop_off_foot False. Mask CoP losses by load if this matters.
  • Han2023 sit (12 trials) and sit-to-stand (2 trials): in the frames a posture rule marks as seated, the foot GRF stays near 1 BW (median 0.999 BW) and there is no seat force channel, so either the seat load is not measured or the label does not describe a loaded seated phase. The labels were not changed.
  • Treadmill belt residual. On treadmill trials the stance foot is not perfectly stationary after the belt motion is removed with a kinematic estimate of the horizontal belt speed (and the protocol grade for the vertical component): the median stance-foot speed is about 0.05-0.10 m/s depending on the trial sample and pose set, against about 0.02-0.05 m/s overground.
  • SKEL knee clamped at full extension. In many walking trials the SKEL knee sits on its 0 bound while the AddB target knee is flexed about 3-8 deg, more often on the left. Flagged per frame in knee_clamp_frame_kin / _dyn since 1.2.1 (1,061 trials above 10% of frames in poses). Not refitted. On kin_retimed trials knee_clamp_frame_dyn compares poses_dyn with q_gt, not with the retimed kinematic target.
  • Stairs (22 trials in activity class stairs). No per-step floor is shipped. floor_height_m is one loaded-step height per trial, and one fitted floor plane per trial (as in the T2M encoders) treats a staircase as a ramp, so heights above that floor are not step-relative.
  • Moore2015 standing (29 trials). In the wide-stance phases the feet sit 2-4 cm below the trial median floor level on about 40% of frames. Whether this is real penetration or a floor estimate set too high is not resolved.
  • Pathological persons (Fregly2012 TKA 78 trials, Li2021 stroke 65 trials). The caption_* phrases never mention the pathology, and the Li2021_S05 fits have about 1.8 times the healthy fit error (standing 26.6 mm vs 14.7 mm). Use them as an out-of-distribution stratum (eval_stratum).
  • Trials at 201 Hz and 250 Hz (by rounded fps_true_grid, 24 Han2023 trials and 1 Carter2023 trial at 250 Hz, 111 Tiziana2019 trials at 201 Hz) ship on their native grid. Resampling them to 100 Hz without a low-pass filter (as the T2M encoders do) aliases force-plate noise (Han2023: 7-30 N RMS below 50 Hz, peaks are kept).
  • vanderZee2022 walking. The median peak hip flexion torque is 1.95 N m/kg, against 1.0 (Carter2023) and 1.36 (Moore2015). The cause is not identified (possibly the plate cross-talk above).
  • q_kin beyond 2 pi on a shoulder DOF in a few trials (qc_q_kin_wrap): an AddB Euler branch of the pass-0 target, not unwrapped. The arm frames are masked in arm_event_ok_kin (1.2.1). The mask covers only the frames beyond 2 pi (+-0.1 s). In some of these trials the wrapped stretch is wider (an arm DOF of q_kin more than pi/2 from q_gt outside the mask), so skip arm losses on the whole trial if in doubt.
  • Constant-labelled treadmill belts that change speed (qc_belt_speed_change, _suspected) and segments whose label does not match the motion (label_conflict_codes SEGMENT_MOTION_MISMATCH) are flagged in 1.2.1. Trials and labels are unchanged.

License and attribution

The ForceBody data are released under the Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0). Commercial use is not permitted. LICENSE.md holds the full terms.

  • Fields derived from AddBiomechanics (forces, AddB kinematics, torques, uncertainty, metadata) build on the AddBiomechanics dataset (CC BY 4.0). Cite AddBiomechanics and every source study you use.
  • SKEL-derived data (the trial fields poses*, trans*, joints*, betas, skel_gender, the persons.csv columns beta_0 to beta_9 and shapy_*, and marker_vertex_map.json) are also subject to the SKEL model license, which allows non-commercial scientific research only. Building meshes from these fields requires the SKEL model, which you obtain from its authors under their license.
  • The code in the GitHub repository (forcebody_io.py, visualize.py, benchmark/, baselines/ in code/) is released under the MIT license. This Hugging Face repository holds no code.

How to cite 

@inproceedings{kwon2026forcebody,
  title     = {ForceBody: Force-Paired Parametric Body Motion with Torque Uncertainty},
  author    = {Kwon, Joonwoo and Zhang, Yufei and Liu, Xiaoming and Cui, Zijun},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}
@inproceedings{werling2024addbiomechanics,
  title     = {AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale},
  author    = {Werling, Keenon and Kaneda, Janelle and Tan, Tian and Agarwal, Rishi and Skov, Six and Van Wouwe, Tom and Uhlrich, Scott and Bianco, Nicholas and Ong, Carmichael and Falisse, Antoine and Sapkota, Shardul and Chandra, Aidan and Carter, Joshua and Preatoni, Ezio and Fregly, Benjamin and Hicks, Jennifer and Delp, Scott and Liu, C. Karen},
  booktitle = {European Conference on Computer Vision (ECCV 2024)},
  series    = {Lecture Notes in Computer Science},
  pages     = {490--508},
  publisher = {Springer},
  year      = {2024},
  doi       = {10.1007/978-3-031-73223-2_27}
}
@article{werling2023addbiomechanics,
  title   = {AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization},
  author  = {Werling, Keenon and Bianco, Nicholas A. and Raitor, Michael and Stingel, Jon and Hicks, Jennifer L. and Collins, Steven H. and Delp, Scott L. and Liu, C. Karen},
  journal = {PLOS ONE},
  volume  = {18},
  number  = {11},
  pages   = {e0295152},
  year    = {2023},
  doi     = {10.1371/journal.pone.0295152}
}

@article{keller2023skin,
  title   = {From Skin to Skeleton: Towards Biomechanically Accurate {3D} Digital Humans},
  author  = {Keller, Marilyn and Werling, Keenon and Shin, Soyong and Delp, Scott and Pujades, Sergi and Liu, C. Karen and Black, Michael J.},
  journal = {ACM Transactions on Graphics},
  volume  = {42},
  number  = {6},
  pages   = {1--12},
  year    = {2023},
  doi     = {10.1145/3618381}
}

Source studies. Cite every study whose trials you use (manifest columns study and study_citation). AddBiomechanics data: https://addbiomechanics.org/.

study study_citation reference
Carter2023 Carter2023 Carter, J., Chen, X., Cazzola, D., Trewartha, G., Preatoni, E. Full body kinematics and ground reaction forces of fifty heterogeneous runners completing treadmill running at various speeds and gradients. University of Bath Research Data Archive, 2024. https://doi.org/10.15125/BATH-01341
Falisse2017 Falisse2017 Falisse, A., Van Rossom, S., Jonkers, I., De Groote, F. EMG-driven optimal estimation of subject-specific Hill model muscle-tendon parameters of the knee joint actuators. IEEE Transactions on Biomedical Engineering 64(9), 2253-2262, 2017. https://doi.org/10.1109/TBME.2016.2630009
Fregly2012 Fregly2012 Fregly, B.J., Besier, T.F., Lloyd, D.G., Delp, S.L., Banks, S.A., Pandy, M.G., D'Lima, D.D. Grand challenge competition to predict in vivo knee loads. Journal of Orthopaedic Research 30(4), 503-513, 2012. https://doi.org/10.1002/jor.22023
Hamner2013 Hamner2013 Hamner, S.R., Delp, S.L. Muscle contributions to fore-aft and vertical body mass center accelerations over a range of running speeds. Journal of Biomechanics 46(4), 780-787, 2013. https://doi.org/10.1016/j.jbiomech.2012.11.024
Han2023 Han2023 Han, X., Senderling, B., To, S., Kumar, D., Whiting, E., Saito, J. GroundLink: A dataset unifying human body movement and ground reaction dynamics. SIGGRAPH Asia 2023 Conference Papers, 1-10, 2023. https://doi.org/10.1145/3610548.3618247
Li2021 Li2021 Li, G., Shourijeh, M.S., Ao, D., Patten, C., Fregly, B.J. How well do commonly used co-contraction indices approximate lower limb joint stiffness trends during gait for individuals post-stroke? Frontiers in Bioengineering and Biotechnology 8, 588908, 2021. https://doi.org/10.3389/fbioe.2020.588908
Moore2015 Moore2015 Moore, J.K., Hnat, S.K., van den Bogert, A.J. An elaborate data set on human gait and the effect of mechanical perturbations. PeerJ 3, e918, 2015. https://doi.org/10.7717/peerj.918
Tiziana2019 Lencioni2019 Lencioni, T., Carpinella, I., Rabuffetti, M., Marzegan, A., Ferrarin, M. Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks. Scientific Data 6, 309, 2019. https://doi.org/10.1038/s41597-019-0323-z
Uhlrich2023 Uhlrich2023 Uhlrich, S.D., Falisse, A., Kidziński, Ł., Muccini, J., Ko, M., Chaudhari, A.S., Hicks, J.L., Delp, S.L. OpenCap: Human movement dynamics from smartphone videos. PLOS Computational Biology 19(10), e1011462, 2023. https://doi.org/10.1371/journal.pcbi.1011462
vanderZee2022 vanderZee2022 van der Zee, T.J., Mundinger, E.M., Kuo, A.D. A biomechanics dataset of healthy human walking at various speeds, step lengths and step widths. Scientific Data 9, 704, 2022. https://doi.org/10.1038/s41597-022-01817-1

Versions

version date note
1.0.0-reviewed 2026-05-07 reviewed build (numbers of the submitted paper)
internal v1 2026-08-31 internal build
internal 1.1 2026-09-27 internal build (frame and timebase fixes)
1.2.0 2026-10-11 first frozen 1.2 build (first build 2026-10-09). Superseded by 1.2.1 (QC mask erratum)
1.2.1 2026-10-12 1.2.0 with corrected QC masks and flags (pose, force and torque arrays unchanged)
1.3.0 2026-10-29 this release: Hamner2013 study id (40 trial keys renamed, map shipped), sex_resolved corrected, benchmark results, documentation. Arrays otherwise identical to 1.2.1, see CHANGELOG.md. Documentation updated on 2026-11-01 and 2026-11-02 (SCHEMA_v1.3.csv descriptions only, no other data file changed)

Maintenance

Report errors and questions on the GitHub issues page https://github.com/DominoAI-Lab/ForceBody-NeurIPS-ED-2026/issues. CHANGELOG.md tracks issues and corrections per version. Replaced arrays are kept in an internal v1.1 provenance archive (not uploaded), so every change can be traced back to v1.1.