louisye/social-robotics-affirmation-gesture

Social Robotics: Affirmation Gesture (03e)

Did the bystander nod (affirm) or shake their head (negate)?

One layer of the Social-Affective Filter (SAF) — dehydrated social-signal metadata extracted from egocentric (first-person) video so robots can learn to read human reactions. No raw pixels and no audio. Each row is one source video, keyed by video_id; rehydrate against your own legally-obtained Ego4D copies (below).

• Rows: 10 — videos in the 50-clip evaluation slice for which this layer produced a measured signal (videos it could not measure are excluded from this per-layer dataset; the layers still join 1:1 on video_id).
• Signal: Detection of communicative head nods (affirming_nod) and shakes (negating_shake) from rhythmic head-pose oscillation during the task window.
• Method: 1–3 Hz Butterworth band-pass + SciPy peak-finding on 03a's head-pose pitch/yaw traces (no extra inference).

⚠️ Read this first — interpretation caveats

• Derived from 03a's head-pose trace, which is sparse on egocentric footage — so this layer is low-yield by nature.
• interpolated_fraction in the raw column flags how much of the window was bbox-interpolated; high values are lower-trust.
• Egocentric footage is legitimately low-yield (small/sparse bystander faces, heavy camera motion); we publish honest measurements only, never fabricated zeros.

Columns

Identity & manifest (shared across all SAF datasets)

column	type	meaning
video_id	string	Ego4D source-clip UUID. The rehydration key — map back to your own legally-obtained Ego4D copy (<video_id>.mp4).
source_dataset	string	Origin corpus (ego4d).
task_labels	string	Comma-joined VLM task label(s) — the activity the camera-wearer performed.
duration_sec	float	Source clip duration (seconds).
fps	float	Source clip frame rate.

Affirmation Gesture signal

column	type	meaning
affirmation_gesture_tasks_analyzed_raw	JSON string	Per-task / per-person detail: pitch_oscillation_hz, yaw_oscillation_hz, interpolated_fraction, gesture_detected (affirming_nod/negating_shake/ambiguous_wobble/none), confidence.
affirmation_gesture_max_gesture_confidence	float (0–1)	Max gesture confidence across bystanders.
affirmation_gesture_any_nod_detected	bool	True if any bystander produced an affirming_nod.
affirmation_gesture_any_shake_detected	bool	True if any bystander produced a negating_shake.

The *_raw JSON column

The *_raw column holds the full nested per-task / per-person detail as a JSON string. Parse it with:

import json, pandas as pd
df = pd.read_parquet("hf://datasets/louisye/social-robotics-affirmation-gesture/social_metadata.parquet")
raw_col = next(c for c in df.columns if c.endswith("_raw"))
detail = json.loads(df.iloc[0][raw_col])

How to load

import pandas as pd
df = pd.read_parquet("hf://datasets/louisye/social-robotics-affirmation-gesture/social_metadata.parquet")
# or:  from datasets import load_dataset;  ds = load_dataset("louisye/social-robotics-affirmation-gesture")

Rehydration — mapping back to video

video_id is the Ego4D clip UUID. With your own licensed Ego4D copy, the file is <video_id>.mp4; timestamps in the *_raw columns index into that clip. A helper (rehydrate_dataset.py) is included. We never redistribute source media — obtain Ego4D under its own license.

Provenance

Generated by the SAF pipeline (export_metadata.json records schema_version + pipeline_git_sha). Headers are the descriptive layer name + metric; the pipeline's internal 03e_ layer-id prefix is stripped at publish time. License MIT (this metadata only; Ego4D videos remain under the Ego4D license).