Datasets:

ravenwing
/

cheedeh-IMU-data

license: mit
task_categories:
  - other
pretty_name: Cheedeh IMU Gesture Data
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train/*.json
      - split: test
        path: test/*.json

Cheedeh IMU Gesture Data

Phone IMU recordings of mid-air gestures drawn in the air with the phone. Collected to train a real-time gesture classifier running on Android.

Gestures (labels)

Label	Gesture
`z`	Letter Z
`m`	Letter M
`s`	Letter S
`o`	Letter O (circle)
`none`	No gesture / idle

Splits

Split	Samples	m	none	o	s	z
train	266	31	111	43	42	39
test	54	15	20	8	4	7

Data Format

Each sample is a separate JSON file named gesture_{label}_{YYYYMMDD}_{HHMMSS}.json.

{
  "gesture_id": "m_20260210_160623",
  "gesture_name": "m",
  "timestamp": "2026-02-10T15:06:23.660296Z",
  "duration_ms": 3246,
  "sample_rate_hz": 57,
  "data": [
    {
      "t":   1,
      "x":  -0.072,  "y": -0.143,  "z":  0.456,
      "gx": -0.011,  "gy": -0.043, "gz":  0.020,
      "grx": 0.522,  "gry": 4.386, "grz": 8.759,
      "qx":  0.152,  "qy": -0.175, "qz": -0.653, "qw": 0.721
    },
    ...
  ]
}

Fields in `data` array

Field	Sensor	Unit
`t`	Elapsed time	ms
`x`, `y`, `z`	Linear acceleration (`TYPE_LINEAR_ACCELERATION`)	m/s²
`gx`, `gy`, `gz`	Gyroscope (angular velocity)	rad/s
`grx`, `gry`, `grz`	Gravity vector (rotation frame)	m/s²
`qx`, `qy`, `qz`, `qw`	Orientation quaternion	—

Sample rate is approximately 50–57 Hz. Duration varies by gesture (typically 1–4 s).

Loading

from datasets import load_dataset

ds = load_dataset("ravenwing/cheedeh-IMU-data")
sample = ds["train"][0]
# sample["gesture_name"]  → "m"
# sample["data"]          → list of dicts with t, x, y, z, ...

Collection

Recorded with an Android app (gather-data) using TYPE_LINEAR_ACCELERATION and gyroscope sensors. Each session was a manual recording of one gesture, labeled at collection time.

Training code: learn/ — SVM/RF classifier achieving ~0.76 test accuracy, exported to ONNX for on-device inference.