README.md

---
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`.

```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

```python
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](https://github.com/ravenwing/cheedeh)) using `TYPE_LINEAR_ACCELERATION` and gyroscope sensors. Each session was a manual recording of one gesture, labeled at collection time.

## Related

- Training code: [learn/](https://github.com/ravenwing/cheedeh/tree/main/learn) — SVM/RF classifier achieving ~0.76 test accuracy, exported to ONNX for on-device inference.