ravenwing/cheedeh-model

cheedeh-gesture-classifier

ONNX model for classifying phone air-gestures from accelerometer data. Designed for on-device inference on Android. Trained with scikit-learn (StandardScaler + SVM rbf), exported to ONNX.

Classes: z, m, s, o, none

Files

File	Description
gesture_classifier.onnx	Inference model (StandardScaler + SVM, ONNX opset 15)
label_map.json	Maps output class index (0–4) to gesture name

Model Details

Property	Value
Architecture	StandardScaler + SVM (rbf, C=10, gamma=scale)
Input	52 hand-crafted features from 3-axis accelerometer
Output	Class index (int64) + probabilities (float32[5])
Test accuracy	0.759
Macro F1	0.793
Training samples	~372
Test samples	~54

Usage

Input tensor: float32[1, 52] — 52 features extracted from a 100-point resampled accelerometer gesture. Output tensors: int64[1] (class index), float32[1, 5] (class probabilities).

For data collection and inference implementation see cheedeh-collect.

Input Sensor Requirements

• Sensor type: TYPE_LINEAR_ACCELERATION (gravity-compensated, m/s²)
• Sample rate: ~50 Hz (interpolated to exactly 100 points before feature extraction)
• Gesture duration: typically 0.5–3 seconds

The none class represents background / non-gesture motion.

Training

Trained on the cheedeh-IMU-data dataset collected with the cheedeh-collect Android app. Training pipeline at cheedeh-learn.

Class weights were balanced during training to handle imbalanced class distribution.