InGesture Dataset

The InGesture dataset provides high-resolution (200 Hz) inertial sensor data for hand gesture recognition, focusing on the challenge of distinguishing fluid intake from seven other kinematically similar gestures. Data were collected from 50 participants (34 male, 16 female, aged 18-67) across 65 recording sessions.

An inertial sensor (WT901BLECL5 IMU) was placed on each participant's dominant wrist, and gestures were annotated in real-time with a synchronized mobile app to ensure high temporal accuracy. The full dataset contains de labeled gesture instances.

To facilitate use, the data is provided in two complementary formats:
1. Continuous Recordings:
CSV files containing the full recording of each session (~10 minutes), ideal for segmentation and sequence modeling tasks.
Columns: timestamp, accX/Y/Z (accelerometer), asX/Y/Z (gyroscope), and label (gesture code).

2. Segmented Gestures:
Individual CSV files, where each file represents a single, pre-segmented gesture instance, ready for use in classification models.
Columns: timestamp, x/y/z (accelerometer), gx/gy/gz (gyroscope). The filename indicates the gesture (e.g., fluid_intake_1_800.csv).

Gesture Classes (Labels):
0: Free Condition / Other
1: Fluid Intake
2: Answering Phone
3: Scratching Head
4: Passing Hand over Face
5: Adjusting Glasses
6: Holding Chin
7: Stretching with Hands behind Neck

Additional Resources:
To accelerate analysis, the repository includes:
two Jupyter Notebook with Python code for loading, processing, and visualizing the data and classify examples.
A detailed metadata file with participant demographics (age, sex, height, weight) and session details (e.g., container type used), allowing for robust, stratified analysis.