Subject	Biomedical Engineering.
Specific subject area	Co-located sEMG-pFMG dataset for hand gesture recognition under Varying Arm-Position conditions.
Type of data	Raw data: 777 MB (.npy)
Data collection	Data were collected using a custom-built wearable armband comprising eight co-located sEMG-pFMG sensing pairs (16 channels). sEMG was acquired using stainless-steel electrodes and pFMG using Honeywell ABPDANT015PGAA5 pressure sensors, synchronized via a Teensy 4.1 microcontroller. Data acquisition was implemented in Python 3.10.4 (Visual Studio Code). Participants performed predefined hand gestures under single static, multiple static, and dynamic arm-posture conditions. sEMG signals were filtered (20–150 Hz, 50 Hz notch), whereas raw pFMG signals were preserved.
Data source location	The University of Wollongong, Wollongong, NSW 2522, Australia
Data accessibility	Repository name: sEMG+pFMG multimodal gesture data Direct URL to data: https://www.kaggle.com/datasets/uowmultimodalhgr/semg-pfmg-multimodal-gesture-data
Related research article	None.
Previously published dataset or data descriptor	None.