EMG-EPN-107

The dataset, named EMG-EPN-107, contains electromyography (EMG) and orientation data collected from 107 participants for the study and benchmarking of hand gesture recognition systems. It was developed by the Artificial Intelligence and Computer Vision Research Laboratory “Alan Turing” of Escuela Politécnica Nacional, Quito, Ecuador. Data acquisition was carried out using two devices: the G-ForcePro armband, which records EMG signals at 500 Hz and inertial measurement unit (IMU) data at 50 Hz, and the Myo armband, which records EMG signals at 200 Hz and IMU data at 50 Hz, allowing the capture of both muscular activity and motion-related information from the forearm.

The dataset comprises 11 hand gestures grouped into 12 classes, including five gestures (wave-in, wave-out, fist, open, and pinch), six directional gestures (up, down, left, right, forward, and backward), and a relax or no gesture class. Each participant performed 30 repetitions per gesture, resulting in 360 samples per user, with each sample lasting 5 seconds. In addition, each user performed five repetitions of a synchronisation gesture, each lasting 10 seconds, which are intended to support signal alignment and calibration.

The dataset is organised into two levels of division. The 107 participants are split into a training group comprising 54 users and a testing group comprising 53 users. For each participant, the recorded samples are further divided into training and testing repetitions, each containing 180 samples. For benchmarking purposes, labels are partially hidden: only the samples corresponding to the testing repetitions of users in the testing group have hidden labels, enabling unbiased evaluation of classification models.

Each user folder contains a photograph of the participant’s forearm showing the placement of the acquisition device, where the face is not visible in order to preserve anonymity. Additionally, each folder includes MATLAB (.mat) files that store both the recorded signals and associated metadata within a structured variable named userData. The most relevant information includes non-identifiable participant data such as age, gender, handedness, occupation, and forearm-related measurements; acquisition metadata such as repetition counts, duration per repetition, synchronisation settings, and recording date; and device information including the acquisition system used and EMG sampling rate. The recorded data are organised into three main subsets corresponding to synchronisation samples, training samples, and testing samples.

数据集命名为EMG-EPN-107，收录了107名受试者的肌电（electromyography, EMG）与姿态数据，用于手部手势识别系统的研究与基准测试。该数据集由厄瓜多尔基多国立理工学院（Escuela Politécnica Nacional）“艾伦·图灵”人工智能与计算机视觉研究实验室开发。本次数据采集采用两款设备：G-ForcePro臂环，其肌电信号采样率为500Hz，惯性测量单元（inertial measurement unit, IMU）数据采样率为50Hz；以及Myo臂环，其肌电信号采样率为200Hz，IMU数据采样率为50Hz，可捕获前臂的肌肉活动与运动相关信息。
该数据集涵盖11种手部手势，分为12个类别，包括5种基础手势（挥手向内、挥手向外、握拳、张开手掌、捏合），6种方向手势（上、下、左、右、前、后），以及放松/无手势类别。每名受试者需完成每种手势30次重复操作，每名用户共生成360条样本，每条样本时长为5秒。此外，每名受试者需完成5次同步手势重复操作，每次时长10秒，用于辅助信号对齐与校准。
数据集采用两级划分机制。107名受试者被分为训练组（54名用户）与测试组（53名用户）。针对每名受试者，其记录的样本进一步划分为训练重复样本与测试重复样本，各含180条样本。为满足基准测试需求，部分标签被隐藏：仅测试组用户的测试重复样本标签处于隐藏状态，以实现分类模型的无偏评估。
每个用户文件夹中包含一张受试者前臂照片，展示了采集设备的佩戴位置，照片未露出受试者面部以保护匿名性。此外，每个文件夹均包含MATLAB格式（.mat）文件，其中以结构化变量userData存储了记录的信号与相关元数据。其中关键信息包括不可识别的受试者个人数据，如年龄、性别、利手性、职业与前臂相关测量数据；采集元数据，如重复次数、单次重复时长、同步设置与录制日期；以及设备信息，如所用采集系统与肌电采样率。记录的数据分为三大主要子集，分别对应同步样本、训练样本与测试样本。