Abbas Khalid A, Rashid Mofeed Turky, Fortuna Luigi
Electrical Engineering Department, University of Basrah, Basrah, Iraq.
Computer Engineering Department, University of Basrah, Basrah, Iraq.
Data Brief. 2025 Apr 14;60:111558. doi: 10.1016/j.dib.2025.111558. eCollection 2025 Jun.
Mechanomyography (MMG) datasets are crucial due to their unique characteristics, non-invasive techniques, fewer required sensors, improved signal-to-noise ratio, lightweight equipment, and no need for skin preparation, unlike some other techniques. This paper introduces a mechanomyography (MMG) signal dataset intended for application in human-computer interaction (HCI) research. The dataset is obtained from integrated sensor data, capturing mechanical signals from muscle activity via the accelerometer, augmented by the gyroscope for motion analysis. The dataset comprises 6-axis accelerometer and gyroscope data from 43 participants, ranging in age from 18 to 69 years, exhibiting a male-to-female distribution of 60 % to 40 % respectively. The dataset includes the following 11 gestures: clapping, coin flipping, finger snapping, fist making, horizontal wrist extension, index finger flicking, index thumb tapping, shooting, thumb up, wrist extension, and wrist flexion. A novel, assembled, and manufactured wearable system collected data from the main muscles that end at the wrist, just below the watch strap. These muscles include flexors and extensors, which work together to move the wrist and fingers when making the hand gestures listed above. Every participant completed a total of fifty repetitions for each of the eleven hand motions, resulting in 550 samples per subject. Before recording the signals, a demographic survey with the participants is conducted. Researchers focusing on classification, recognition, and prediction can use the gathered data to develop MMG-based hand motion controller systems. The collected data can also serve as a reference for developing a model using artificial intelligence (e.g., a deep learning or machine learning model) that is capable of identifying gesture-related MMG signals. It is suggested that the proposed dataset is used to evaluate existing datasets in the literature or to validate artificial intelligence models developed with alternative datasets through the participant-independent evaluation approach. This dataset can be useful in a variety of applications and fields, including interaction between humans and robots, gaming, assistive technology, healthcare observation, and sports analytics, to name a few specific examples.
肌动图（MMG）数据集因其独特的特性、非侵入性技术、所需传感器较少、信噪比提高、设备轻便且无需皮肤准备（与其他一些技术不同）而至关重要。本文介绍了一个旨在用于人机交互（HCI）研究的肌动图（MMG）信号数据集。该数据集来自集成传感器数据，通过加速度计捕捉肌肉活动的机械信号，并由陀螺仪增强以进行运动分析。该数据集包含来自43名参与者的六轴加速度计和陀螺仪数据，年龄在18至69岁之间，男女分布分别为60%和40%。该数据集包括以下11种手势：鼓掌、抛硬币、打响指、握拳、手腕水平伸展、食指轻弹、食指拇指轻敲、射击、竖起大拇指、手腕伸展和手腕弯曲。一个新颖的、组装制造的可穿戴系统从位于手表表带下方手腕处的主要肌肉收集数据。这些肌肉包括屈肌和伸肌，在做出上述手势时它们共同作用以移动手腕和手指。每个参与者对这十一种手部动作中的每一种都总共完成了五十次重复，每个受试者产生550个样本。在记录信号之前，对参与者进行了人口统计调查。专注于分类、识别和预测的研究人员可以使用收集到的数据来开发基于MMG的手部运动控制器系统。收集到的数据还可以作为开发使用人工智能（例如深度学习或机器学习模型）的模型的参考，该模型能够识别与手势相关的MMG信号。建议使用所提出的数据集来评估文献中的现有数据集，或通过参与者独立评估方法验证使用替代数据集开发的人工智能模型。这个数据集在各种应用和领域中可能会很有用，包括人机与机器人交互、游戏、辅助技术、医疗观察和体育分析等一些具体例子。
