Dataset on Force Myography for Human Robot Interactions

Force myography (FMG) is a contemporary, non-invasive, wearable technology that can read the underlying muscle volumetric changes during muscle contractions and expansions. The FMG technique can be used in recognizing human applied hand forces during physical human robot interactions (pHRI) via data-driven models. Several FMG-based pHRI studies were conducted in 1D, 2D and 3D during dynamic interactions between a human participant and a robot to realize human applied forces in intended directions during certain tasks. Raw FMG signals were collected via 16-channel (forearm) and 32-channel (forearm and upper arm) FMG bands while interacting with a biaxial stage (linear robot) and a serial manipulator (Kuka robot). In this paper, we present the datasets and their structures, the pHRI environments, and the collaborative tasks performed during the studies. We believe these datasets can be useful in future studies on FMG biosignal-based pHRI control design.

The full description of this dataset, it’s components and structure are available in the data descriptor article submitted in MDPI Data. Please cite the following data descriptor article if you are using this open-access dataset for legitimate scientific research:

     U. Zakia, and C. Menon. Dataset on Force Myography for Human Robot Interactions. Data 2022, vol., no., pp, doi: (submitted on        June 2022).

肌力描记法（Force myography, FMG）是一种新兴的非侵入式可穿戴技术，可检测肌肉收缩与舒张过程中深层的肌肉体积变化。该FMG技术可通过数据驱动模型，识别物理人机交互（physical human robot interactions, pHRI）过程中人类施加的手部作用力。已有多项基于FMG的pHRI研究，在人类受试者与机器人的动态交互场景中，于一维、二维及三维空间下开展，旨在精准识别特定任务中人类按预期方向施加的作用力。原始FMG信号通过16通道（前臂）及32通道（前臂与上臂）FMG采集绑带采集得到，采集场景为与双轴位移台（线性机器人）及串联机械臂（库卡KUKA机器人）的交互过程。本文将介绍本次数据集及其结构、pHRI实验环境，以及研究过程中开展的协作任务。我们认为，该数据集可为后续基于FMG生物信号的pHRI控制设计研究提供助力。

本数据集的完整说明、组成部分与结构，可查阅已提交至MDPI《Data》期刊的数据描述论文。

若您将该开源数据集用于合法的科学研究，请引用以下数据描述论文：

U. Zakia与C. Menon. 用于人机交互的肌力描记法数据集. 《Data》2022年，卷，期，页码，DOI：（2022年6月提交）。