Physical Action Dataset

Thousands of people around the globe are limited in their daily activities due to physical or mental disability, which hinders in their daily tasks. All such individuals can get a boost in their lives by introduction of computer aided assistive applications that help them by classifying the muscle movements. Designing of such systems gets further uplifts by availability of non-invasive sensors, like Surface Electromyography (sEMG), which can be mapped onto the routine activities using machine learning. In this dataset, sEMG signature against routine activities such as typing, resting, lifting and exercise (push up) has been acquired. The sEMG data is coupled with information from a 9-DoF inertial measurement unit (IMU) in the form of accelerometer, gyro and derived orientation signals. Fusion of sEMG with IMU data can lead us to get better classification results especially while segregating abnormal data from normal or routine actions. This data can be of particular value to the researchers working on computer-aided systems for subjects with physical or mental disabilities.全球范围内，数以千计的人群因躯体或精神残疾导致日常活动受限，严重阻碍其完成日常事务。通过引入可对肌肉运动进行分类的计算机辅助应用程序，此类人群的生活质量可得到显著改善。此类系统的研发可借助无创传感器的普及获得进一步推进，例如表面肌电（Surface Electromyography, sEMG）——借助机器学习技术，该类传感器可与日常活动建立映射关联。本数据集采集了针对打字、休息、负重及俯卧撑等日常活动的表面肌电信号特征。该表面肌电数据同步搭载了来自9自由度惯性测量单元（Inertial Measurement Unit, IMU）的信息，包括加速度计、陀螺仪及衍生方位信号。将表面肌电数据与惯性测量单元数据进行融合，可助力获得更优异的分类效果，尤其在区分异常数据与正常或日常动作场景中效果突出。该数据集对于致力于为躯体或精神残疾人群研发计算机辅助系统的研究人员而言，具有重要的学术与应用价值。