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.
The dataset is downloadable using following link https://data.mendeley.com/datasets/bcv9vsxkyc/2