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sEMG dataset of routine activities.

Asad Mansoor Khan1, Sajid Gul Khawaja1, Muhammad Usman Akram1

  • 1Department of Computer and Software Engineering, CEME, National University of Sciences and Technology, Islamabad, Pakistan.

Data in Brief
|December 11, 2020
PubMed
Summary

This study introduces a new dataset combining surface electromyography (sEMG) and Inertial Measurement Unit (IMU) signals for human activity recognition. This data aids in developing assistive technologies for individuals with disabilities.

Keywords:
AccelerometerGyroscopeIMUPhysical actionsRoutine activitiessEMG

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Rehabilitation Engineering

Background:

  • Surface electromyography (sEMG) and Inertial Measurement Unit (IMU) sensors are crucial for capturing physiological and motion data.
  • Accurate human activity recognition is vital for developing advanced assistive technologies.
  • Existing datasets may lack comprehensive multi-modal data for diverse daily activities.

Purpose of the Study:

  • To present a novel, multi-modal dataset capturing human muscle activity during routine tasks.
  • To provide raw and derived sensor data for advanced activity classification research.
  • To facilitate the development of intelligent assistive systems.

Main Methods:

  • Utilized the Myo Thalamic Armband to collect sEMG signals from forearm muscles.
  • Acquired synchronized data from sEMG and IMU sensors (accelerometer, gyroscope, orientation).
  • Recorded data during four distinct activities: resting, typing, push-up exercise, and lifting a heavy object.

Main Results:

  • The dataset includes raw sEMG, accelerometer, gyroscope, and derived orientation signals.
  • Five data files are associated with each of the four recorded activities.
  • Demonstrated the potential for fusing IMU and sEMG data for improved activity classification, distinguishing between normal and aggressive movements.

Conclusions:

  • The presented dataset offers a valuable resource for researchers in human activity recognition.
  • Fusion of sEMG and IMU data enhances the ability to differentiate between various physical activities.
  • This data can significantly contribute to the advancement of assistive computer-aided support systems for individuals with physical or mental impairments.