SEEDS, simultaneous recordings of high-density EMG and finger joint angles during multiple hand movements

AbstractWe present the SurfacE Electromyographic with hanD kinematicS (SEEDS) database. It contains electromyographic (EMG) signals and hand kinematics recorded from the forearm muscles of 25 non-disabled subjects while performing 13 different movements at normal and slow-paced speeds. EMG signals were recorded with a high-density 126-channel array centered on the extrinsic flexors of the fingers and 8 further electrodes placed on the extrinsic extensor muscles. A data-glove was used to record 18 angles from the joints of the wrist and fingers. The correct synchronisation of the data-glove and the EMG was ascertained and the resulting data were further validated by implementing a simple classification of the movements. These data can be used to test experimental hypotheses regarding EMG and hand kinematics. Our database allows for the extraction of the neural drive as well as performing electrode selection from the high-density EMG signals. Moreover, the hand kinematic signals allow the development of proportional methods of control of the hand in addition to the more traditional movement classification approaches.Measurement(s)muscle electrophysiology traitTechnology Type(s)electromyographyFactor Type(s)age • sex • dominant handSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9867962

[1]  Dario Farina,et al.  EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees , 2011, Journal of NeuroEngineering and Rehabilitation.

[2]  Max Ortiz-Catalan Deciphering neural drive , .

[3]  Manfredo Atzori,et al.  Comparison of six electromyography acquisition setups on hand movement classification tasks , 2017, PloS one.

[4]  Tanja Schultz,et al.  Advancing Muscle-Computer Interfaces with High-Density Electromyography , 2015, CHI.

[5]  Christian Cipriani,et al.  The SSSA-MyHand: A Dexterous Lightweight Myoelectric Hand Prosthesis , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Pornchai Phukpattaranont,et al.  Feature reduction and selection for EMG signal classification , 2012, Expert Syst. Appl..

[7]  Aaron M. Dollar,et al.  An investigation of grasp type and frequency in daily household and machine shop tasks , 2011, 2011 IEEE International Conference on Robotics and Automation.

[8]  Manfredo Atzori,et al.  Electromyography data for non-invasive naturally-controlled robotic hand prostheses , 2014, Scientific Data.

[9]  Max Ortiz-Catalan,et al.  Neuroengineering: Deciphering neural drive , 2017, Nature Biomedical Engineering.

[10]  Yu Hu,et al.  Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation , 2017, Sensors.

[11]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[12]  Dario Farina,et al.  The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  Bert U Kleine,et al.  Using two-dimensional spatial information in decomposition of surface EMG signals. , 2007, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[14]  Christian Cipriani,et al.  Independent Long Fingers are not Essential for a Grasping Hand , 2016, Scientific Reports.

[15]  Dario Farina,et al.  Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control , 2014, IEEE Transactions on Biomedical Engineering.

[16]  Jacob L. Segil,et al.  Mechanical design and performance specifications of anthropomorphic prosthetic hands: a review. , 2013, Journal of rehabilitation research and development.

[17]  J. Forsberg,et al.  Traumatic and trauma-related amputations: Part II: Upper extremity and future directions. , 2010, The Journal of bone and joint surgery. American volume.

[18]  Dario Farina,et al.  Decoding the neural drive to muscles from the surface electromyogram , 2010, Clinical Neurophysiology.

[19]  Dario Farina,et al.  Reflections on the present and future of upper limb prostheses , 2016, Expert review of medical devices.

[20]  Sean R. Anderson,et al.  Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework , 2018, Front. Bioeng. Biotechnol..

[21]  Arto Visala,et al.  urrent state of digital signal processing in myoelectric interfaces and elated applications , 2015 .

[22]  O. Stavdahl,et al.  Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  D. Atkins,et al.  Epidemiologic Overview of Individuals with Upper-Limb Loss and Their Reported Research Priorities , 1996 .