A kinematic and electromyographic study of grip in extension in a clinical setting

Abstract Aim: Grip, including grip in extension, is an essential element in human beings. The functional evaluation scales of the hand require a greater number of objective variables in order to offer an overall perspective. Devices such as surface electromyography and inertial sensors can be used in evaluation and tasks. Such equipment can lead to new variables for analysis and offer different approaches for treatment. Methods: Six participants were selected randomly from a sample of healthy population. Each participant made the grip in extension, which was parameterized in real time. This movement was analyzed and recorded in a synchronized manner with surface electromyography and accelerometer-type inertial sensors in the hand. Results: After analyzing and processing the data, it was possible to detect five phases within the movement thanks to in-depth analysis of the module vector of the index finger along with electromyography of the musculature of the first dorsal interosseous. Conclusions: Parameterization is possible in real time for the grip in extension based on surface electromyography and accelerometer, offering new analysis variables on hand operation while providing a suitable complement to standardized assessments. Implications for Rehabilitation The use of surface electromyography and accelerometry in the arm synchronously, allows clinicians to identify new intervention and treatment variables. The protocol developed can be used in clinical practice because it is non-invasive, the enabled devices do not cause damage on the subject. According to the results, the muscles of the hypothenar region and the first dorsal interosseous have greater activation in the hand during grip extension. The wrist extensor muscles and flexor carpi ulnaris have more activation during the approach phase to grip. These are the muscles that should be prioritized for rehabilitation. The variation of the acceleration allows you to differentiate between the phases of rest and movement of the hand. If the variation of the acceleration at rest is higher than 0.3g, this could indicate the presence of abnormal movements or tremor.

[1]  Marco Santello,et al.  Transfer of Learned Manipulation following Changes in Degrees of Freedom , 2011, The Journal of Neuroscience.

[2]  Roberto Merletti,et al.  Advances in surface EMG: recent progress in clinical research applications. , 2010, Critical reviews in biomedical engineering.

[3]  K. Pierzchala,et al.  The usefulness of accelerometric registration with assessment of tremor parameters and their symmetry in differential diagnosis of parkinsonian, essential and cerebellar tremor. , 2012, Neurologia i neurochirurgia polska.

[4]  Antonio I Cuesta-Vargas,et al.  The use of inertial sensors system for human motion analysis , 2010, Physical therapy reviews : PTR.

[5]  Xiang Chen,et al.  Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Dinesh K Kumar,et al.  Fractal feature of sEMG from Flexor digitorum superficialis muscle correlated with levels of contraction during low-level finger flexions , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[7]  S. Peiró,et al.  [Spanish version of the DASH questionnaire. Cross-cultural adaptation, reliability, validity and responsiveness]. , 2006, Medicina clinica.

[8]  S. Black,et al.  The Fugl-Meyer Assessment of Motor Recovery after Stroke: A Critical Review of Its Measurement Properties , 2002, Neurorehabilitation and neural repair.

[9]  P. Bonato,et al.  Data mining techniques to detect motor fluctuations in Parkinson's disease , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Hong Liu,et al.  Estimation of hand grasp force based on forearm surface EMG , 2009, 2009 International Conference on Mechatronics and Automation.

[11]  C.J. De Luca,et al.  A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Silvia Conforto,et al.  Early recognition of upper limb motor tasks through accelerometers: real-time implementation of a DTW-based algorithm , 2011, Comput. Biol. Medicine.

[13]  S. Peiró,et al.  Versión española del cuestionario DASH. Adaptación transcultural, fiabilidad, validez y sensibilidad a los cambios , 2006, Medicina Clínica.

[14]  Stuart Donaldson,et al.  SEMG Evaluations: An Overview , 2003, Applied psychophysiology and biofeedback.

[15]  Marco Santello,et al.  Common input to motor units of digit flexors during multi-digit grasping. , 2004, Journal of neurophysiology.

[16]  H. Hermens,et al.  SENIAM 8: European recommendations for surface electromyography , 1999 .

[17]  M. Hodgson.  The medical evaluation. , 1995, Occupational medicine.

[18]  J. Verbunt,et al.  Assessment of arm activity using triaxial accelerometry in patients with a stroke. , 2011, Archives of physical medicine and rehabilitation.

[19]  Qiang Zhan,et al.  Description of the human hand grasp using graph theory. , 2013, Medical engineering & physics.

[20]  Joseph D. Towles,et al.  Towards a realistic biomechanical model of the thumb: the choice of kinematic description may be more critical than the solution method or the variability/uncertainty of musculoskeletal parameters. , 2003, Journal of biomechanics.

[21]  M. Santello,et al.  Common input to motor units of intrinsic and extrinsic hand muscles during two-digit object hold. , 2008, Journal of neurophysiology.

[22]  Ganesh R. Naik,et al.  Identification of Hand and Finger Movements Using Multi Run ICA of Surface Electromyogram , 2012, Journal of Medical Systems.

[23]  Paolo Bonato,et al.  Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors , 2009, IEEE Transactions on Information Technology in Biomedicine.

[24]  L. Matheson,et al.  Development and Construct Validation of the Hand Function Sort , 2001, Journal of Occupational Rehabilitation.

[25]  Øyvind Stavdahl,et al.  A multi-modal approach for hand motion classification using surface EMG and accelerometers , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Dennis R. Whitlow,et al.  MUSCLE TESTING: TECHNIQUES OF MANUAL EXAMINATION , 1974 .

[27]  M. Marfell-Jones,et al.  International standards for anthropometric assessment. , 2012 .

[28]  Herbert F. Voigt,et al.  IEEE Engineering in Medicine and Biology Society , 2019, IEEE Transactions on Biomedical Engineering.

[29]  Konrad Paul Kording,et al.  The statistics of natural hand movements , 2008, Experimental Brain Research.

[30]  G E Caldwell,et al.  Elbow torques and EMG patterns of flexor muscles during different isometric tasks. , 1991, Electromyography and clinical neurophysiology.

[31]  E. Delagi,et al.  Anatomical guide for the electromyographer : the limbs and trunk /by Edward F. Delagi [et al.] ; illustrated by Phyllis B. Hammond, Aldo O. Perotto, and Hugh Thomas , 2005 .

[32]  Yang Jihai,et al.  Dynamic gesture recognition based on multiple sensors fusion technology , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  R. Buschbacher.  Anatomical Guide for the Electromyographer: The Limbs and Trunk , 2007 .

[34]  C. Light,et al.  Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity. , 2002, Archives of physical medicine and rehabilitation.

[35]  T. M. Hamm,et al.  Influence of fatigue on hand muscle coordination and EMG-EMG coherence during three-digit grasping. , 2010, Journal of neurophysiology.

[36]  L. Michener,et al.  The Upper Limb Functional Index: development and determination of reliability, validity, and responsiveness. , 2006, Journal of hand therapy : official journal of the American Society of Hand Therapists.

[37]  C. Pipper,et al.  [''R"--project for statistical computing]. , 2008, Ugeskrift for laeger.