A kinematic and electromyographic study of grip force

References

• Hoang-Kim A, Pegreffi F, Moroni A, Ladd A. Measuring wrist and hand function: Common scales and checklists. Injury. 2011;42:253–8.
   PubMed Web of Science ®Google Scholar
• Kapandji IA. Articular physiology: Commented schemes of human mechanics. Upper limb. Bogotá: Editorial Médica Panamericana; 2006.
   Google Scholar
• Light CM, Chappell PH, Kyberd PJ. Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: Normative data, reliability, and validity. Arch Phys Med Rehabil. 2002;83:776–83.
   PubMed Web of Science ®Google Scholar
• Sancho-Bru JL, Pérez-González A, Vergara M, Giurintano DJ. A 3D biomechanical model of the hand for power grip. J Biomech Eng. 2003;125:78–83.
   PubMed Web of Science ®Google Scholar
• Vicon. Motion Capture Systems from Vicon [Internet]. 2012 [citado 18 de noviembre de 2012]. Recuperado a partir de: http://www.vicon.com/
   Google Scholar
• Windolf M, Götzen N, Morlock M. Systematic accuracy and precision analysis of video motion capturing systems – Exemplified on the Vicon-460 system. J Biomech. 2008;41:2776–80.
   PubMed Web of Science ®Google Scholar
• Cuesta-Vargas A, Galán-Mercant A, Williams J. The use of inertial sensors system for human motion analysis. Phys Ther Rev. 2010;15:462–73.
   PubMedGoogle Scholar
• Luinge HJ, Veltink PH. Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med Biol Eng Comput. 2005;43:273–82.
   PubMed Web of Science ®Google Scholar
• Hendelman D, Miller K, Baggett C, Debold E, Freedson P. Validity of accelerometry for the assessment of moderate intensity physical activity in the field. Med Sci Sports Exerc. 2000;32(9 Suppl):S442–449.
   Web of Science ®Google Scholar
• Tien I, Glaser SD, Aminoff MJ. Characterization of gait abnormalities in Parkinson's disease using a wireless inertial sensor system. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:3353–6.
   PubMedGoogle Scholar
• Muscillo R, Schmid M, Conforto S, D’Alessio T. Early recognition of upper limb motor tasks through accelerometers: Real-time implementation of a DTW-based algorithm. Comput Biol Med. 2011;41:164–72.
   PubMed Web of Science ®Google Scholar
• Mannini A, Sabatini AM. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors (Basel). 2010;10:1154–75.
   PubMed Web of Science ®Google Scholar
• Calautti C, Jones PS, Persaud N, Guincestre J-Y, Naccarato M, Warburton EA, et al. Quantification of index tapping regularity after stroke with tri-axial accelerometry. Brain Res Bull. 2006;70:1–7.
   PubMed Web of Science ®Google Scholar
• Disselhorst-Klug C, Schmitz-Rode T, Rau G. Surface electromyography and muscle force: Limits in sEMG-force relationship and new approaches for applications. Clin Biomech (Bristol, Avon). 2009;24:225–35.
   PubMed Web of Science ®Google Scholar
• DiTrapani R, Rubin DI. A day in the EMG laboratory: Case studies of 10 patients with different clinical problems. Neurol Clin. 2012;30:731–55.
   PubMed Web of Science ®Google Scholar
• Fujii S, Moritani T. Spike shape analysis of surface electromyographic activity in wrist flexor and extensor muscles of the world's fastest drummer. Neurosci Lett. 2012;514:185–8.
   PubMed Web of Science ®Google Scholar
• Tang X, Liu Y, Lv C, Sun D. Hand motion classification using a multi-channel surface electromyography sensor. Sensors (Basel). 2012;12:1130–47.
   PubMed Web of Science ®Google Scholar
• Ryait HS, Arora AS, Agarwal R. Study of issues in the development of surface EMG controlled human hand. J Mater Sci Mater Med. 2009;20 Suppl 1:S107–114.
   Web of Science ®Google Scholar
• Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol. 2000;10:361–74.
   PubMed Web of Science ®Google Scholar
• Mogk JPM, Keir PJ. Crosstalk in surface electromyography of the proximal forearm during gripping tasks. J Electromyogr Kinesiol. 2003;13:63–71.
   PubMed Web of Science ®Google Scholar
• Fougner A, Scheme E, Chan ADC, Englehart K, Stavdahl Ø. A multi-modal approach for hand motion classification using surface EMG and accelerometers. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:4247–50.
   PubMedGoogle Scholar
• Roy SH, Cheng MS, Chang S-S, Moore J, De Luca G, Nawab SH, et al. A combined sEMG and accelerometer system for monitoring functional activity in stroke. IEEE Trans Neural Syst Rehabil Eng. 2009;17:585–94.
   PubMed Web of Science ®Google Scholar
• Li Y, Chen X, Zhang X, Wang K, Yang J. Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:3358–61.
   PubMedGoogle Scholar
• Bassett DR Jr, Ainsworth BE, Swartz AM, Strath SJ, O’Brien WL, King GA. Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc. 2000;32(9 Suppl):S471–480.
   PubMed Web of Science ®Google Scholar
• De Monsabert BG, Rossi J, Berton E, Vigouroux L. Comparison of muscle loadings between power and pinch grip tasks. Comput Meth Biomech Biomed Engin. 2012;15 Suppl 1:159–61.
   PubMedGoogle Scholar
• Jacquier-Bret J, Rezzoug N, Vallier JM, Tournebise H, Gorce P. Reach to grasp kinematics and EMG analysis of C6 quadriplegic subjects. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:5934–7.
   PubMedGoogle Scholar
• Alderman AK, Chung KC. Measuring outcomes in hand surgery. Clin Plast Surg. 2008;35:239–50.
   PubMed Web of Science ®Google Scholar
• TOMM-J (Arthur S and L Carter). International Standards for Anthropometric Assessment. International Society for the Advancement of Kinanthropometry; 2006.
   Google Scholar
• Patterson Medical. Patterson Medical – Evaluation [Internet]. 2003 [citado 19 de octubre de 2012]. Recuperado a partir de: http://www.pattersonmedical.com/app.aspx?cmd = getProduct&key = IF_921002866
   Google Scholar
• Cuesta-Vargas AI, Gabel PC. Cross-cultural adaptation, reliability and validity of the Spanish version of the upper limb functional index. Health Qual Life Outcomes. 2013;11:126.
   PubMed Web of Science ®Google Scholar
• Hervás MT, Navarro Collado MJ, Peiró S, Rodrigo Pérez JL, López Matéu P, Martínez Tello I. [Spanish version of the DASH questionnaire. Cross-cultural adaptation, reliability, validity and responsiveness]. Med Clin (Barc). 2006; 127:441–7.
   PubMed Web of Science ®Google Scholar
• Gummesson C, Ward MM, Atroshi I. The shortened disabilities of the arm, shoulder and hand questionnaire (QuickDASH): Validity and reliability based on responses within the full-length DASH. BMC Musculoskelet Disord. 2006;7:44.
   PubMed Web of Science ®Google Scholar
• AnthroTronix. AcceleGlove [Internet]. 2009 [citado 20 de octubre de 2013]. Recuperado a partir de: http://www.acceleglove.com/
   Google Scholar
• Mega Electronics Ltd. ME6000 | Mega Electronics Ltd [Internet]. 2012 [citado 16 de mayo de 2012]. Recuperado a partir de: http://www.megaemg.com/products/biomonitor-me6000/
   Google Scholar
• Daniels L, Worthingham C. Pruebas funcionales musculares. Interamericana 1998. 189 p.
   Google Scholar
• Perotto A, Delagi EF. Anatomical guide for the electromyographer: The limbs and trunk. Springfield, IL: Charles C. Thomas Publisher; 2005. 362 p.
   Google Scholar
• Hermens HJ, Freriks B, Merletti R. SENIAM: European recommendations for surface electromyography. Enschede : Roessingh Research and Development; 1999.
   Google Scholar
• R Project. The R Project for Statistical Computing [Internet]. 2002 [citado 25 de octubre de 2012]. Recuperado a partir de: http://www.r-project.org/
   Google Scholar
• MathWorks. MathWorks España – MATLAB – El lenguaje de cálculo técnico [Internet]. 2012 [citado 15 de enero de 2013]. Recuperado a partir de: http://www.mathworks.es/products/matlab/
   Google Scholar
• IBM SPSS Statistics [Internet]. [citado 25 de octubre de 2012]. Recuperado a partir de: http://www-01.ibm.com/software/analytics/spss/products/statistics/
   Google Scholar
• Dale LM, Strain-Riggs S-R. Comparing responsiveness of the quick disabilities of the arm, shoulder, and hand and the Upper Limb Functional Index. Work. 2013;46:243–50.
   PubMedGoogle Scholar
• Martin-Martin J, Cuesta-Vargas AI. A biomechanical study of spherical grip. Springerplus. 2013;2:591.
   PubMedGoogle Scholar
• Liu X, Zhan Q. Description of the human hand grasp using graph theory. Med Eng Phys. 2013;35:1020–7.
   PubMed Web of Science ®Google Scholar
• Vignais N, Marin F. Modelling the musculoskeletal system of the hand and forearm for ergonomic applications. Comput Methods Biomech Biomed Engin. 2011;14(Suppl 1): 75–6.
   Google Scholar