Design and development of an automated, portable and handheld tablet personal computer-based data acquisition system for monitoring electromyography signals during rehabilitation

This article describes the design of a robust, inexpensive, easy-to-use, small, and portable online electromyography acquisition system for monitoring electromyography signals during rehabilitation. This single-channel (one-muscle) system was connected via the universal serial bus port to a programmable Windows™ operating system handheld tablet personal computer for storage and analysis of the data by the end user. The raw electromyography signals were amplified in order to convert them to an observable scale. The inherent noise of 50 Hz (Malaysia) from power lines electromagnetic interference was then eliminated using a single-hybrid IC notch filter. These signals were sampled by a signal processing module and converted into 24-bit digital data. An algorithm was developed and programmed to transmit the digital data to the computer, where it was reassembled and displayed in the computer using software. Finally, the following device was furnished with the graphical user interface to display the online muscle strength streaming signal in a handheld tablet personal computer. This battery-operated system was tested on the biceps brachii muscles of 20 healthy subjects, and the results were compared to those obtained with a commercial single-channel (one-muscle) electromyography acquisition system. The results obtained using the developed device when compared to those obtained from a commercially available physiological signal monitoring system for activities involving muscle contractions were found to be comparable (the comparison of various statistical parameters) between male and female subjects. In addition, the key advantage of this developed system over the conventional desktop personal computer-based acquisition systems is its portability due to the use of a tablet personal computer in which the results are accessible graphically as well as stored in text (comma-separated value) form.

[1]  Mohamad Parnianpour,et al.  EMG activity normalization for trunk muscles in subjects with and without back pain. , 2002, Medicine and science in sports and exercise.

[2]  H J Hermens,et al.  Evaluating the effect of electrode location on surface EMG amplitude of the m. erector spinae p. longissimus dorsi. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[3]  Amod Kumar,et al.  An innovative device for instant measurement of Surface Electro-Myography for clinical use , 2012 .

[4]  Luciano Boquete,et al.  A portable wireless biometric multi-channel system , 2012 .

[5]  F. Mohd-Yasin,et al.  Techniques of EMG signal analysis: detection, processing, classification and applications , 2006, Biological Procedures Online.

[6]  Yasunobu Handa,et al.  A VERSATILE LABVIEW-BASED TOOLBOX DESIGN AND MAN-MACHINE INTERFACE FOR THE ELECTRICAL STIMULATION SYSTEM , 2006 .

[7]  B. Freriks,et al.  Development of recommendations for SEMG sensors and sensor placement procedures. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[8]  Roberto Merletti,et al.  Electromyography. Physiology, engineering and non invasive applications , 2005 .

[9]  G. Wulf,et al.  Increased jump height and reduced EMG activity with an external focus. , 2010, Human movement science.

[10]  Neelesh Kumar,et al.  Low cost prototype development of electronic knee , 2010 .

[11]  Rafael A. Calvo,et al.  Effect of Experimental Factors on the Recognition of Affective Mental States through Physiological Measures , 2009, Australasian Conference on Artificial Intelligence.

[12]  R. Badlishah Ahmad,et al.  Recent Survey of Automated Rehabilitation Systems Using EMG Biosensors , 2011 .

[13]  Enrique J. Gómez,et al.  Upper Limb Portable Motion Analysis System Based on Inertial Technology for Neurorehabilitation Purposes , 2010, Sensors.

[14]  L Leinonen,et al.  Interaction of Lidocaine and Hypothermia in Bier Blocks in Volunteers , 1989, Anesthesia and analgesia.

[15]  J. Mercer,et al.  SURFACE ELECTROMYOGRAPHIC ASSESSMENT OF THE EFFECT OF STATIC STRETCHING OF THE GASTROCNEMIUS ON VERTICAL JUMP PERFORMANCE , 2005, Journal of strength and conditioning research.

[16]  C D Ingersoll,et al.  Effect of knee joint effusion on quadriceps and soleus motoneuron pool excitability. , 2001, Medicine and science in sports and exercise.

[17]  Mashhour M. Bani Amer,et al.  Design of a user-friendly LabVIEW-based toolbox for real-time monitoring and diagnosis of vital signals , 2010, Int. J. Medical Eng. Informatics.

[18]  Alan Barr,et al.  Pinch force and forearm-muscle load during routine colonoscopy: a pilot study. , 2009, Gastrointestinal endoscopy.

[19]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[20]  M. Naeije,et al.  Relation between EMG power spectrum shifts and muscle fibre action potential conduction velocity changes during local muscular fatigue in man , 1982, European Journal of Applied Physiology and Occupational Physiology.

[21]  Ondrej Krejcar,et al.  Solutions of hardware platform for Biotelemetric Systems , 2010, 2010 International Conference on Applied Electronics.

[22]  D. E. Geister,et al.  Computerized data acquisition and analysis for real-time electromyography in clinical dentistry , 1975, Proceedings of the IEEE.

[23]  Maria Grazia Benedetti,et al.  Clinician's view: dynamic EMG , 2001 .

[24]  J. Perry,et al.  Relationship between wire EMG activity, muscle length, and torque of the hamstrings. , 2002, Clinical biomechanics.

[25]  Kanav Kahol,et al.  The impact on musculoskeletal system during multitouch tablet interactions , 2011, CHI.

[26]  M Oehler,et al.  A multichannel portable ECG system with capacitive sensors , 2008, Physiological measurement.

[27]  B.C. Choi,et al.  Biosignal monitoring system for mobile telemedicine , 2005, Proceedings of 7th International Workshop on Enterprise networking and Computing in Healthcare Industry, 2005. HEALTHCOM 2005..

[28]  J. Pauly,et al.  An electromyographic study of some muscles crossing the elbow joint , 1967, The Anatomical record.

[29]  J P Clarys,et al.  Electromyography in sports and occupational settings: an update of its limits and possibilities , 2000, Ergonomics.

[30]  .. S. Day Important Factors in surface EMG measurement By Dr , 2002 .

[31]  J. Webster Encyclopedia of Medical Devices and Instrumentation , 1988 .

[32]  Jongsang Son,et al.  Development and Assessment of an EMG-Based Exoskeleton System , 2010 .

[33]  David Cuesta-Frau,et al.  Description of a Portable Wireless Device for High-Frequency Body Temperature Acquisition and Analysis , 2009, Sensors.

[34]  Adrian Burns,et al.  SHIMMER™ – A Wireless Sensor Platform for Noninvasive Biomedical Research , 2010, IEEE Sensors Journal.

[35]  P V Komi,et al.  Signal characteristics of EMG at different levels of muscle tension. , 1976, Acta physiologica Scandinavica.

[36]  Zhu Qiang,et al.  A Wireless PDA-based Electrocardiogram Transmission System for Telemedicine , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[37]  Carlo J. De Luca,et al.  The Use of Surface Electromyography in Biomechanics , 1997 .

[38]  Christian Ritz,et al.  Multimedia user feedback based on augmenting user tags with EEG emotional states , 2011, 2011 Third International Workshop on Quality of Multimedia Experience.

[39]  Nikos Dimitropoulos,et al.  PDA-BASED TELERADIOLOGY SYSTEM WITH REAL-TIME VOICE CONFERENCING CAPABILITIES , 2006 .

[40]  Cheryl L. Willis,et al.  Tablet PC's as instructional tools or the pen is mightier than the 'board! , 2004, CITC5 '04.

[41]  Mario Ignacio Chacon Murguia,et al.  EMG Hand Burst Activity Detection Study Based on Hard and Soft Thresholding , 2009, Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition.

[42]  Deborah S. Won,et al.  An EMG-based system for continuous monitoring of clinical efficacy of Parkinson's disease treatments , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.