• Breaking It Down: 1972-2018

    40 Years of EMG Decomposition

    Forty years ago, a unique bio-electrode was designed to record a muscle’s individual motor unit firings, forever changing the course of neuromuscular research.  Since then, improvements to technology have given researchers worldwide a window into motor control, and a chance to solve the mysteries of human movement.

     

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  • Prof. Carlo J De Luca,

    founder of Delsys Inc. and inventor of the aforementioned bio-electrode, aimed to reveal the connection between the muscles and the nervous system through advanced, but human-friendly, technology.  His innovations in EMG over the years enabled those at Boston University and Delsys to continue developing better tools, including finely-tuned hardware and automated EMG decomposition algorithms (dEMG). 

    Carlo J. De Luca
  • Now as the world leader in EMG technology,

    Delsys has uses this foundation to enable researchers in over 87 different countries to dig deeper into muscle control than ever before.

     

    Here, you can explore over 40 years of dEMG innovation and beyond, as we offer you a glimpse into the past and future of neuromuscular research.

  • Introduction of quadrifilar needle electrode

    De Luca and Forrest design the very first electrode for human MUAP recording during strong contractions. Prior to this invention, detecting motor units was scarcely possible.

    1972 – Introduction of quadrifilar needle electrode
  • Conception of motor unit decomposition

    In a step to make motor unit firings more accessible, LeFever and De Luca produced semi-automated algorithms to decompose a full EMG signal.

    1982 – Conception of motor unit decomposition
  • First breakthroughs in understanding the control properties of motor units

    The new EMG decomposition algorithms streamlined signal processing and allowed for unprecedented studies of motor unit control properties.

    1982 – First breakthroughs in understanding the control properties of motor units
  • Algorithm improvements & new accuracy tests

    As all good science needs evidence to back it up, Mambrito and De Luca sought out to validate their decomposition techniques and improve accuracy in the readings.  Improved performance and the introduction of a two-source test for accuracy proved vital towards decomposition’s acceptance among scientists.

    Decomposition improvements and two-source test for accuracy
  • Decomposition algorithm improvements and breakthroughs in motor control continue

    The quest for algorithm improvement continued over the next two decades, as fully automatic decomposition was introduced.  At the same time, major studies were published showing the power of EMG decomposition, as users investigated the effects of aging, fatigue, and stroke on motor unit behavior.

    1985-2006 – Algorithm improvements and breakthroughs in motor control continue
  • First non-invasive system for decomposition of surface EMG signals

    A major milestone was achieved in 2006, as the first system for decomposing surface EMG signals was described.  The system combined a new 4-channel surface array sensor with improved decomposition algorithms, opening up the doors even further for scientists in a slew of fields to use EMG decomposition.

    2006 – First non-invasive system for decomposition of surface EMG signals
  • High-yield decomposition of surface EMG signals

    The evolution of decomposition eventually brought about fully automated high-yield decomposition (20-30+ motor units).  A new “Decompose, Synthesize, Decompose, Compare” validation test was introduced, improving accuracy to an average of 92.5%.

    2010 – High-yield decomposition of surface EMG signals and DSDC test for accuracy
  • Error reduction methods for motor unit decomposition

    The new automated decomposition algorithms brought new opportunities for reducing error and increasing yield, so in 2014 Kline and De Luca introduced error reduction algorithms to reduce inherent errors that come with sEMG.

    2014 – Error reduction methods for motor unit decomposition
  • Decomposition of surface EMG signals from cyclic dynamic contractions

    Another breakthrough was recently achieved in 2015, as De Luca et al decomposed cyclic dynamic contractions, marking the first time decomposition was successfully achieved from a dynamic sEMG signal.

    2015 – Decomposition of surface EMG signals from cyclic dynamic contractions

  • The Future of dEMG

    The key to improving the health of the aging, the injured, or those with movement disorders lies within this neuromuscular connection.

    Now more than ever, this information is right at our fingertips; if we can understand and control it, we can improve the lives of millions worldwide. The possibilities are endless.

    To support innovative researchers and clinicians, Delsys is committed to bringing innovative technology to the field.

     

    The next release of the dEMG system features:

     

    • Robust 40 meter wireless range
    • Isometric + Dynamic contractions
    • Compatible with existing Trigno™ Wireless Systems
    • Fully automatic EMG decomposition
    • Advanced artificial-intelligence algorithms
    • Powerful motor unit analysis tools