E.A. Clancy

Worcester Polytechnic Institute, Worcester, MA, United States

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Publications (56)67.91 Total impact

  • Kishor Koirala, Meera Dasog, Pu Liu, Edward Clancy
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    ABSTRACT: Processed (i.e., rectified, smoothed) electromyogram (EMG) activity from skeletal muscles precedes mechanical tension by 50-100 ms. This property can be exploited to anticipate muscle mechanical activity. Thus, we investigated the ability of surface EMG to estimate joint torque at future times, up to 750 ms. EMG recorded from the biceps and triceps muscles of 54 subjects during constant-posture, force-varying contractions was related to elbow torque. Higher-order FIR models, combined with advanced EMG processing (whitening; four EMG channels per muscle), provided a nearly identical minimum error of 5.48 ± 2.21% MVCF (flexion maximum voluntary contraction) over the time advance range of 0-60 ms. Error grew for larger time advances. The more common method of filtering EMG amplitude with a Butterworth filter (2nd-order, 1.5 Hz cutoff frequency) produced a statistically inferior (p<10-6) minimum torque error of 6.90 ± 2.39% MVCF, with an error nadir at a time advance of 60 ms. Error was progressively poorer at all other time advances. Lower-order FIR models mimicked the poorer performance of the Butterworth models. The more advanced models provide lower estimation error, require no selection of an electromechanical delay term and maintain their lowest error over a substantial range of advance times.
    06/2014;
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    ABSTRACT: The reliability of clinical and scientific information provided by algorithms that automatically decompose the electromyogram (EMG) depends on the algorithms' accuracies. We used experimental and simulated data to assess the agreement and accuracy of three publicly available decomposition algorithms-EMGlab [1] (single channel data only), Fuzzy Expert [2] and Montreal [3]. Data consisted of quadrifilar needle EMGs from the tibialis anterior of 12 subjects at 10%, 20% and 50% maximum voluntary contraction (MVC); single channel needle EMGs from the biceps brachii of 10 controls and 10 patients during contractions just above threshold; and matched simulated data. Performance was assessed via agreement between pairs of algorithms for experimental data and accuracy with respect to the known decomposition for simulated data. For the quadrifilar experimental data, median agreements between the Montreal and Fuzzy Expert algorithms at 10%, 20% and 50% MVC were 95%, 86% and 64%, respectively. For the single channel control and patient data, median agreements between the three algorithm pairs were statistically similar at ~97% and ~92%, respectively. Accuracy on the simulated data exceeded this performance. Agreement/accuracy was strongly related to the Decomposability Index [3]. When agreement was high between algorithm pairs applied to simulated data, so was accuracy.
    05/2014;
  • Meera Dasog, Kishor Koirala, Pu Liu, Edward Clancy
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    ABSTRACT: Whitening the surface electromyogram (EMG) improves EMG amplitude (EMGσ) and EMG-torque estimation. Laboratory studies utilizing contraction levels up to maximum voluntary contraction (MVC) show that whitening is useful over a frequency band extending to 1000-2000 Hz. However, EMG electrode systems with such wide bandwidth are uncommon, particularly in real-time applications; and these contraction levels are also not common. Thus, we studied the influence of the frequency band over which whitening was performed vs. the resulting performance. Low-level, torque-varying contractions (average torque level of 18.5% flexion MVC) of the elbow were contrasted with medium-level 50% MVC constant-torque contractions. For each, the maximum whitening bandwidth was varied between 30-2000 Hz. The low-level contractions (which incorporate the contraction range of most daily tasks) showed that performance utilizing frequencies out to 400-500 Hz was not statistically different (p<0.01) than results out to the full available frequency (2000 Hz). For the medium-level (50% MVC) contractions, frequencies out to 800-900 Hz were statistically equivalent to the full bandwidth. These results suggest that conventional electrodes with a typical passband of ~500 Hz are appropriate when whitening data from contraction levels typically experienced in many applications. Wider bandwidths may be advantageous for strenuous activities.
    IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 10/2013; · 2.42 Impact Factor
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    ABSTRACT: Electromyogram (EMG)-torque modeling is of value to many different application areas, including ergonomics, clinical biomechanics and prosthesis control. One important aspect of EMG-torque modeling is the ability to account for the joint angle influence. This manuscript describes an experimental study which relates the biceps/triceps surface EMG of 12 subjects to elbow torque at seven joint angles (spanning 45-135°) during constant-posture, quasi-constant-torque contractions. Advanced EMG amplitude (EMGσ) estimation processors (i.e., whitened, multiple-channel) were investigated and three non-linear EMGσ-torque models were evaluated. When EMG-torque models were formed separately for each of the seven distinct joint angles, a minimum "gold standard" error of 4.23±2.2% MVCF90 resulted (i.e., error relative to maximum voluntary contraction at 90° flexion). This model structure, however, did not directly facilitate interpolation across angles. The best model which did so (i.e., parameterized the angle dependence), achieved an error of 4.17±1.7% MVCF90. Results demonstrated that advanced EMGσ processors lead to improved joint torque estimation. We also contrasted models that did vs. did not account for antagonist muscle co-contraction. Models that accounted for co-contraction estimated individual flexion muscle torques that were ∼29% higher and individual extension muscle torques that were ∼68% higher.
    Journal of electromyography and kinesiology: official journal of the International Society of Electrophysiological Kinesiology 08/2013; · 2.00 Impact Factor
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    ABSTRACT: Stroke affects 750,000 people annually, and 80% of stroke survivors are left with weakened limbs and hands. Repetitive hand movement is often used as a rehabilitation technique in order to regain hand movement and strength. In order to facilitate this rehabilitation, a robotic glove was designed to aid in the movement and coordination of gripping exercises. This glove utilizes a cable system to open and close a patients hand. The cables are actuated by servomotors, mounted in a backpack weighing 13.2lbs including battery power sources. The glove can be controlled in terms of finger position and grip force through switch interface, software program, or surface myoelectric (sEMG) signal. The primary control modes of the system provide: active assistance, active resistance and a preprogrammed mode. This project developed a working prototype of the rehabilitative robotic glove which actuates the fingers over a full range of motion across one degree-of-freedom, and is capable of generating a maximum 15N grip force.
    IEEE ... International Conference on Rehabilitation Robotics : [proceedings]. 06/2013; 2013:1-7.
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    ABSTRACT: Time and frequency domain features of the surface electromyogram (EMG) signal acquired from multiple channels have frequently been investigated for use in controlling upper-limb prostheses. A common control method is EMG-based motion classification. We propose the use of EMG signal whitening as a preprocessing step in EMG-based motion classification. Whitening decorrelates the EMG signal, and has been shown to be advantageous in other EMG applications including EMG amplitude estimation and EMG-force processing. In a study of ten intact subjects and five amputees with up to 11 motion classes and ten electrode channels, we found that the coefficient of variation of time domain features (mean absolute value, average signal length and normalized zero crossing rate) was significantly reduced due to whitening. When using these features along with autoregressive power spectrum coefficients, whitening added approximately five percentage points to classification accuracy when small window lengths (<100 ms) were considered.
    IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 03/2013; · 2.42 Impact Factor
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    ABSTRACT: The time delay between two surface electromyograms (EMGs) acquired along the conduction path is used to estimate mean action potential conduction velocity. Modeling the linear impulse response between "upstream" and "downstream" EMG signals permits an estimate of the distribution of velocities, providing more information. In this work, we analyzed EMG from bipolar electrodes placed on the tibialis anterior of 36 subjects, using an inter-electrode distance of 10 mm. Regularized least squares was used to fit the coefficients of a finite impulse response model. We trained the model on one recording, then tested on two others. The optimum correlation between the model-predicted and actual EMG averaged 0.70. We also compared estimation of the mean conduction delay from the peak time of the impulse response to the "gold standard" peak time of the cross-correlation between the upstream and downstream EMG signals. Optimal models differed from the gold standard by 0.02 ms, on average. Model performance was influenced by the regularization parameters. The impulse responses, however, incorrectly contained substantive power at very low time delays, causing delay distribution estimates to exhibit high probabilities at very short conduction delays. Unrealistic distribution estimates resulted. Larger inter-electrode spacing may be required to alleviate this limitation.
    Medical & Biological Engineering 02/2013; · 1.76 Impact Factor
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    ABSTRACT: The reliability of automated electromyogram (EMG) decomposition algorithms is important in clinical and scientific studies. In this paper, we analyzed the performance of two multi-channel decomposition algorithms -- Montreal and Fuzzy Expert using both experimental and simulated data. Comparison data consisted of quadrifiler needle EMG from the tibialis anterior muscle of 12 subjects (young and elderly) at three contraction levels (10, 20 and 50% MVC), and matched simulation data. Performance was assessed via agreement between the two algorithms for experimental data and accuracy with respect to the known decomposition for simulated data. For the experimental data, median agreement between the Montreal and Fuzzy Expert algorithms at 10, 20 and 50% MVC was 95.7, 86.4 and 64.8%, respectively. For the simulation data, median accuracy was 99.8%, 100% and 95.9% for Montreal, and 100%, 98% and 93.5% for Fuzzy Expert at the different contraction levels.
    Bioengineering Conference (NEBEC), 2013 39th Annual Northeast; 01/2013
  • K. Koirala, M. Dasog, Pu Liu, E.A. Clancy
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    ABSTRACT: This paper investigates the ability of surface electromyogram (EMG) to estimate joint torque at future times, up to 1 s. EMG was recorded from the biceps and triceps muscles of 54 subjects during constant-posture, force-varying contractions and related to the torque produced about the elbow. EMG to joint torque was predicted up to 80 ms into the future without any changes in the minimum least square error of 5.48% of maximum voluntary contraction for the best estimation model investigated: whitened, multiple-channel EMG used with a nonlinear model. Error progressively increased for prediction times above 80 ms.
    Bioengineering Conference (NEBEC), 2013 39th Annual Northeast; 01/2013
  • M. Dasog, K. Koirala, Pu Liu, E.A. Clancy
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    ABSTRACT: It has been demonstrated that whitening the surface electromyogram (EMG) improves EMG amplitude (EMGσ) estimation. But, due to the wide bandwidth ranges often used when whitening, custom high-cost electrodes (bandwidth of ~2000 Hz) have been used. This paper investigates the effect of limiting the bandwidth for the whitened EMG data. The change in the average error of EMG to torque estimation was observed for 54 subjects over different whitening bandwidths ranging from 20-2000 Hz. We found that the average error remained the same for bandwidth limits between 600 Hz to 2000 Hz, suggesting that wider EMG electrodes were not helpful with this data set.
    Bioengineering Conference (NEBEC), 2013 39th Annual Northeast; 01/2013
  • Lukai Liu, E.A. Clancy, P. Bonato
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    ABSTRACT: The discharge rate of motor unit action potential sequences has been related to fatigue and neuromuscular diseases, but typically simple methods are used to do so. We adapted more advanced methods used to calculate heart rate to fit in the context of surface electromyogram discharge rate calculation. Simulation results with a deterministic discharge rate modulation model suggest that parameter fine-tuning is necessary to accurately and robustly estimate discharge rate.
    Bioengineering Conference (NEBEC), 2013 39th Annual Northeast; 01/2013
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    ABSTRACT: The electromyogram (EMG) signal has been used as the command input to myoelectric prostheses. A common control scheme is based on classifying the EMG signals from multiple electrodes into one of several distinct classes of user intent/function. In this work, we investigated the use of EMG whitening as a preprocessing step to EMG pattern recognition. Whitening is known to decorrelate the EMG signal, with improved performance shown in the related applications of EMG amplitude estimation and EMG-torque processing. We reanalyzed the EMG signals recorded from 10 electrodes placed circumferentially around the forearm of 10 intact subjects and 5 amputees. The coefficient of variation of two time-domain features-mean absolute value and signal length-was significantly reduced after whitening. Pre-whitened classification models using these features, along with autoregressive power spectrum coefficients, added approximately five percentage points to their classification accuracy. Improvement was best using smaller window durations (<100 ms).
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:2627-30.
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    ABSTRACT: Mean electromyogram (EMG) conduction delay is often estimated as the average time delay between two surface EMG recordings arranged along the conduction path. It has previously been shown that the complete distribution of conduction delays can be estimated from the impulse response relating the "upstream" EMG recording to the "downstream" recording. In this work, we examined regularized least squares methods for estimating the impulse response, namely the pseudo-inverse with small singular values discarded and post hoc lowpass filtering. Performance was evaluated by training the model to one recording, then testing on others. Correlation between model-predicted EMG and measured EMG was assessed for 36 subjects, using EMG recordings with 5 mm inter-electrode spacing. The best correlation was 0.86, on average, for both regularization methods. We additionally compared the mean conduction delay computed from the "gold standard" cross-correlation method to the peak time of the impulse response. The best models differed by 0.01 ms, on average, for both regularization methods. Nonetheless, the impulse responses exhibited excessive energy near zero time, causing delay distribution estimates to exhibit high probabilities at unphysiological short time delays. Inter-electrode spacing larger than 5 mm may be required to alleviate this limitation.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:3468-71.
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    John Meklenburg, Edward A Clancy, Radouil Tzekov
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    ABSTRACT: Oscillatory potentials (OPs) are typically isolated from the electroretinogram (ERG) via linear, time-invariant, bandpass filtering. The use of a highpass cutoff frequency that is too low results in a- and b-wave contamination of the OP signal, while a cutoff frequency that is too high removes significant OP signal energy. Two methods for automated highpass cutoff frequency estimation were developed and evaluated. An OP amplitude analysis method exploited a trend in variation of maximum OP amplitude with cutoff frequency. A second method fit a time-varying exponential model to the rising edge of the b-wave and selected a cutoff frequency based on minimizing the error between the residual ERG signal (the signal formed by subtracting the OP signal from the original ERG signal) and the exponential fit. The performance of each method was evaluated at 11 luminances (0.001-100 scot cd·s/m(2)) in ten wild-type adult mice by comparing the automated selections to expert-selected highpass cutoff frequencies. It was noted that cutoff frequency selection was not critical at the lower luminance levels, but strongly influenced the OP signal shape for higher luminances. At the highest luminance, errors between the OP amplitude and exponential model versus expert selection were -6.3 ± 13 and -8.2 ± 7.3 Hz, respectively. ANOVAs showed that estimations made by the OP amplitude analysis method were generally statistically indistinguishable from the expert identifications. Furthermore, both OP amplitude analysis and exponential fitting error analysis provided excellent fits to the manual selections for the four highest stimulus luminance values.
    Documenta Ophthalmologica 07/2012; 125(2):101-11. · 1.54 Impact Factor
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    ABSTRACT: The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMGσ outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 ± 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 × 10 (-3) on 52 s of four-channel whitened EMG data. Similar performance (4.67 ± 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1.
    IEEE transactions on bio-medical engineering 01/2012; 59(1):205-12. · 2.15 Impact Factor
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    Lukai Liu, Pu Liu, D.V. Moyer, E.A. Clancy
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    ABSTRACT: The surface electromyogram (EMG) from biceps/triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/non-linear model structures and system identification techniques. EMG-torque performance was improved by: advanced (i.e., whitened, multiple-channel) EMGσ processors; longer duration training sets (52 s vs. 26 s); and determination of model parameters via the use of the pseudo-inverse and ridge regression methods. Best performance provided an error of 4.65% maximum voluntary contraction (MVC) flexion.
    Bioengineering Conference (NEBEC), 2011 IEEE 37th Annual Northeast; 05/2011
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    ABSTRACT: We provide a preliminary report on work to relate the EMG activity from forearm flexors and extensors to the flexion-extension forces generated at the finger tips during constant-posture, slowly force-varying contractions. EMG electrode arrays (up to 64 channels) were applied over the flexor and, separately, extensor musculature of the forearm. Spatial filters were used to create derived EMG channels that were then related to finger tip force (via least squares models). Preliminary results identify the “pinky” finger as having the most independent EMG-force control, with moderate control available from some combinations of the other fingers.
    Bioengineering Conference (NEBEC), 2011 IEEE 37th Annual Northeast; 05/2011
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    J. Meklenburg, E.A. Clancy, R. Tzekov
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    ABSTRACT: The oscillatory potentials (OPs) of the electroretinogram (ERG) have long been used to study the progression of a number of retinal disorders. The OP parameters used in these studies are difficult to evaluate directly from the unconditioned ERG signal. Automated OP detection software was developed in MATLAB to extract the OP wavelets, identify peaks, and extract parameters of interest including the implicit time and amplitude of each OP along with the noise-adjusted RMS value of the entire OP signal. The cutoff frequency for OP filtering was determined adaptively for each record based on the characteristics of the Discrete Fourier Transform of the ERG. Preliminary testing of the software was conducted using mouse data.
    Bioengineering Conference (NEBEC), 2011 IEEE 37th Annual Northeast; 05/2011
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    ABSTRACT: This paper describes an experimental study which relates the simultaneous biceps/triceps surface electromyogram (EMG) of 12 subjects to elbow torque at seven joint angles during constant-posture, quasi-constant-torque contractions. Advanced EMG amplitude (EMGσ) estimation processors were investigated, and an EMG-torque model considering agonist and antagonist co-contractions was evaluated at each joint angle. Preliminary results show that advanced (i.e., whitened, multiple- channel) EMGσ processors lead to improved joint torque estimation and that the EMGσ torque relationship may only change by a scaling factor as a function of joint angle. I. INTRODUCTION A significant literature has developed around the problem of relating the surface EMG to muscle tensions and joint torque. However, most investigators have not accounted for muscle co-contractions by assuming that an agonist muscle can be contracted while the antagonist muscle is inhibited (1), (2). Also, there are clear advances in EMGσ processing techniques over the last few years (3), yet little have been incorporated into EMG-torque estimation. The present study investigated the EMG-torque problem by modeling agonist- antagonist co-contractions over a wide range of joint torques at seven different angles, and also applied advanced EMGσ processing techniques (whitening, multiple-channel combination).
    01/2011;
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    Lukai Liu, Pu Liu, Edward A. Clancy, Kevin B. Englehart
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    ABSTRACT: The surface electromyogram (EMG) signal collected from multiple channels has frequently been investigated for use in controlling upper-limb prostheses. One common control method is EMG-based motion classification. Time and frequency features derived from the EMG have been investigated. We propose the use of EMG signal whitening as a preprocessing step in EMG-based motion classification. Whitening decorrelates the EMG signal, and has been shown to be advantageous in other EMG applications. In a ten-subject study of up to 11 motion classes and ten electrode channels, we found that whitening improved classification accuracy by approximately 5% when small window length durations (
    01/2011;

Publication Stats

790 Citations
67.91 Total Impact Points

Institutions

  • 2000–2013
    • Worcester Polytechnic Institute
      • • Department of Electrical and Computer Engineering
      • • Department of Biomedical Engineering
      Worcester, MA, United States
  • 2009
    • Harvard Medical School
      • Department of Medicine
      Cambridge, MA, United States
  • 2005
    • University of New Brunswick
      Fredericton, New Brunswick, Canada
    • Spaulding Rehabilitation Hospital
      Boston, Massachusetts, United States
  • 1997–1999
    • Liberty Mutual Research Institute for Safety
      Boston, Massachusetts, United States
  • 1996–1999
    • Laval University
      • Département de Génie Mécanique
      Québec, Quebec, Canada
  • 1991–1999
    • Massachusetts Institute of Technology
      • Department of Electrical Engineering and Computer Science
      Cambridge, MA, United States