Neuromusculoskeletal Modeling Applications in Myoelectric Control for Upper-Limb Rehabilitation Devices

What are some drawbacks with the control systems in current prostheses?

The clinical standard of care for upper-limb myoelectric prostheses uses surface electrodes placed over muscles on the user’s residual limb to record electrical potentials (known as electromyograms or EMG) generated during muscle contraction. The EMG signals from these electrodes, when mapped appropriately to a motorized joint on a prosthesis, can serve as inputs to a control interface to open and close a hand with as little as one electrode.^1,2 However, the simplicity and robustness of these interfaces comes at the expense of the ability for prosthesis users to rapidly switch between different grasp patterns, as well as to perform more complex and individuated movements. Pattern recognition systems that use machine learning models to map signal features from multiple EMG electrodes to a set of grasp patterns have allowed for users to switch between a larger number of functional grasps faster and more intuitively.^3,4 These systems, however, have largely avoided clinical translation due to a need for frequent retraining to account for changes in EMG signal properties during prolonged use, as well as from an inability to predict motions not included during model training.^5,6,7

How can we improve the capabilities of myoelectric systems?

The available myoelectric control interfaces provide prosthesis users with an ability to select between several functional hand postures required for activities of daily living (ADLs), such as cylindrical grips for holding bottles and pinch grips for holding keys, among others. These postures are selected by the user one after the other and are ultimately executed in a binary open-close fashion, which relegates these systems to the functional realm of grasping. Dexterous motions, characterized by simultaneous and independent control of multiple joints, and an ability to correct motions in real-time remain outside of the current capabilities of clinical myoelectric interfaces. 

How can we use physiology to develop better control systems?

Our approach is to borrow from computational modeling techniques from various fields ranging from human motor control to motor unit physiology and musculoskeletal biomechanics,^8,9,10 to develop a myoelectric interface capable of achieving simultaneous and individuated control of fingers in a prosthetic hand. 

Using musculoskeletal simulations of a user’s amputated limb is a recent alternative to conventional control interfaces that may serve as a powerful basis to develop such an interface. However, given their mathematical complexity, these simulations generally occur offline, although recent work has introduced musculoskeletal models into real-time control paradigms.^12,13,14,15 Towards these efforts, our initial work is focused on developing novel EMG signal processing techniques that can generate a proportional control signal of minimal-latency, in contrast to traditional EMG enveloping which introduces substantial time delays while calculating moving average or RMS values. Our current work focuses on evaluating a thresholded digital representation of the surface EMG signal, known as Myopulse Modulation,^16,17 and its potential as an input signal for a control interface based on a musculoskeletal model. 

References

1. Berger, N., and C. R. Huppert. “The Use of Electrical and Mechanical Muscular Forces for the Control of an Electrical Prosthesis.” The American Journal of Occupational Therapy: Official Publication of the American Occupational Therapy Association 6, no. 3 (1952): 110–14. 
2. Dorcas, D. S., and R. N. Scott. “A Three-State Myo-Electric Control.” Medical and Biological Engineering 4, no. 4 (July 1, 1966): 367–70. https://doi.org/10.1007/BF02476154. 
3. Herberts, P., C. Almström, R. Kadefors, and P. D. Lawrence. “Hand Prosthesis Control via Myoelectric Patterns.” Acta Orthopaedica Scandinavica 44, no. 4 (1973): 389–409. https://doi.org/10.3109/17453677308989075. 
4. Hargrove, Levi J., Laura A. Miller, Kristi Turner, and Todd A. Kuiken. “Myoelectric Pattern Recognition Outperforms Direct Control for Transhumeral Amputees with Targeted Muscle Reinnervation: A Randomized Clinical Trial.” Scientific Reports 7, no. 1 (October 23, 2017): 13840. https://doi.org/10.1038/s41598-017-14386-w. 
5. Adewuyi, Adenike A., Levi J. Hargrove, and Todd A. Kuiken. “An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 24, no. 4 (April 2016): 485–94. https://doi.org/10.1109/TNSRE.2015.2424371. 
6. Kaufmann, Paul, Kevin Englehart, and Marco Platzner. “Fluctuating Emg Signals: Investigating Long-Term Effects of Pattern Matching Algorithms.” Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2010 (2010): 6357–60. https://doi.org/10.1109/IEMBS.2010.5627288. 
7. Young, Aaron J., Levi J. Hargrove, and Todd A. Kuiken. “Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Changing Interelectrode Distance and Electrode Configuration.” IEEE Transactions on Bio-Medical Engineering 59, no. 3 (March 2012): 645–52. https://doi.org/10.1109/TBME.2011.2177662. 
8. Fuglevand, A. J., D. A. Winter, and A. E. Patla. “Models of Recruitment and Rate Coding Organization in Motor-Unit Pools.” Journal of Neurophysiology 70, no. 6 (December 1993): 2470–88. https://doi.org/10.1152/jn.1993.70.6.2470. 
9. Moritz, Chet T., Benjamin K. Barry, Michael A. Pascoe, and Roger M. Enoka. “Discharge Rate Variability Influences the Variation in Force Fluctuations Across the Working Range of a Hand Muscle.” Journal of Neurophysiology 93, no. 5 (May 2005): 2449–59. https://doi.org/10.1152/jn.01122.2004. 
10. Seth, Ajay, Michael Sherman, Jeffrey A. Reinbolt, and Scott L. Delp. “OpenSim: A Musculoskeletal Modeling and Simulation Framework for in Silico Investigations and Exchange.” Procedia IUTAM, IUTAM Symposium on Human Body Dynamics, 2 (January 1, 2011): 212–32. https://doi.org/10.1016/j.piutam.2011.04.021. 
11. Bogert, Antonie J. van den, Dimitra Blana, and Dieter Heinrich. “Implicit Methods for Efficient Musculoskeletal Simulation and Optimal Control.” Procedia IUTAM, IUTAM Symposium on Human Body Dynamics, 2 (January 1, 2011): 297–316. https://doi.org/10.1016/j.piutam.2011.04.027. 
12. Bogert, Antonie J. van den, Dimitra Blana, and Dieter Heinrich. “Implicit Methods for Efficient Musculoskeletal Simulation and Optimal Control.” Procedia IUTAM, IUTAM Symposium on Human Body Dynamics, 2 (January 1, 2011): 297–316. https://doi.org/10.1016/j.piutam.2011.04.027. 
13. Blana, Dimitra, Antonie J. Van Den Bogert, Wendy M. Murray, Amartya Ganguly, Agamemnon Krasoulis, Kianoush Nazarpour, and Edward K. Chadwick. “Model-Based Control of Individual Finger Movements for Prosthetic Hand Function.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 3 (March 2020): 612–20. https://doi.org/10.1109/TNSRE.2020.2967901. 
14. Blana, Dimitra, Edward K. Chadwick, Antonie J. van den Bogert, and Wendy M. Murray. “Real-Time Simulation of Hand Motion for Prosthesis Control.” Computer Methods in Biomechanics and Biomedical Engineering 20, no. 5 (April 4, 2017): 540–49. https://doi.org/10.1080/10255842.2016.1255943. 
15. Chadwick, Edward K., Dimitra Blana, Robert F. Kirsch, and Antonie J. van den Bogert. “Real-Time Simulation of Three-Dimensional Shoulder Girdle and Arm Dynamics.” IEEE Transactions on Biomedical Engineering 61, no. 7 (July 2014): 1947–56. https://doi.org/10.1109/TBME.2014.2309727. 
16. Weir, RF ff, Jonathon W Sensinger, and M Kutz. “Design of Artificial Arms and Hands for Prosthetic Applications.” Biomedical Engineering and Design Handbook 2 (2009): 537–98. 
17. Childress, DS, DW Holmes, and JN Billock. “Ideas on Myoelectric Prosthetic Systems for Upper-Extremity Amputees.” The Control of Upper-Extremity Prostheses and Orthoses, 1974, 86–106.