Rohan Sagar
SFSU - English 670
October 22, 2017
Critical Review (Draft 2)
Instructor: Sofia de Almeida
Real-time control scheme for multifunction myoelectric control - A Critical Review
Englehart, K., & Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50(7), 848-854.
It is a bitter truth that accidents keep happening everyday and some of them even lead to people losing their limbs. Hand amputations in particular can drastically affect the victim’s capabilities. There is a wide variety of prosthetics available in the market for victims with such disabilities. However, most of the people prefer a cosmetic alternative over a functional robotic prosthetic. This is because the robotic prosthetics that are available in the market today are either too expensive or too complicated to use. In order to make them inexpensive and intuitive to the user a technique called Myo-electric Control can be used.
Electromyography (EMG) is a technique used for evaluating and recording the electrical activity of skeletal muscles. The device used to obtain the EMG signal is called electromyograph. Whenever a muscle is contracted, electrical activity occurs in the muscle, which can then be recorded by using the electromyograph. Myo-Electric control utilizes electromyography to extract the data from muscles and uses it to control the desired object.
In this paper Englehart and Hudgins (2003) talk about the basic necessities for a functional robotic prosthetic, such as accuracy, interface and response time. While talking about the history of myo-electric robotic prosthetic control, Englehart and Hudgins say that extensive research was done in the 1970s. However, the technology that existed then didn’t support the complex computational requirements of pattern recognition algorithms. The advancement in semiconductor technologies in the current day makes it possible to perform such calculations. They talk about a technique that can be used to extract the intent of the user, known as pattern recognition, using a sliding window technique (taking a small window of values and performing pattern recognition continuously instead of waiting to collect huge amounts of data and process it all at once). In this paper they explain this technique in detail. Inspite of the techniques mentioned in this paper being efficient and robust, the current day research in myo-electric control has evolved to a degree where sensors such as accelerometer are being used to complement the myoelectric data.
Englehart and Hudgins talk about the experiment that they conducted on a workstation that has a 1 GigaHertz (1,000,000,000 cycles per second) Pentium 3 processor that implemented the above mentioned technique using a software called MATLAB. They found that the system took roughly 16ms (milli seconds) to process the information using the sliding window technique.
In this paper, the authors give key information on how the Electrographic data can be analyzed to facilitate pattern recognition and myo-electric control. The technique mentioned in this paper is efficient in terms of processor resource utilization and facilitates for a lesser response time(the time taken for the system to respond after it is given the data to process). This paper serves as a guide on how to frame data acquisition and analysis algorithms efficiently.
However, recent research has proven that the use of more degrees of freedom (more sensors) can yield better accuracy. For example in the paper “ MyoHMI: A low-cost and flexible platform for developing real-time human machine interface for myoelectric controlled applications. In Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on (pp. 004495-004500). IEEE.” Donovanet al.(2016) talk about how they used an accelerometer and a gyroscope in addition to myoelectric sensors to achieve higher accuracy.
Even though Englehart and Hudgins’ paper presents a highly accurate method for analyzing myoelectric data, current research in this domain has evolved beyond using only myoelectric data to using more degrees of freedom to complement the myoelectric data. Adding more degrees of freedom such as accelerometer and gyroscope to the methods mentioned in this paper would make it more relevant to the current day research in myoelectric control.
References:
1)Englehart, K., & Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50(7), 848-854.
2) Donovan, I., Valenzuela, K., Ortiz, A., Dusheyko, S., Jiang, H., Okada, K., & Zhang, X. (2016, October). MyoHMI: A low-cost and flexible platform for developing real-time human machine interface for myoelectric controlled applications. In Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on (pp. 004495-004500). IEEE.