For persons with transradial amputation, current clinical prosthetics practice uses surface electromyograms (EMGs) from the wrist flexor and extensor muscle groups to control a small number of movements. Generally, the prosthetic component is driven at a speed that is proportional to the difference in the amplitude of the two EMG signals. This form of control is referred to as “direct control.”
The most frequently implemented myoelectric prosthesis for the transradial population is a single-degree-of-freedom pre-hensor. While it is possible to control other movements (e.g., wrist pronation/supination or wrist flexion/extension), these additional degrees of freedom are often controlled in a serial fashion and require unintuitive movements of the phantom limb to produce both the control signals and the cocontractions necessary to toggle between the different degrees of freedom. While serial control is adequate for a small number of movements, it does not allow for simultaneous control or multiple joints and quickly becomes cumbersome as more degrees of freedom need to be controlled.
Many of the muscles that were used to actuate the hand and wrist are still present after amputation and produce relatively distinct patterns of activity with different movements of the phantom limb. Therefore, rather than use the cumbersome direct control approach for multiple-degree-of-freedom devices, many researchers have attempted to recognize patterns of muscle activity associated with different movements of the phantom limb and link these patterns to movements of the prosthesis. While good offline performance of these controllers has been reported, pattern recognition systems have yet to be implemented commercially.
The typical steps of a pattern-recognition-based classifier are shown in . The raw data from the EMG channels are often collected in windows or bins. These windows then undergo some form of signal processing to extract different features from the EMG data. These features can be basic amplitude information or more complex features such as the coefficients of an autoregressive (AR) model. The features are then input into a classifier that compares the features extracted from the current data window to previously collected feature sets extracted for each of the possible movement classes. The movement class that best matches the features from the current window is then selected as the “output class.”
Schematic diagram of the typical steps of pattern-recognition-based classifiers.
It is also possible to perform postprocessing techniques such as majority voting to increase the stability and robustness of the class decision stream. Majority voting stipulates that the output of the controller is not simply the most recent class decision but the class that appears the most often in the previous n class decisions. The output of the postprocessing stage dictates which degree of freedom is to be actuated and this signal is then passed into a motor controller that drives the requisite prosthesis component.
Most previous studies have used “classification accuracy” as the metric of success for pattern-recognition-based classifiers. Classification accuracy is defined as the percentage of time that the classifier is able to correctly decipher the intended movement of the user. To maximize classification accuracy, many researchers have examined a variety of different classifiers ranging from AR filters [1
] to “evidence accumulation” methods [2
], fuzzy logic classifiers [4
], Gaussian mixture matrices [8
], hidden Markov models [9
], linear discriminant methods [8
], maximum likelihood approaches [17
], multiple-hypothesis testing methods [19
], nearest-neighbor approaches [18
], and a variety of neural-type networks [6
]. In addition, to help the classifiers better interpret the intended movement of the user, researchers have attempted to extract more complex information from the EMG signals. A variety of signal features representing both EMG amplitude and spectral content have been used and most have been shown to increase classification accuracy. Examples of these features include AR model coefficients [1
], time-domain (TD) features [8
], various frequency spectra [10
], wavelet features [10
], and cepstral coefficients [2
These previous efforts attempted to improve the classification accuracies of multifunctional prosthesis controllers using different feature sets or classifiers; however, nearly all of these efforts used surface EMG recordings. Surface electrodes are advantageous because they are relatively cheap, noninvasive, and have a large pickup area. The large pickup area may be beneficial for pattern-recognition-based classifiers because it allows the electrode to detect activity from muscles other than the muscle located directly beneath the electrode. By extracting features from the surface recordings, it is possible for a classifier to parse out the activity of the different muscles that sum together to produce the recorded surface EMG signal. Detecting activity from many muscles on one channel may increase the amount of information available to the pattern recognition system and allow it to “take advantage of the crosstalk.”
Alternatively, intramuscular EMG may have advantages over surface recordings for prosthesis control. These advantages include the ability to record focally from deep muscles, the ability to provide consistent recording sites as the user changes arm orientation or dons and doffs the prosthesis, and a reduction of crosstalk, which would allow an increase in the number of independent muscle sites for one muscle/one function control or forward dynamic models of the forearm. However, intramuscular EMG has seldom been investigated. Some early work was done with intramuscular EMG to implement direct control using implanted electrodes [40
] and the authors are aware of only two groups that have investigated intramuscular EMG for pattern-recognition-based control [42
]. Only Hargrove et al
] has compared surface and intramuscular electrodes, recording from 16 untargeted surface (US) and six targeted intramuscular (TI) channels.
As well as almost solely utilizing surface electrodes, previous studies in pattern-recognition-based multifunctional prosthesis control chose to either target the electrodes to specific muscles or use untargeted arrays of electrodes. Studies using only EMG amplitude tended to target their electrodes to specific muscles to increase signal independence [4
]. Only one amplitude—only study did not [20
]. Those studies that used additional signal features tended to use untargeted electrode arrays in what the authors perceive as an attempt to capture as much muscle activity as possible. When using additional signal features, most researchers did not target their electrodes to specific muscles [1
], but there were a few exceptions [16
]. However, the authors are not aware of any study that has attempted to directly compare the use of targeted and untargeted electrodes to determine which method is superior.
Untargeted electrodes are simpler to implement and therefore are preferable for both intramuscular and surface recordings. When considering surface EMG recordings, socket fabrication can be simplified if it is shown that EMG sites for the surface channels simply need to be arranged in an equally spaced array around the forearm instead of targeted over specific muscles. Additionally, while it may be beneficial for increasing signal independence, targeting implantable sensors to specific muscles is not a trivial task. If it can be shown that untargeted intramuscular (UI) EMGs produce similar or better classification accuracies than targeted recordings, the potential need for additional procedures (e.g., ultrasound guidance) to insert the intramuscular electrodes into specific muscles would be eliminated.
Given that relatively little work has been done to examine the effect of either electrode targeting or electrode implantation, the goals of this paper were to compare the classification accuracies of multifunctional prosthesis classifiers that used either surface or intramuscular EMG as well as those that used either targeted and untargeted electrodes. These goals were accomplished by comparing the accuracies resulting from targeted surface (TS), TI, US, and untargeted intramuscular (UI) electrode recordings.