Determining delay created by multifunctional prosthesis controllers.

Todd R. Farrell, PhD I am writing to express a concern that my colleagues from previous laboratories at Northwestern University/Rehabilitation Institute of Chicago (RIC) and I have shared for a number of years related to multifunctional prosthesis control. Previous investigations have used a variety of analysis window attributes for their multifunctional prosthesis controllers [1–8]. Researchers have varied the length of the analysis window, the amount of overlap between consecutive windows, and the number of majority votes used in the postprocessing of the classifier decisions. However, we believe that many researchers have made decisions about these attributes with little regard for the overall delay created in the real-time system. (Note that the term classifier typically refers to an element of the controller that uses the inputs provided to it, e.g., electromyographic [EMG] signals, force sensor data, and position sensor data to decide which joint(s) of the prosthesis should be actuated.) For example, Peleg et al. performed classification decisions on data up to 1.4 seconds after the onset of the contraction [9]. This classifier would require its user to wait on the order of seconds for Peleg et al.’s prosthesis to respond, which would likely be quite frustrating for the user. We are not suggesting that new algorithms should not be explored simply because they may create substantial delays. However, we do believe that these delays should be considered and discussed in each article that is published on this topic. While a particular classifier may create a 1 percent increase in classification accuracy, if it cannot add this increase in accuracy in a reasonable amount of time, it may be a “nonstarter.” Continuing in this vein, we would like to discuss some findings that we believe will allow prosthesis controller designers to better understand how their controllers will behave in real time. (For the purpose of this editorial, further use of “we” and “our” indicates my colleagues and me.) This editorial focuses on analysis window issues from the perspective of EMG-based multifunctional prosthesis control. However, the work described can be extended to other forms of window-based biosignal classifiers, such as brain machine interfaces that use neural spike-counting algorithms for intracortical data [10–12] or those that use electroencephalogram recordings [13–14].