The numeric entry, or 10-key, speed is a measure of one's ability to manipulate a numeric keypad. With the introduction of computers and word-processors, there has been a change in how text-entry is performed. In the past, using a typewriter, speed was measured with a stopwatch and errors were tallied by hand. With the current technology, document preparation is more about using word-processors as a composition aid, changing the meaning of error rate and how it is measured. Research performed by R. William Soukoreff and I. Scott MacKenzie, has led to a discovery of the application of a well-known algorithm. Through the use of this algorithm and accompanying analysis technique, two statistics were used, minimum string distance error rate (MSD error rate) and keystrokes per character (KSPC). The two advantages of this technique include: 1. Participants are allowed to enter text naturally, since they may commit error and correct them. 2. The identification of errors and generation of error rate statistics is easy to automate.Through analysis of keystrokes, the keystrokes of the input stream were divided into four classes: Correct (C), Incorrect Fixed (IF), Fixes (F), and Incorrect Not Fixed (INF). These key stroke classification are broken down into the following 1. The two classes Correct and Incorrect Not Fixed comprise all of the characters in transcribed text. 2. Fixes (F) keystrokes are easy to identify, and include keystrokes such as backspace, delete, cursor movements, and modifier keys. 3. Incorrect Fixed (IF) keystrokes are found in the input stream, but not the transcribed text, and are not editing keys. Using these classes, the Minimum String Distance Error Rate and the Key Strokes per Character statistics can both be calculated. The minimum string distance (MSD) is the number of "primitives" which is the number of insertions, deletions, or substitutions to transform one string into another. The following equation was found for the MSD Error Rate MSD Error Rate = (INF/(C + INF)) * 100% With the minimum string distance error, errors that are corrected do not appear in the transcribed text. The following example will show you why this is an important class of errors to consider: Presented Text: the quick brown Input Stream: the quix<-ck brown Transcribed Text: the quick brown in the above example, the incorrect character ('x') was deleted with a backspace ('<-'). Since these errors do not appear in the transcribed text, the MSD error rate is 0%. This is why there is the key strokes per character (KSPC) statistic. KSPC = (C+INF+IF+F)/(C+INF) The three shortcomings of the KSPC statistic are listed below: 1. High KSPC values can be related to either many errors which were corrected, or few errors which were not corrected, however there is no way to distinguish the two. 2. KSPC depend on the text input method, and cannot be used to meaningfully compare two different input methods, such as Qwerty-keyboard and a multi-tap input. 3. There is no obvious way to combine KSPC and MSD into an over-all error rate, even though they have an inverse relationship. Using the classes described above, further metrics were defined by R. William Soukoreff and I.Scott MacKenzie: 1. Error correction efficiency refers to the ease with which the participant performed error correction. Correction Efficiency = IF/F 2. Participant conscientiousness is the ratio of corrected errors to the total number of error, which helps distinguish perfectionists from apathetic participants. Participant Conscientiousness = IF / (IF + INF) 3. If C represents the amount of useful information transferred, INF, IF, and F represent the proportion of bandwidth wasted. Utilized Bandwidth = C / (C + INF + IF + F) Wasted Bandwidth = (INF + IF + F)/ (C + INF + IF + F) The classes described also provide an intuitive definition of total error rate: Total Error Rate = ((INF + IF)/ (C + INF + IF)) * 100% Not Corrected Error Rate = (INF/ (C + INF + IF)) * 100% Corrected Error Rate = (IF/ (C + INF + IF)) * 100% Since these three error rates are ratios, they are comparable between different devices, something that cannot be done with the KSPC statistic, which is device dependent.[18] Keystroke dynamics, or typing dynamics, is the obtaining of detailed timing information that describes exactly when each key was pressed and when it was released as a person is typing at a computer keyboard for the identification of humans by their characteristics or traits,[19] similar to speaker recognition.[20] Data needed to analyze keystroke dynamics is obtained by keystroke logging. The behavioral biometric of Keystroke Dynamics uses the manner and rhythm in which an individual types characters on a keyboard or keypad.[21]