Barry A. Romich*, Katya J. Hill**, and Donald M. Spaeth***
* Prentke Romich Company
Wooster, OH 44691
** Edinboro University of Pennsylvania
Edinboro, PA 16444
*** University of Pittsburgh
Pittsburgh, PA 15260

Augmentative and alternative communication (AAC) rate enhancement is a significant goal for all working in this area of assistive technology. The speed of making selections from an array of choices is one of the factors that influence communication rate. Automated language activity monitoring (LAM) (1) tools allow the measurement of selection rate based on logged data generated by actual AAC system operators. A method for measuring selection rate is proposed. Implications are significant to the clinical assessment and intervention processes.

The AAC clinical processes initiate with a team commitment to work toward the goal of AAC. The team agrees to provide the supports and services that result in the most effective communication possible for the individual. Generally, effective communication is accomplished through spontaneous novel utterance generation (SNUG). For people who rely on AAC, one measure of communication effectiveness is communication rate, which is almost always far slower than natural speech. Therefore, every effort must be made to maximize communication rate for this population. Communication rate is influenced by many factors. By far the most significant factor can be the language representation method (LRM) employed for accessing core vocabulary. However, the speed of making selections also can be an important factor.

Once the team has determined the LRM(s), decisions regarding the most appropriate selection technique must be made. Research on how different AAC configurations affect speed and efficiency is needed to facilitate the clinical decision-making process for motor access (2). For most teams, determination of selection techniques has been qualitative ratings of speed and reliability. The measurement of selection rate has been based on clinical intuition, trial-and-error counts, or not documented.
The recent investigation into automated data logfiles (3) and the development of AAC language activity monitoring (LAM) for clinical use has made available quantitative data on which to base assessment and intervention decisions. The LAM data can be used to produce quantitative measures of various aspects of AAC system use, including both language and non-language parameters.

The objective of this work is to provide a method of using LAM tools to measure the selection rate in an AAC system. This information allows the comparison of different techniques, the areas of a keyboard, for example, that are best accessed, the measurement of progress in learning to use a particular technique, and changes in rate that might occur as a result of other short term (e.g., fatigue) or long term (e.g., learning curve, physical improvement or deterioration) factors.

The human interface information transfer rate has historically been measured in terms of bits per second. (The rate for able-bodied people is generally considered to be under 100 bits per second. (4)). Consistent with historical measures, for this work the selection rate also is being reported in terms of bits per second.

The size of the array (A) (e.g., number of keys on a keyboard) from which choices are being made determines the number of bits (N) that are available with each choice. A = 2(exponent N). N = ln(A) / ln(2). The number of bits (N) for various array sizes (A) is presented in this chart:


Thus, if a person were able to make one choice per second from a keyboard with 128 keys, the selection rate would be seven bits per second. It is important to make the distinction here that the selection rate does not constitute the AAC communication rate. While the selection rate influences the communication rate, other factors, such as the language representation methods employed, do as well.

Language samples from LAM are reported in the following format: 20:37:00 “content”. The time stamp is a 24 hour format with one second resolution. A space and two quotation marks follow the time stamp. The content of the language event being recorded is between the quotation marks. Each event is presented on a new line.

A selection rate measurement would consist of having an AAC operator enter a list of selections. The LAM would time stamp and record these selections. It is essential that the AAC system be configured such that each selection generates a language event, one or more characters. Most AAC systems can be programmed to provide for this.

Issues related to selection rate measurement include the number of choices selected in the test, the particular choices to be included, the order of their selection, and the size of the sample required to produce useful results. Standardizing on these items would allow the information to be most useful. Thus, the following proposal is offered.

The number of choices included in the test should be 10% of those allowed by the system, with a minimum of two. Thus, if the system selection array is based on 128 keys (8 x 16 matrix), 13 keys would be included in the test. If a pointing technique is being used, the keys would be those forming an “X” in the center of the selection area. For a row-column scanning system the test keys would be the diagonal keys starting in the upper left corner of the array. Keys are selected in an order that avoids or minimizes adjacent keys being selected in sequence when using a pointing technique. The duration of the test is at least thirty seconds but long enough for all test keys to be selected the same number of times.

The examiner starts the test with a selection that produces a start indicator in the LAM record. The LAM record then shows the start time (S) and end time (E) in seconds for the test. The selection rate (SR) in bits per second is defined as follows, where C is the number of choices made from an array of A keys: SR = (C x ln(A) / ln(2)) / (E – S). For example, if the LAM data showed that 26 selections were made (from an array (A) of 128 keys), the start time was 09:37:17 and the end time was 09:37:58, then the selection rate would be 4.4 bits per second.

AAC professionals, in order to increase the selection rate, generally will want to maximize the number of keys available to the user. However, it is clear that range of motion and pointing skill put limits on what can be done in this area. Fitts’ Law (5) offers some theoretical predictions on how quickly an individual can make choices of targets of a given size located a given distance from a starting point. Since the application of Fitts’ Law in the clinical setting would require information not generally available, the more practical approach is actual trials on keyboards of different sizes.

Selection skills often require training time for development. With quantitative measurement of performance, rational decisions can be made relative to level and stability of performance.

Consideration of error correction is not provided in the above procedure. This is because the number of selections necessary to correct an error is a function of features of the AAC system. The time used in generating errors is included in the test, but no consideration of correction time.

The AAC clinician is cautioned that other factors can influence communication rate. Some of these factors can be far more significant than the typical differences between some selection techniques. For example, motor planning can be important in developing communication speed. Also, an alphabet-based language representation method may produce only a single letter per selection while a whole word per selection may be produced by other methods. And navigating from screen to screen to access single meaning pictures can be problematic and time consuming.

The availability of methods for measuring selection rate has implications in the areas of clinical intervention, outcomes measurement, and research. The end result of the use of these tools is the enhanced communication and higher personal achievement of people who rely on AAC.

By having measurement tools and methods, additional information on the acquisition of personal AAC performance will be forthcoming. Understanding the characteristics of the learning process should impact AAC assessment approaches.

(1) Romich BA & Hill KJ (1999). A language activity monitor for AAC and writing systems: Clinical intervention, outcomes measurement, and research. Proceedings of the RESNA ’99 Annual Conference. Long Beach, California. pp 19-21.

(2) Glennen SL & DeCoste D (1997). Handbook of Augmentative and Alternative Communication. Singular Publishing Group, Inc. San Diego. pg. 249.

(3) Higginbotham DJ & Lesher GW (1999). Development of a voluntary standard format for augmentative communication device logfiles. Proceedings of the RESNA ’99 Annual Conference. Long Beach, CA. pp 25-27.

(4) Lucky RW (1989). Silicon Dreams New York, NY: St. Martin’s Press.

(5) Fitts P (1954). The Information Capacity of the Human Motor System in Controlling the Amplitude of Movements. Journal of Experimental Psychology. Vol. 47, No 6.

The National Institute for Deafness and Other Communication Disorders of NIH has provided funding to Prentke Romich Company to support the work on LAM.

Barry A. Romich, P.E.
Prentke Romich Company
1022 Heyl Road
Wooster, OH 44691
Tel: 330-262-1984 ext. 211
Fax: 330-263-4829
Email: bromich@aol.com