Measuring AAC System Selection Rate
Katya J. Hill*, Pushpa Ramachandran*, Barry Romich**
*Edinboro University of Pennsylvania
**Prentke Romich Company and University of Pittsburgh
People who rely on AAC value communication rate highly as a trait they desire in an AAC system. Their communication rate 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.
After the team has determined the LRM(s) to include in a system, 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 (Glennen and DeCoste, 1997). For most teams, determination of selection techniques (e.g., keyboard vs. head pointing) has been qualitative ratings of speed and reliability. Decisions relative to selection rate have been based on clinical intuition, trial-and-error counts, or not documented at all.
Recent work has resulted in AAC language activity monitoring (LAM) for clinical use, funded in part by the National Institute for Deafness and Other Communication Disorders of NIH (Romich and Hill, 1999; Hill and Romich, 1999). These developments have made available tools to collect language sample quantitative data on which to base assessment and intervention decisions. LAM data is being used to produce quantitative AAC performance summary measures.
The objectives of this work were to 1) demonstrate a method of extracting selection rate measurement from normal LAM data and 2) begin the establishment of indices of performance in the area of selection rate for individuals who rely on AAC and use direct selection. Selection rate can be used for the comparison of different selection techniques on systems of different array sizes, 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. (Lucky, 1989). Consistent with historical measures, for this work, selection rate is reported in terms of bits per second.
Language samples from LAM are reported in the following format:
20:37:00 "content of the language event".
The time stamp is a 24 hour format with one second resolution. For individuals who spell as a normal part of their communication, and the letters are selected with a predictable number of selections (generally one), spelled word LAM data can be used to determine selection rate. The selection rate (SR) in bits per second is defined as
SR = NS ((L) x ln(A) / ln(2)) / (E - S)
Where NS is the number of selections required per letter, L is the number of letters (including SPACE) following the first event in the spelled word, A is the number of locations in the selection array, E is the end time, and S is the start time.
The above process is applied to all spelled single words with no multiple or repeated letters and no error correction in the language sample. Considering that spelling may be interrupted or erratic, the reported selection rate is the weighted average (by L) of all calculated selection rates above the mean. The need for multiple calculations makes this potentially time consuming. However, as a feature in an automatic analysis program it provides routine selection rate measurement with no additional clinical procedure. The above procedure was applied to the LAM data of six individuals who were subjects in a controlled study on automated language activity monitoring. All subjects used a direct selection technique: either manual pointing or head stick on a keyboard or electronic head pointing. All subjects included sufficient spelled words in their language samples to allow this process to be applied. Results will be included on the poster.
AAC professionals, in order to increase the selection rate, generally will want to maximize the number of keys available to the user. However, range of motion and pointing skill put limits on what can be done in this area. Fitts' Law (Fitts, 1954) 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.
The development of selection skills requires training time. With quantitative measurement of performance, rational decisions can be made relative to level and stability of performance.
The availability of methods for measuring selection rate has implications in the areas of clinical intervention, outcomes measurement, and research. With minimal training, AAC clinicians can use this method with commonly available software. The end result of the use of these tools is the enhanced communication and higher personal achievement of people who rely on AAC.
Glennen SL & DeCoste D (1997). Handbook of Augmentative and Alternative Communication. Singular Publishing Group, Inc. San Diego. pg. 249.
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, CA. pp 19-21.
Hill, K & Romich B (1999). AAC language activity monitoring and analysis for clinical intervention and research outcomes. CSUN. Los Angeles, CA.
Romich, B, Hill, K & Spaeth, D (2001). AAC selection rate measurement: a method for clinical use based on spelling. Proceedings of the RESNA 2001 Annual Conference. Reno, NV.
Lucky RW (1989). Silicon Dreams New York, NY: St. Martin's Press.
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.
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