As the user's mistakes in inputting text have a large influence on the real use of the keyboards, the occurrence of errors in text inputting is also modelled in order ...
Optimisation of the Selection Set Features for Scanning Text Input Julio Abascal, Luis Gardeazabal, and Nestor Garay Laboratory of Human-Computer Interaction for Special Needs University of the Basque Country-Euskal Herriko Unibertsitatea Manuel Lardizabal 1, E-20018 Donostia, Spain {julio.abascal,luisg,nestor}@si.ehu.es
Abstract. Scanning is one of the most popular text input methods for people with severe movement restrictions due to diverse kinds of disabilities. It is frequently used to input messages into communication systems, such as text-tovoice translators in order to maintain conversations. Nevertheless, the rate of text production usually obtained is very slow. For this reason, every effort to save time and optimize communication speed is welcome. In this way, this paper presents a study on the influence on the character input rate of diverse parameters related to the matrix that contains the selection set, such as shape, size, number of dimensions and layout of the selectable items. Its purpose is to extract a set of guidelines to design efficient input systems well adapted to the user, based on the scanning of items and its selection by means of one switch or push-button.
1 Introduction In a classical paper [11], Vanderheiden classifies input methods into three types: direct selection, scanning and codification. Direct selection uses spatial keyboards (the standard) where selectable items are spatially distributed, mapping each key with an item (character, syllable, word, image, etc.). This kind of keyboard requires a considerable amount of mobility, coordination and strength in the upper limbs required on the user’s part. Input by codification also requires coordination and strength in order to press a sequence of keys, usually in a reduced keyboard to compose the code related to each character (e.g. Morse code). However, people with diverse motor impairments that restrict their mobility may use scanning input. This input method is composed of a temporal keyboard and an activation system benefiting any voluntary residual movement in the user. Temporal keyboards sequence selectable items over the time. That is, the diverse items are sequentially presented to the user until he or she selects one of them by activating any type of switch or push-button. This input method is well described in the literature [1, 2, 3], and the most relevant concepts, such as selection set, control interface, input domain, activation method, etc., are precisely defined by Cook and Hussey [4]. In addition to physical interaction features, other authors have studied the cognitive aspects of interaction using scanning input. For instance, Mizuko et al. [9] considered the effects of selection techniques and array sizes on short-term visual memory. K. Miesenberger et al. (Eds.): ICCHP 2004, LNCS 3118, pp. 788–795, 2004. Springer-Verlag Berlin Heidelberg 2004
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Even if scanning techniques allow people to input texts, the communication rate obtained with temporal keyboards is significantly slower than the one obtained speaking or typing in spatial keyboards. Diverse researches have been conducted to enhance the input rate, producing interesting techniques, such as word prediction [5], and character disambiguation [7]. On the other hand, several authors, such as Lesher [8] and Venkatagiri [12], have shown the influence on the selection rate of various parameters, for instance, the scanning strategies or the matrix layout. In this work, these diverse parameters are jointly studied, simulated and statistically measured. Therefore, this research is aimed at studying the overall influence of the diverse parameters in order to propose clear guidelines for the design of optimal input set configurations, to minimize text input rate using a single push-button.
2 Methodology A number of parameters have been analytically described by means of equations and their influence simulated. For this purpose, we used a simulator that tries to compose sample texts using diverse input sets. This simulator calculates the theoretical number of keystrokes and the lowest time needed to write them. As the user’s mistakes in inputting text have a large influence on the real use of the keyboards, the occurrence of errors in text inputting is also modelled in order to study its influence on the input rate. 2.1 Variables The diversity of potential selection sets for each language makes an exhaustive simulation of all the possibilities impossible. In addition, small variations in the number of items that compose the selection set are not relevant for the results. For this reason, in this study we use a selection set1 composed of 36 items that include 26 characters, a blank, punctuation signs, and a number of commands, such as "erase", "enter", etc. This selection set is easily adaptable to most languages using Latin characters. The variables we can manipulate for the research are the distribution of these elements into a matrix, the number of dimensions of the matrix and its layout. Therefore, the key question is how to design the matrix containing the input set in order to allow the fastest scanning. 2.2 Measurements In order to be able to compare diverse layouts, we need to calculate the time needed to write a text using them. However, this time cannot be absolutely measured because it depends on the scanning period T that is usually adjusted to the reaction capacity of the user. For this reason, the measurements are given in number of T's.
1
Selection set is the items available from which choices are made (Lee and Thomas, 1990). Cited by Cook and Hussey in [4].
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Let us consider a selection set distributed in a D-dimensional matrix of [1..n] x [1..m] … x [1..s] elements (see Figure 1). If the system sequentially scans each dimension, any position may be numbered in order as X i, j…k To select the item in position p = X a, b…d, the user needs to wait for a time of (r-1)*T seconds, where r = a + b +…+ d. After that, this item becomes selectable for a maximum period of T seconds. The time expended by the user to select the item is called reaction time and is assumed T/2, on average. Therefore, the time elapsed to select the item in position p is: t(p) = (r - 1) T + T/2
(1)
Fig. 1. A D-dimensional matrix of [1..n] x [1..m] x… x [1..s] elements
3 First Hypothesis: All the Options Have the Same Probability In order to study the influence of the layout isolated from other influences, the first step is to consider that all the items have the same selection probability. Under this hypothesis, three cases are analysed with the input set distributed in a one-dimension matrix, a two-dimension matrix and a D-dimensions matrix. From this study, we deduced that the average access time for a D-dimensional matrix with Ni items allocated to each dimension i, is: D
t =∑ NT i =1
i
2
(2)
The selection of an item in a D-dimensions matrix requires D keystrokes. Matrixes having more than two dimensions must be split into blocks of two-dimension matrixes to be displayed correctly. Figure 2 shows, for instance, a three-dimension matrix (2x3x4) containing 24 characters. In this way, the average access time and the time needed to write a text can be calculated. For instance, using a 4x3x3 matrix for 36 selectable items, the average time is:
t
( 3 x 4 x 3)
=
3+ 4 + 3 T = 5T 2
(3)
and the time needed to produce an 80 characters text (with T= 1 s) is: 80 x 5 = 400 s
(4)
Optimisation of the Selection Set Features for Scanning Text Input Block 1 Row 1
Block 2
791
Block 3
Options
Row 2
Fig. 2. 3-dimension matrix with 2x3x4 distribution
4 Second Hypothesis: Distribution of the Items by Frequency of Use In the previous section, we considered all options equally probable. However, when natural language is used, this is not true. The frequency of each letter is different and, in addition, is heavily dependent on the language. This suggests that the most frequent items should be located in the places requiring the lowest access times. Following this simple procedure, the average access time decreases considerably. The previously studied three cases are analysed under this hypothesis, with the input set distributed in a one-dimension matrix, a two-dimension matrix or a Ddimensions matrix. For example, the average access time for a two-dimensional matrix with 36 items sorted by frequency in the Spanish language is:
t
( 6 x 6)
6
=
6
∑∑ f
i, j
× t i,j = 0.1828 × 1 + 0.1126 × 2 + 0.1028 × 3 + ... ≅ 3.15T
(5)
i =1 j =1
Using this layout, the average time needed to write 80 characters is T(6x6) (80) = 3.15 × 1 × 80 = 252 s
(6)
As a first conclusion, Table 1 shows the average access times and the average times to write 80 characters for equally probable items and 1-, 2- and 3-dimension distributions, opposed to distributions taking item probabilities into account. Results are given in T units for the average time and seconds for 80 characters. Table 1. Average access times and times to write 80 characters for 1-, 2- and 3-dimensional distributions
Equally probable Tm_a By frequencies Tm_a
1 Dim 36×1
2 Dim 6×6
3 Dim
18T(1440 s)
6T (480 s)
5T (400 s)
5,5T (440 s)
6,06T (485 s)
3,15T (252 s)
3,11T (249 s)
3,2T (256 s)
3×4×3
3×2×6
5 Modelling User Mistakes After studying key factors such as the layout and shape of the matrix, it is very convenient to study the influence of the mistakes made by the user and the way they are
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treated by the system. The aim is to model the type and frequency of errors and to use this model to determine the extra time introduced by their management. Let us suppose a scanning system that uses a D-dimension matrix. When a user makes a mistake in the selection of the kth dimension, he or she has to wait B scanning cycles in the next dimension without making any selection. Then, the system starts the scanning again by the first dimension. To start, let us define error and delay concepts in this environment. An error occurs when a selection (a keystroke) different to the one wanted by the user is made. This kind of mistake is frequent when the user is tired or dispersed and when the scanning period T is too small2. On the other hand, Delay is the time elapsed from the moment a mistake happens until the system recovers the situation before the mistake. To measure the influence of the delay e and Tdelay parameters are used. e is the rate of mistakes in relation to the number of correct selections made by a specific user, and provides an indication of the relative occurrence of errors. In addition, delay time is the number of T units needed to carry out a mistaken selection and correct it. That is, the time needed to go back from the wrong selection to the situation in which the user can start the correct selection. Tdelay = taverage_access+ tselection_abort
(7)
tselection_abort depends on the dimension where the mistake occurred, as the user has to wait B cycles in the next dimension until the system considers the wrong one cancelled. For instance, delay times for a 3-dimension matrix are as follows. If the mistake occurs in the first dimension: tdelay_1 = taverage_access_i + tabort_i =(½N1+B×N2)T
(8)
in the second dimension: tdelay_2 = taverage_access_i+taverage_access_j+ tabort_j=
(9)
= (½N1+ ½N2+B×N3)×T in the third dimension: tdelay_3 =taverage_access_i+taverage_access_j+taverage_access_k+ tabort_k = ½(N1+N2+N3)T+ tabort
(10)
Tabort depends on the position in the matrix occupied by the abort option. Let us suppose that Tabort is equal to the average access time. tdelay_3 = (N1+ N2+N3) T 2
(11)
The scanning period T is a very influencing factor. If this period is too long, the user makes an early selection and the resting time became useless. Since this extra time is included in each scanning steep, the lost time can be considerable. If T is too short, the user has not enough time to react and makes more mistakes. In both cases the time needed to compose a text considerably increases. Most systems include a parameter that allows adjusting this period to each specific user. Nevertheless, the reaction time of each user changes significantly along the day due to factors such as fatigue and attitude. A good solution is to use adaptive systems that dynamically adjust the period T to the user, maintaining a low rate of mistakes. In [7] Gardeazabal presents an adaptive system based on fuzzy logic to continuously finetune T.
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Generalizing to a D dimensions matrix, the delay time is: d −1
tdelay _ d = ∑ i =1
Ni T + B × Nd × T 2 D
tdelay _ D = 2∑ i =1
Ni T 2
(12)
with d < D, and
(13)
with d = D
and the average delay time is: D
∑t
(14)
delay _ dim_ i
taverage _ delay =
i =1
D
To summarize, Table 2 shows delay times for several layouts. Table 2. Time delays for three matrixes and three different values for B Tdelay_1 Tdelay_2 Tdelay_3 Taverage_delay Taverage_delay B=1 Taverage_delay B=2 Taverage_delay B=3
2 Dim 6×6 (3+6B)T 12T -(7,5+3B)T
3 Dim 3×4×3 (1,5+4B)T (3,5+3B)T 10T (5+2,3B)T
3 Dim 3×2×6 (1,5+2B)T (2,5+6B)T 11T (5+2,7B)T
10,5T 13,5T 16,5T
7,3T 9,7T 12T
7,7T 10,3T 13T
To avoid long delays when undesired selections are made, the selection set may contain a special item (represented here by #) to go back without having to wait until the end of the scanning cycle after a mistake. The selection matrix may contain more than one of this kind of cycle-abort item. This procedure speeds up the management of mistakes, but slows down the normal composition of messages. That is due to the fact that the selection set is enlarged by a number of extra characters that must be scanned at each cycle. Therefore, to optimize the message composition speed, the inclusion of cycle-abort items should be conditioned to the average rate of mistakes made by each user. In Figure 3, we show a 3x2x7 distribution with ‘#’ in the 4th position of the last dimension (this is what we call 3x2x7-#4 distribution, being the number after ‘#’ the position where the cycle-abort character is shown). j=1 4 5 6 7 i=1 1,5 2,5 3,5 # 5,5 6,5 7,5 k=1 2
2
3
2,5 3,5 4,5 #
6,5 7,5 8,5
2
2,5 3,5 4,5 #
6,5 7,5 8,5
3,5 4,5 5,5 #
7,5 8,5 9,5
3
3,5 4,5 5,5 #
7,5 8,5 9,5
4,5 5,5 6,5 #
8,5 9,5 10,5
Fig. 3. Access times for a distribution matrix 3x2x7 with a cycle-abort character in every 4th position (3x2x7-#4 distribution)
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In Figure 4, global average times of 3-D distributions, supposing B=2, are summarised. The distributions shown are 3x4x3, 3x2x6, 3x2x7-#4 and 3x2x7-#7. 5,5
Average time in T units
5,3
3*2*6
5,1
3*2*7 (#7)
4,9 4,7 4,5 4,3 4,1
3*2*7 (#4)
3,9 3,7 3,5 3,3 3,1
3*4*3 0
5
10
15
20
25
% Errors
Fig. 4. Global average times for 3-D distributions, depending on the user’s error rates
6 Conclusions The main contribution of this research is the study of the overall interaction of the diverse factors influencing the performance of input set configurations used in text input rate with a single push-button. These results allow for the proposal of clear guidelines for the design of optimal layouts in order to optimise the input rate. Among our conclusions, we would like to underline the following: 1. Items must be located following their frequency of use. Other dispositions, such as alphabetical order, largely increase access time. 2. The shape of the matrix (number of dimensions) is the second relevant parameter. If a 2-dimensions matrix is used, it should be square: 6x6 is much better than 9x4, for instance. 3. Among the studied layouts, the best results are offered by the ones specified as 3x4x3, 3x2x7-#4, and 3x2x7-#7. The last decision depends on the average number of user errors, his or her preferences, as well as other personal characteristics. These results for alphabetic characters can be extrapolated to other selection sets containing syllables, words, icons, etc., if their relative frequencies are known. As these results have been statistically obtained by modelling the user, it is necessary to conduct experiments with real users so as to determine the speed and accuracy that can really be obtained by users (such as the one conducted by Szeto et al. [10]). For this reason, they are being tested with real users to verify usability and accessibility issues. In these experiments aspects such as acceptability, convenience, ease of learning and use, etc., are verified.
References 1. Abascal J., Gardeazabal L.: Technology to Support Alternative and Augmentative Communication. In: Casals A. (Ed.): Technological Aids for the Disabled, Societat Catalana de Tecnologia-Institut d’Estudis Catalans, Barcelona (1998)
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2. Bain B. K., Leger D.: Switches, Control Interfaces, and Access Methods. In: Bain B. K., Leger D. (Eds.): Assistive Technology. An Interdisciplinary Approach. Churchill Livingstone (1997) 3. Barret J. and Herriotts P.: Communication and Access to Computer Technology. The Disability Information Trust, Oxford (1995) 4. Cook A. M., Hussey S. M.: Assistive Technologies: Principles and Practice. Mosby (1.995) 5. Garay N., Abascal G. J.: Intelligent Word-Prediction to Enhance Text Input Rate. In: Moore J. et al. (Eds.): Procs. of the 1.997 Int. Conf. Intel. User Interfaces, ACM (1997) 241-244 6. Gardeazabal L.: Applications of the computer technology to the enhancement of the communication rate in Augmentative and Alternative Communication Systems. Ph. D. Report (in Spanish). University of the Basque Country, San Sebastián (1999). (Published by Servicio Editorial de la UPV-EHU. Leioa (2001) 7. Lesher G. W., Moulton B. J., Higginbotham D. J.: Optimal Character Arrangements for Ambiguous Keyboards. IEEE Trans. on Rehab. Eng., vol. 6, no. 4 (1998) 415-23 8. Lesher G. W. et al.: Techniques for augmenting scanning communication. Augmentative and Alternative Communication, vol. 14 (1998) 81-101 9. Mizuko M., Reichle J., Ratcliff A., Esser J.: Effects of Selection Techniques and Array Sizes on Short-Term Visual Memory. AAC, vol. 10, no. 2 (1994) 237-244 10. Szeto A.Y.J., Allen E.J., Littrell M.C.: Comparison of Speed and Accuracy for Selected Electronic Communication Devices and Input Methods. AAC, vol. 9, no. 4 (1993) 229-242. 11. Vanderheiden G. C.: Augmentative Modes of Communication for the Severely Speech- and Motor-Impaired. Clinical Orthopaedics and Related Research, no. 148 (1980) 70-86 12. Venkatagiri H. S. Efficient Keyboard Layouts for Sequential Access in Augmentative and Alternative Communication, AAC, vol. 15 (1999) 126-134
