Optimizing Assisted Communication Devices for Children With Motor Impairments Using a Model of Information Rate and Channel Capacity 陳泳宏

Reference Terence D. Sanger and Juliet Henderson “ Optimizing Assisted Communication Devices for Children With Motor Impairments Using a Model of Information Rate and Channel Capacity ” IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 15, NO. 3, SEPTEMBER 2007

Terence D. Sanger and Juliet Henderson “ Optimizing Assisted Communication Devices for Children With Motor Impairments Using a Model of Information Rate and Channel Capacity ” IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 15, NO. 3, SEPTEMBER 2007

Outline Introduction Method Results Discussion Conclusion

Introduction

Method

Results

Discussion

Conclusion

Introduction When a child is unable to communicate, he or she loses one of the most basic aspects of human function. Some children with speech impairments are able to communicate through the use of assistive devices. Their communication abilities may be limited by the deficits in the skilled use of their limbs.

When a child is unable to communicate, he or she loses one of the most basic aspects of human function.

Some children with speech impairments are able to communicate through the use of assistive devices.

Their communication abilities may be limited by the deficits in the skilled use of their limbs.

Augmentative and Assistive Communication (AAC) Devices Recent advances in computer technology have resulted in AAC devices with flexible interfaces that can in principle be adapted to the clinical differences between different children’s communication capabilities. Such devices can be used for typing, and in many cases for voice output (VOCAs).

Recent advances in computer technology have resulted in AAC devices with flexible interfaces that can in principle be adapted to the clinical differences between different children’s communication capabilities.

Such devices can be used for typing, and in many cases for voice output (VOCAs).

State of the Art Professionals choose and program interface devices for children based on experience, clinical interviews, and trial-and-error. The choice of physical characteristics of the button interface is usually determined by the smallest button that the child can press with reasonable accuracy, without regard to measurement of speed of movement.

Professionals choose and program interface devices for children based on experience, clinical interviews, and trial-and-error.

The choice of physical characteristics of the button interface is usually determined by the smallest button that the child can press with reasonable accuracy, without regard to measurement of speed of movement.

Three Major Factors Communication rate is acknowledged to be a primary determinant of success in assisted communication. Factors: cognitive and language skill for selecting meaningful utterances sensory ability to locate and identify buttons on the device motor ability to activate chosen buttons to produce the desired output

Communication rate is acknowledged to be a primary determinant of success in assisted communication.

Factors:

cognitive and language skill for selecting meaningful utterances

sensory ability to locate and identify buttons on the device

motor ability to activate chosen buttons to produce the desired output

Three Primary Features of the Physical Design Button size. Button spacing and layout. Number of button presses required to produce each vocabulary element To seek a mathematical model that predicts the relationship between button size, spacing, number of presses, and the communication rate.

Button size.

Button spacing and layout.

Number of button presses required to produce each vocabulary element

To seek a mathematical model that predicts the relationship between button size, spacing, number of presses, and the communication rate.

Goal To demonstrate feasibility of this approach by predicting a single value: the optimal number of buttons for each child . Use a model whose parameters are determined from measurements of individual children, so that each predicted optimal value will be specific to a particular child.

To demonstrate feasibility of this approach by predicting a single value: the optimal number of buttons for each child .

Use a model whose parameters are determined from measurements of individual children, so that each predicted optimal value will be specific to a particular child.

Subject (1/3) Ten children ages 7–18 years (mean 12, SD 3.6, six boys four girls) with static upper extremity impairment. All children were required to have sufficient cognitive ability to understand the tasks, follow multistep commands, and be able to communicate understanding of the instructions. Children were excluded if they had fewer than 36 different symbols in their Dynavox vocabulary, or if there was any possibility of increased risk due to testing.

Ten children ages 7–18 years (mean 12, SD 3.6, six boys four girls) with static upper extremity impairment.

All children were required to have sufficient cognitive ability to understand the tasks, follow multistep commands, and be able to communicate understanding of the instructions.

Children were excluded if they had fewer than 36 different symbols in their Dynavox vocabulary, or if there was any possibility of increased risk due to testing.

Subject (2/3) Melbourne: the scaled score for the test of upper extremity function in the dominant arm n(1,2): the number of buttons per screen IR(1,2): measured information rate E(1,2)%: percent of incorrect button presses made in 10 min Vocabulary: the total number of different buttons used for reprogramming

Melbourne: the scaled score for the test of upper extremity function in the dominant arm

n(1,2): the number of buttons per screen

IR(1,2): measured information rate

E(1,2)%: percent of incorrect button presses made in 10 min

Vocabulary: the total number of different buttons used for reprogramming

Subject (2/3)

Task 1 (1/2) Subjects were required to touch targets on a computer display fitted with a touch-sensitive screen of width 30 cm and height 23 cm. Subjects were required to maintain contact with the start button until a “go” tone was sounded at a random interval between 500 and 1500 ms later. As soon as the go tone sounded, subjects were asked to touch the target button as rapidly as possible.

Subjects were required to touch targets on a computer display fitted with a touch-sensitive screen of width 30 cm and height 23 cm.

Subjects were required to maintain contact with the start button until a “go” tone was sounded at a random interval between 500 and 1500 ms later.

As soon as the go tone sounded, subjects were asked to touch the target button as rapidly as possible.

Task 1 (2/2) RT: between the go tone and release of the start button. MT: between release of the start button and contact with the target button.

RT: between the go tone and release of the start button.

MT: between release of the start button and contact with the target button.

Task 2 (1/2) The screen layout was similar to task 1, except that instead of a single target button being displayed, a square grid randomly chosen with 4, 9, 16, or 25 target buttons was displayed. During the hold time at the start button and before the target grid was displayed, one of the objects was randomly selected, and its name was spoken by the computer.

The screen layout was similar to task 1, except that instead of a single target button being displayed, a square grid randomly chosen with 4, 9, 16, or 25 target buttons was displayed.

During the hold time at the start button and before the target grid was displayed, one of the objects was randomly selected, and its name was spoken by the computer.

Task 2 (2/2) RT and MT were recorded for all successful trials. Displayed targets and the desired target were selected and arranged randomly on each trial. Subjects continued the task until they achieved 50 successful trials.

RT and MT were recorded for all successful trials.

Displayed targets and the desired target were selected and arranged randomly on each trial.

Subjects continued the task until they achieved 50 successful trials.

Task 3 (1/2) For each trial exactly four randomly-selected target buttons were visible. During the start button hold time, a sequence of two to four graphic images was displayed in the upper right-hand corner of the screen, and each image flashed while its name was spoken in turn by the computer at 1600 ms intervals. RT and MT were recorded for all successful trials.

For each trial exactly four randomly-selected target buttons were visible.

During the start button hold time, a sequence of two to four graphic images was displayed in the upper right-hand corner of the screen, and each image flashed while its name was spoken in turn by the computer at 1600 ms intervals.

RT and MT were recorded for all successful trials.

Task 3 (2/2) The desired sequence remained displayed in the corner of the screen throughout the task in order to provide a cue in case subjects had difficulty remembering the sequence. Data were analyzed if at least 25 successful trials were accomplished.

The desired sequence remained displayed in the corner of the screen throughout the task in order to provide a cue in case subjects had difficulty remembering the sequence.

Data were analyzed if at least 25 successful trials were accomplished.

Task 4 (1/2) There was no start button and no display of sample images. The size of the buttons was adjusted. The computer dictated a random sequence of words from the vocabulary list, and subjects were instructed to touch the appropriate button as quickly as possible after each word was spoken.

There was no start button and no display of sample images.

The size of the buttons was adjusted.

The computer dictated a random sequence of words from the vocabulary list, and subjects were instructed to touch the appropriate button as quickly as possible after each word was spoken.

Task 4 (2/2) The total time from the start of the dictated word to the button contact was recorded as MT. Subjects continued until they had achieved 25 successful trials for each grid size.

The total time from the start of the dictated word to the button contact was recorded as MT.

Subjects continued until they had achieved 25 successful trials for each grid size.

Task 5 Each child’s DynaVox maintained the child’s customary settings as originally programmed for that child. The words were spoken by an examiner and subjects were instructed to type those words using their communications interface as rapidly as possible. The total number of words typed correctly during 10 min of dictation was recorded for each child.

Each child’s DynaVox maintained the child’s customary settings as originally programmed for that child.

The words were spoken by an examiner and subjects were instructed to type those words using their communications interface as rapidly as possible.

The total number of words typed correctly during 10 min of dictation was recorded for each child.

Task 6 Each child’s DynaVox was reprogrammed temporarily using the predicted optimal number of buttons. When more than one screen was needed the assignment of vocabulary words to buttons on subsequent screens was performed using Huffman coding. Huffman coding is not expected to produce meaningful groupings of words. The total number of words typed correctly during a subsequent 10 min of dictation was recorded.

Each child’s DynaVox was reprogrammed temporarily using the predicted optimal number of buttons.

When more than one screen was needed the assignment of vocabulary words to buttons on subsequent screens was performed using Huffman coding.

Huffman coding is not expected to produce meaningful groupings of words.

The total number of words typed correctly during a subsequent 10 min of dictation was recorded.

Fitts’ Law 應用在頭部、腳部、水下及遙控操作等各種動作分析上。此法則主要在探討『動作時間』，但並不足以解釋非視覺回饋的快速動作。當目標大小不變時，動作時間與距離呈對數關係；當距離不變時，動作時間與目標大小呈對數關係。 MT= a + b log 2 ( 2D/W) MT ：動作時間 a 、 b ：實驗常數 D ：起點到目標中心點的距離 W ：目標寬度

應用在頭部、腳部、水下及遙控操作等各種動作分析上。此法則主要在探討『動作時間』，但並不足以解釋非視覺回饋的快速動作。當目標大小不變時，動作時間與距離呈對數關係；當距離不變時，動作時間與目標大小呈對數關係。

MT= a + b log 2 ( 2D/W)

MT ：動作時間 a 、 b ：實驗常數

D ：起點到目標中心點的距離

W ：目標寬度

Hick’s Law 根據資訊理論 (information theory) ，只要改變各選項的發生機率，即可改變刺激的資訊量，且選項的數目不變。故回應時間是刺激資訊量以位元量度的線性函數。 RT=k log 2 N RT ：人員反應時間 k : 實驗常數 N : 所有個數

根據資訊理論 (information theory) ，只要改變各選項的發生機率，即可改變刺激的資訊量，且選項的數目不變。故回應時間是刺激資訊量以位元量度的線性函數。

RT=k log 2 N

RT ：人員反應時間

k : 實驗常數

N : 所有個數

Mathematical Model (1/2)

Mathematical Model (2/2) IR e ( b ) was measured only for b = 4 , 9, 16, or 25 buttons, CC e can only be calculated for those values.

IR e ( b ) was measured only for b = 4 , 9, 16, or 25 buttons, CC e can only be calculated for those values.

DynaVox Programming (1/3) The number and identity of all button presses over a one-week period were retrieved from the DynaVox memory. The probability for each symbol in the vocabulary was calculated using a frequency table. Given the number of buttons and the probability of individual symbols, Huffman coding produces a sequence of buttons that is the “code” for each symbol. (ignore the semantic content of the button symbols)

The number and identity of all button presses over a one-week period were retrieved from the DynaVox memory.

The probability for each symbol in the vocabulary was calculated using a frequency table.

Given the number of buttons and the probability of individual symbols, Huffman coding produces a sequence of buttons that is the “code” for each symbol.

(ignore the semantic content of the button symbols)

DynaVox Programming (2/3) To place the most probable button at the upper left. For buttons that indicated a subsequent screen, a small image of the subsequent screen was placed on the button. Subjects were given 10 min of practice using the reprogrammed system prior to testing on it.(task 5 always preceded task 6)

To place the most probable button at the upper left.

For buttons that indicated a subsequent screen, a small image of the subsequent screen was placed on the button.

Subjects were given 10 min of practice using the reprogrammed system prior to testing on it.(task 5 always preceded task 6)

DynaVox Programming (3/3) before after

Results (1/3) The shape of the predicted curve always has a peak value and provides a fairly close match to the empirically-measured data for all but two of the subjects. IRp, solid line IRe, hollow circles and error bars

The shape of the predicted curve always has a peak value and provides a fairly close match to the empirically-measured data for all but two of the subjects.

IRp, solid line

IRe, hollow circles and error bars

Results (2/3) Patients (filled circles), control subjects (open circles) The regression was significant over all subjects ( r 2 = 0.77, F = 381.53, p < 0.0001) Not significant within the patient group alone ( r 2 = 0.01)

Patients (filled circles),

control subjects (open circles)

The regression was significant over all subjects

( r 2 = 0.77, F = 381.53, p < 0.0001)

Not significant within the patient group alone ( r 2 = 0.01)

Results (3/3) patients (filled circles), control subjects (open circles) The regression was significant over all subjects ( r 2 = 0.82, F = 126.34, p < 0.0001) Not significant within the patient group alone (r 2 = 0)

patients (filled circles),

control subjects (open circles)

The regression was significant over all subjects

( r 2 = 0.82, F = 126.34, p < 0.0001)

Not significant within the patient group alone (r 2 = 0)

Discussion (1/2) The information rate and channel capacity differ significantly between control subjects and subjects with arm movement difficulties. The channel capacity did not correlate with the Melbourne test score. Each child’s device had previously been programmed by using best professional judgment and was believed to be optimal for that child. Yet for half the children significant increases in information rate were possible (27% ~190%), and in two cases the information rate more than doubled.

The information rate and channel capacity differ significantly between control subjects and subjects with arm movement difficulties.

The channel capacity did not correlate with the Melbourne test score.

Each child’s device had previously been programmed by using best professional judgment and was believed to be optimal for that child. Yet for half the children significant increases in information rate were possible (27% ~190%), and in two cases the information rate more than doubled.

Discussion (2/2) Why did not 5 children improve on the reprogrammed device? motor impairment is not the only factor cognitive function language ability lack of familiarity with the new keyboard layout There was a nonsignificant trend suggesting that a larger number of different screens is associated with lack of improvement (4.8 for not improve; 3.4 for improve)

Why did not 5 children improve on the reprogrammed device?

motor impairment is not the only factor

cognitive function

language ability

lack of familiarity with the new keyboard layout

There was a nonsignificant trend suggesting that a larger number of different screens is associated with lack of improvement (4.8 for not improve; 3.4 for improve)

Conclusion We have developed a simple model that predicts information rate from measurements. The model predicts that there is an optimal number of buttons for each child. Half of the children who used assisted communication devices improved their rate of communication, despite only limited familiarity with the new keyboard layout.

We have developed a simple model that predicts information rate from measurements.

The model predicts that there is an optimal number of buttons for each child.

Half of the children who used assisted communication devices improved their rate of communication, despite only limited familiarity with the new keyboard layout.