Investigating the applicability of user models for motion-impaired users

Simeon Keates, John Clarkson

Department of Engineering
University of Cambridge
Trumpington Street, Cambridge CB2 1PZ, UK
+44 1223 332673

lsk12@eng.cam.ac.uk, pjc10@eng.cam.ac.uk

Peter Robinson

Computer Laboratory
University of Cambridge
Pembroke Street, Cambridge, CB2 3QG, UK
+44 1223 334637

Peter.Robinson@cl.cam.ac.uk

ABSTRACT

This paper considers the differences between users with motion-impairments and able-bodied users when they interact with computers and the implications for user models. Most interface design and usability assessment practices are based on explicit or implicit models of user behaviour. This paper studies the applicability of an existing interface design user model to motion-impaired users for the relatively straightforward task of button activation. A discussion of the empirical results is provided and the paper concludes that there are significant differences between the behaviour of motion-impaired users and the accepted modelling theory.

Keywords

Universal Access, motion-impaired users, user models

INTRODUCTION

Current practices in the design of interfaces (both input hardware and software) are often based on user models and descriptions derived almost exclusively from studies of able-bodied users. However, such users are only one point on a wide and varied scale of physical capabilities. For example, users with a number of different motion impairment conditions cannot cope with most current computer access systems. Such conditions include Cerebral Palsy, Muscular Dystrophy, Friedrich’s Ataxia, and spinal injuries or disorder. Frequent symptoms include tremor, spasm, poor co-ordination, restricted movement, and reduced muscle strength. Similar symptoms are also seen amongst the elderly able-bodied population resulting from conditions such as Parkinson’s Disease, strokes and arthritis. To ensure that users who do not conform to the able-bodied stereotype are not excluded from the use of computers, it is necessary to adopt the principle of designing interfaces for universal access.

Design for Universal Access

Designing for universal access requires the use of standard engineering practice [1]:

Stage 1 - define the problem;

Stage 2 - prescribe a solution;

Stage 3 - evaluate the solution.

When designing interfaces, there is a very strong temptation to jump to Stage 2 of the design process, with only a cursory examination of Stage 1. This line of thinking is common when designing products for motion-impaired users and has, for example, led to numerous rehabilitation robotics products that are neither usable [2] nor commercially successful [5].

Recent discussions about the concept of Universal Access have shown that traditional HCI approaches are not the best way to achieve universal accessibility and that more specific studies are required to reach this goal [9]. Two core themes from this work are proactivity - addressing the issue of accessibility at design time, and adaptation - the ability for the interface to be tailored to the user [8].

Requirements for Universal Access

There are three principal components in a human-computer interface: the user, the interface and the target system. For an interface to be effective it is necessary to enable clear and succinct communication between the user and the target system. This is affected by the rate of delivery of input symbols (speed); the number of bits per symbol (density); and the accuracy of production and interpretation of the symbols (accuracy).

Designing for motion-impaired users requires the adoption of strongly user-centred design practices, complemented by rigorous usability testing. Usability engineering techniques enable a designer to produce an end-product that is ultimately more usable [6]. This covers a wide range of attributes including user ability to understand, ease of learning, and clarity of input. However, central to the theme of user-centred design is having an understanding, or model, of the user. Looking at the interaction process involves investigating a wide range of parameters with complex interdependencies. A framework is needed to achieve successful descriptions of interaction.

User modelling techniques provide such a structure. The origins of such techniques are in neuropsychology and the attempts to understand the brain and its functions through empirical models. User modelling is widely used in the field of human computer interaction because of the clear benefits it brings to the design process [4]. It requires an understanding of the user, the interface and the system. This paper concentrates on evaluating how well a model can be used to obtain a detailed understanding of button activation for motion-impaired users.

It is important to note at this stage that practical limitations, principally involving the variable availability of individual users, restrict the usefulness of detailed statistical analysis. Consequently, the evidence is presented as primarily qualitative.

UNDERSTANDING THE MOTION-IMPAIRED USER

Characterisation of human-computer interaction involves the observation of a wide range of parameters with complex interdependencies. To obtain a thorough understanding, a framework is required for the investigation. User modeling provides such a structure [3].

The role of user models

User models and descriptions provide the basis for defining the architecture and behaviour of an interface. However, they are almost always calibrated exclusively on able-bodied subjects using traditional input devices such as the keyboard and mouse. They have not made been applied to designing for motion-impaired users for several reasons. Principally there are more able-bodied users of computers than there are disabled ones and consequently, the financial incentive is to supply interfaces optimised for able-bodied users. It is often perceived that there is not the client-base to make using similarly rigorous techniques for disabled-specific software financially worthwhile.

Computer-based assessment tools can be developed from accepted neuropsychological theory to provide information on user performance parameters [3]. If designed well, they can provide the basis for a detailed user model.

The Model Human Processor

One of the most straightforward user models is the Model Human Processor (MHP) [3]. This is a very simple model segments the interaction process into three broad function types: the time to perceive an event; the time to process the information and decide upon a course of responsive action; and, finally, the time to perform the appropriate response. Consequently, total response times to stimuli can be described by the following equation:

Total time = xt _p + yt _c + zt _m

where x, y and z are integers and t _p, t_c and t_m correspond to the times for single occurrences of the perceptual, cognitive and motor functions. On neurological grounds, the model assumes that it is only possible for each constituent of the cycle to occur in integer multiples of the base time. In other words, the theory states that it is impossible to have half a cognitive cycle or a third of a motor response [3].

Although this is a very simple model, it was selected because it is very easy to understand and to observe deviations from the predicted behaviour. It is also the basis on which many more sophisticated models are based.

Modelling the motion-impaired user

User modelling theory is a very useful starting point for understanding the user. There is, however, a question over the degree to which discretisation of parameters is possible. For example, in the MHP model there is an assumption that the various stages in the interaction cycle are entirely independent. More recent results in neuropsychology suggest that motor control is governed by negative feedback control loops [7] and this throws the assumption of independence into doubt. It is therefore necessary to validate user models and verify their theoretical predictions through trials with potential users.

Given the variable nature of motion impairments across the different disability types, it may be expected that motor functions would behave differently for individual users. It is therefore necessary to validate any user model before assuming that the theoretical basis is correct for a particular user group. To this end, a first set of user trials was established at the Papworth Trust, a local residential disabled community, with the users detailed in Table 1. It is worth noting the heterogeneity of the users. They were specifically chosen to represent a broad range of impairments and severities, to avoid condition-specific, or impairment-specific results.

User	Condition
PJ3	Tetraplegia (from head injury)
PJ4	Muscular Dystrophy
PJ5	Spastic Quadriplegia Cerebral Palsy
PJ6	Athetoid Cerebral Palsy
PJ7	Friedrich’s Ataxia
PJ8	Athetoid Cerebral Palsy

Table 1. The users from the first set of Papworth trials.

User trials offer a valuable source of observational data. Such observations can provide qualitative reinforcement of quantitative data and also indications of other behaviour should the theory prove to be in need of modification.

Evaluating the perceptual response time

Measuring the perceptual response time was done by cross-referencing the results from two tasks, both involving a black circle moving in a fixed-location circular pattern on the screen.

The first task, Delay, involved the user observing the motion of the circle whilst time delays were inserted into the motion at random points. The purpose of the experiment was to determine whether the user could successfully detect the locations of the delays for given delay durations. An initial delay of 500ms was used and then incrementally decreased until the user could not determine the location of the delay. The smallest delay successfully observed was recorded as the perceptual response time, t _p.

A second task, Smooth, was also used, in which the circle was moved in the same circular pattern in discrete segments of motion. The duration of each movement segment was varied between 10ms and 150ms. The user was asked to state whether the motion was smooth (continuous), jerky (discrete) or borderline. An analogy to an aeroplane propeller was used to describe differences between the motion types. As a propeller begins moving slowly, it is possible to discern each blade. However, as it gains in speed, the blades begin to merge until all that can be seen is the impression of a circle. The borderline between these cases, which corresponds to t_p [3], was the time recorded.

Both the Delay and Smooth tasks were performed with able-bodied and motion-impaired users. Tables 2(a) and 2(b) summarise the results observed. The mean times recorded are rounded to the nearest 10ms. The perceptual response time from Card et al is approximately 100ms.

User	t _p Delay (ms)	t _p Smooth (ms)
A	75	70 - 80
B	95	70 - 90
C	75	70 - 80
Mean	80	80

Table 2(a). Perceptual response times: able-bodied users.

User	t _p Delay (ms)	t _p Smooth (ms)
PJ3	115	110 - 120
PJ5	105	90 - 110
PJ6	95	90 - 100
PJ7	90	90 - 100
PJ8	75	70 - 80
Mean	100	100

Table 2(b). Perceptual response times: motion-impaired users.

Evaluating the cognitive response time

The cognitive processing time can be evaluated by comparing the response times to different types of stimuli. A third task was developed consisting of three stages. The first stage was to activate a large OK button as soon as it was flashed up on the screen. The button would appear after randomly generated time delays between 1 and 4 seconds duration. A brightly coloured background to the button was used to maximise the visual stimulus. The time recorded was the time to clear the button, which is achieved on the key-up action. The users could choose any key or mouse button as the selection method.

The accepted theory states that the time recorded at the end of the key-up corresponds to one perceptual cycle to perceive that the stimulus has been seen, one cognitive cycle to decide that the stimulus has been perceived and then two motor function times to press and release the key. From the theory, the key-up action should be automatic for this task and not involve any perceptual or cognitive steps. Consequently, the process can theoretically be described by the simple reaction time t _p + t _c + 2t _m.

Instead of the brightly coloured background, the second task had the single OK button accompanied by either a large green triangle or blue square. The users had to recognise the colour before activating the button. This required an additional cognitive step for the extra decision process required. Consequently, the time to complete this task was theoretically t _p + 2t _c + 2t _m..

Finally, the users were presented with a letter above the OK button and were asked to recognise the letter before activating the button. This involves a second additional cognitive step, classifying the shape. The time for recognising the letter then pressing the button corresponded to t _p + 3t _c + 2t _m.

User	t _c (ms)
A	100
B	95
C	90
D	89
Mean	93

Table 3(a). Cognitive cycle times: able-bodied users.

User	t _c (ms)
PJ3	105
PJ4	116
PJ5	101
PJ6	121
PJ7	107
PJ8	128
Mean	110

Table 3(b). Cognitive cycle times: motion-impaired users.

Tables 3(a) and 3(b) show the cognitive cycle times obtained from the computer-based able-bodied and motion-impaired user trials by finding the time differences between each of the different cognitive tasks. Again the mean times shown are rounded to the nearest 10ms. The calculated cognitive response time from Card et al is 70ms.

Evaluating the motor response time

Finally the motor function time was obtained by the measurement of simple, repetitive movements. A large OK button was displayed successively ten times in the same location on the screen and the users asked to clear it as quickly as possible. No time delays were inserted into the process, so the user had to keep pressing the button or key as quickly as possible. The time was recorded when the button was cleared and was, according to the theory, equivalent to 2t_m. Again, this task was performed with both able-bodied and motion-impaired users. The task was repeated for a number of times until the times observed had settled down to a consistent value, to allow for the effects of learning and familiarisation with the task. The times obtained for the motor function are shown in Tables 4(a) and 4(b), rounded to the nearest 10ms.

User	t _m (ms)
A	78
B	81
C	70
D	67
E	61
Mean	70

Table 4(a). Motor function times: able-bodied users.

User	t _m (ms)
PJ4	120
PJ6	96
Mean	110
PJ3	223
PJ7	198
Mean	210
PJ5	306
PJ8	297
Mean	300

Table 4(b). Motor function times: motion-impaired users.

The above tables of results show that the able-bodied users are consistent and compare well with the 70ms from Card. However, the motion-impaired results appear to show three bands of times. The lowest value band is sufficiently close to the able-bodied values to be explained away as the effect of the motion-impairment producing slower motion. However, the other bands require more careful analysis.

HYPOTHESISED INTERACTION PROCESS

The differences between the t _m time bands are approximately 100ms and 90ms respectively. These time differences are approximately the same as half the sum of one perceptual and cognitive cycle, or two cognitive cycles. This is important because the values obtained for t _m are based on the assumption that the time recorded is 2t _m and hence divided by a factor of 2. The discrepancy of half the sum of t _c and t _p implies that perceptual and/or cognitive steps must have been present in the original value. Hence, for these users the key-presses are not automatic, as assumed by the theory, and there are extra t _p and/or t _c terms present in the describing equations. Consequently, a possible process for the intermediate range of values is:

Release key (stops previous cycle, starts new one)

See button on-screen (perceptual cycle)

Decide to clear on-screen button (cognitive cycle)

Press key (motor function)

Release key (motor function)

Taking the values for t _p and t _c derived above, this process gives a total time of t _p+t _c+2t _m » (100+110+110*2) = 430ms. When divided by a factor of two, as for Table 4.b, a time of 215ms results (cf. 210ms observed). However, with the approximate similarity in value between t _c and t _p, it could equally be that two cognitive cycles are present somewhere in the loop.

Similarly, the third variation involves extra cognitive and perceptual steps in recognising that the key has been pressed.

Release key (stops previous cycle, starts new one)

See on-screen button is cleared (perceptual cycle)

Decide to clear on-screen button (cognitive cycle)

Press key (motor function)

Check that key is fully depressed (perceptual cycle)

Decide to release key (cognitive cycle)

Release key (motor function)

This gives a total interaction time of 2*(100+110+110) = 640 giving a time of 320ms (cf. 300ms observed). Again, the perceptual cycle could be replaced by a cognitive cycle to achieve similar times.

The assertion of extra cognitive and perceptual cycles is supported by two different sources. The first is empirical observations. Watching the users, particularly their direction of gaze, whilst they interacted with the computer, showed when cognitive processing was occurring rather than physical motion. It was this observation that initially suggested the presence of the extra cycles.

Secondly, the times taken to respond to a simple stimulus for two of the users showed an apparent discrepancy between the times recorded for t _p, t _c and t _m for the motion impaired users and the reaction time to a simple stimulus. The values obtained are shown in Table 5.

Reaction to simple stimulus (ms)	Able-bodied: theory	Able-bodied: recorded	Motion-impaired: recorded
Predicted	310	320	420
Observed	-	320	620

Table 5. Response times to a simple stimulus.

The 200ms discrepancy for the motion-impaired users is not explained by the standard MHP paradigm. However, it is approximately equal to an extra cognitive and perceptual step under the above regime (210ms) or two additional cognitive steps (220ms).

EVALUATING THE HYPOTHESISED INTERACTION

It is clear from the results discussed above that the theoretical assumption of certain types of motor function being automatic is not valid for motion-impaired users. However, whilst the results imply possible explanations for the deviation from the theoretical model of behaviour, they are not conclusive. Consequently, a second set of user trials was established at the Papworth Trust to determine where the extra cycles were being inserted into the interaction and, if possible, to identify the nature of those cycles.

User	Condition
PI3	Athetoid Cerebral Palsy
PI5	Athetoid Cerebral Palsy, deaf, non-speaking, ambulant
PI6	Athetoid Cerebral Palsy, ambulant
PI7	Friedrich’s Ataxia

Table 6. The users from the second set of Papworth trials.

The users for the second set of trials were selected to form a small group that was representative of a wide range of capabilities. Two of the users, PI5 and PI6 were mildly impaired in their hands movements. The other two users, PI3 and PI7, had more severely impaired hand motion. PI3 displayed symptoms of spasms and weak movement. PI7 had continuous tremor and clenched hands. The full range of users is shown in Table 6. Note that only user PI/PJ7 participated in both this and the previous experiments. The other three users were new to the trials.

The trials were not repeated with able-bodied users because the results from the first set of trials for this group were in accordance with the MHP theory. It was therefore decided that those results would still be applicable for the discussion of the second set of trials.

Experimental procedure

Initially the perceptual and cognitive tasks from the first set of trials were performed to provide values for t _p and t _c for each of the users. Two new tasks were then performed. These were augmented versions of the motor function and reaction time tasks from the first set of trials. Unlike the first set of trials, where only the time to completion was measured, both the button-down and button-up times were separately measured and recorded.

Measuring the cognitive and perceptual times

To help identify where cycles were being inserted into the interaction, it was necessary to calibrate the cycles involved. Following the procedures described for the first set of user trials, the perceptual and cognitive cycle times were measured for the new users. The results are shown in Table 7.

User	t _p (ms)	t _c (ms)
PI3	95	120
PI5	100	100
PI6	90	110
PI7	90	110

Table 7. The perceptual and cognitive cycle times.

Measuring the motor function times

The motor function task consisted of the user pressing the input device button 20 times. This was repeated three times. To remove any possible influences of button characteristics for specific input devices, each user was asked to repeat the task with a mouse, the keyboard space bar and the left button of a laptop trackpad. User PI7 also performed the task with an EasyBall because this was the input device he was most familiar with.

The results from the motor function trials are shown in Table 8. The mean times are rounded the nearest 10ms. Results are only available for the mouse for user PI3 because he had to withdraw from the trials for an operation.

With the single exception of the mouse for user PI5, the results for users PI5 and PI6 reflect the interaction predicted by theory of one motor cycle for each button-down and button-up action. The overall average motor function time is 100ms for both of these users (cf. 70ms for able-bodied users).

The longer times from user PI5 with the mouse are because the user was left-handed and experienced difficulty using a ‘right-handed’ mouse.

The results from user PI3 and PI7 exhibit the longer motor function times seen from the more severely impaired user in the first set of trials. The motor function times observed for both users are significantly longer than those for PI5 and PI6. Only the key-down time for PI7 with the space bar varies from this pattern.

User	Input Device	Button down		Button up
User	Input Device	Mean time (ms)	Std dev.	Mean time (ms)	Std dev.
PI3	Mouse	230	35	210	34
PI5	Mouse	170	42	150	26
	Space bar	80	5	115	22
	Trackpad	90	14	100	19
PI6	Mouse	100	28	90	23
	Space bar	80	14	110	13
	Trackpad	80	17	110	20
PI7	Mouse	210	71	180	43
	Space bar	120	39	240	86
	Trackpad	200	43	230	58
	EasyBall	220	67	310	80

Table 8. Motor function button-down and button-up times.

Looking at the results more closely, it can be seen that for PI3 the button-down and button-up times are very similar, implying the same interaction stages for both actions. The same applies to PI7 with the mouse and the trackpad. However, the results from PI7 using the space bar and EasyBall show approximately 100ms difference between the button-down and button-up times.

Measuring the reaction times

The reaction task consisted of the user being presented with a visual stimulus 10 times, at random time intervals. The stimulus was a large, brightly coloured pattern, which was cleared on a button-up action. As with the motor function time, this was repeated three times for each input device and the button-down and button-up times recorded.

The results from this task are shown in Table 9. Again, the mean times recorded are rounded to the nearest 10ms.

From the MHP theory, the button-down time should correspond to t _p + t _c + t _m and the button-up time should be t _m. For user PI6, the expected button-down and button-up times were 300ms (90+110+100) and 100ms respectively. The button-down and button-up times in Table 9 generally correspond well with the predicted values for PI6. Only the trackpad button-down time appears to differ significantly and the high standard deviation of that value implies that the discrepancy could be because of experimental noise.

User PI5 also had the same predicted times for button-down (100+100+100 = 300ms) and button-up (100ms). The button-down times recorded in Table 9 agree well with the predicted time, however the key-up times vary quite considerably.

User	Input Device	Button down		Button up
User	Input Device	Mean time (ms)	Std dev.	Mean time (ms)	Std dev.
PI5	Mouse	320	52	260	26
	Space bar	320	46	140	18
	Trackpad	320	49	210	30
PI6	Mouse	320	49	110	22
	Space bar	330	42	120	28
	Trackpad	380	70	100	17
PI7	Mouse	360	57	180	20
	Space bar	450	87	190	38
	Trackpad	450	122	190	39
	EasyBall	490	82	170	50

Table 9. Reaction button-down and button-up times.

User PI7 appears to have consistent key-up times, but a spread of approximately 130ms across the mean key-down times.

Results analysis

The motor function task times for users PI5 and PI6 correspond well with those predicted by the MHP theory and with the 70ms value of t _m from the able-bodied trials. Similarly the reaction times generally corresponded well, with the most notable exceptions being the button-up times for PI5. However, the results for PI7 only agree with the theory for the space bar key-down time. All of the other results for that user and the limited results for PI3 disagree with the theory. This reflects the relative differences in physical capability, with the two most capable users correspondingly most closely to the able-bodied model, as would be expected.

The motor function times for PI3 are approximately twice those for users PI5 and PI6. With the exceptions of the space bar key-down and the EasyBall button-up, the same is true for PI7. Given that the number of motor function cycles is fixed at one for each time recorded, then extra cycles of another kind were being inserted into both the button-down and button-up actions, or the motor function times are much longer for those users.

The space bar key-down times show that PI7 is capable of producing faster motor function times than the 210ms button-down average for the other devices. This implies that the apparently slow times are because of the insertion of extra cycles into the interaction and reinforces the assertions made at the conclusion of the first set of user trials.

If the 120ms space bar time is taken as the true value of t _m, then the time difference between the expected and observed button-down actions is approximately 90ms. This broadly corresponds to the values of both t _p and t _c for this user. Looking at the average button-up time of 220ms (excluding the EasyBall time) this again gives a similar difference of 100ms. Consequently, the most likely description of the interaction is one extra cycle being inserted into both the button-down and button-up actions and that it is the same type of cycle being inserted into both.

THE REVISED INTERACTION PROCESS

Given that a perceptual cycle without an associated cognitive one to recognise what has been perceived is unlikely, the most probably explanation is that an extra cognitive cycle is being inserted into both steps. This contradicts the interaction process postulated at the end of the first set of user trials. The most likely description of the process is:

Decide to press the button (cognitive cycle)

Press the button (motor function)

Decide to release the button (cognitive cycle)

Release button (motor function)

Further evidence of this comes from examining the times recorded in more detail. Figure 1 shows the motor function task times recorded for PI7 using the EasyBall.

Figure 1. The motor function task times for PI7.

The major peaks in the button-down times occur at 110, 210 and 310ms. The 110ms peak most likely corresponds to the true value of t _m and compares well to the observed 120ms space bar key-down time. The central peak, at 210ms, represents the most frequent time observed and coincides with the t _p + t _c time from the interaction model above. The third peak, 310ms, most likely corresponds to the insertion of a second cognitive cycle, but could also represent the insertion of a perceptual cycle. The data is inconclusive about this.

The times for the button-up action align well with those for the button-down action, but displaced by 100ms. This implies that for this input device, extra cognitive cycles are being inserted compared to the button-down activity. This is reflected in the 310ms button-up time for the EasyBall in Table 8. For the other input devices, the 100ms offset in button-up times is not observed, and the peaks in button-down and button-up activities are coincident.

Looking at the reaction task, the button-up times for PI7 are similar to those obtained for the motor function task. This implies the presence of a cognitive cycle in that phase of the task. The predicted button-down time for this user should be 90+110+120 = 320ms. This is broadly similar to that recorded for the mouse, but approximately 130-170ms faster than for the other input devices. Figure 2 shows the full range of times obtained for the button-down activity for user PI7 with the trackpad button.

Figure 2. Trackpad button-down times for PI7.

The first peak in Figure 2 occurs at approximately 320ms, in accordance with the time predicted by the MHP theory. This is followed by a broader peak between 400 and 600 ms and then a narrower peak at 650ms. A possible explanation of the central broad peak is that there are actually two peaks present, one at approximately 450ms and the other at 550ms and that the high frequency of the 450-500ms time band is a result of the two peaks overlapping.

Figure 3.Trackpad button-up times for PI7.

The button-up times for reaction task for PI7 using the trackpad button are shown in Figure 3. There are two principal peaks that can be seen. The first occurs at around 110ms and provides further evidence of t _m being approximately that value for PI7. The second peak is broader, but centred on 210ms. This implies the presence of an extra cognitive cycle as for the button-up activity in the motor function task.

Consequently, the most likely resultant interaction process for user PI7 reacting to a stimulus is:

See stimulus appear (perceptual cycle)

Decide stimulus has been seen (cognitive cycle)

Decide to press key (cognitive function - optional)

Press key (motor function)

Decide to release button (cognitive cycle)

Release button (motor function)

CONCLUSIONS

The results show that even using a very simple user model, such as the Model Human Processor, can offer valuable insights into how motion-impaired users interact with computers.

The results from both sets of user trials show that the individual components of the Model Human Processor are comparable for able-bodied and motion-impaired users. As expected, the largest observed difference was the motor function time. For this the motion-impaired users were approximately 50% slower than their able-bodied counterparts. However, a variation of approximately 20ms was also noted between the times for the cognitive cycles, with the motion- impaired group being slower. The precise cause of this cannot be identified from this experiment alone. However, the assertion is that it there is an additional delay due to the extra effort required to plan and particularly to control physical movements by the motion-impaired users.

When combining the observed times into a known interaction process, such as pressing a key in response to a simple stimulus, all of the able-bodied users produced response times in accordance with the predicted results. However, the motion-impaired users did not. This at first appeared to show that the model was not working. However, a careful study of the users during the interaction process and of the times obtained showed that extra cognitive cycles were being inserted.

The most likely explanation for the different interaction patterns between able-bodied and motion-impaired users is the extra cognitive effort required to control physical motions in the presence of an impairment. The extra effort not only manifests itself as slightly slower cognitive cycle times, but also additional cognitive cycles in actions that theory predicts should be automatic. The purpose of those extra cycles is unclear at the moment. It is probable that they arise either from the users’ desire to be certain about each movement or from the user being perpetually in a learning mode when interacting with a computer.

Consequently, the implications for those relying on models of interaction for designing interfaces or usability tests, is not to rely on the accepted able-bodied models and ‘add a bit’, but to actually measure the differences in the interaction styles between users with different capabilities. This is the only way of ensuring that false assumptions about behaviour are not made. It is also clear that it is necessary to support the users wherever possible to minimise the need to insert the extra cognitive cycles into the interaction. This can often be achieved by offering additional supportive feedback to the users, through positive reinforcement of actions.

Further work

Further research is being performed to quantify the nature of the differences between able-bodied and motion-impaired users and to quantify the cognitive overhead associated with different input movement types.

ACKNOWLEDGEMENTS

The authors would like to thank the EPSRC for funding this research and the users at the Papworth Trust.

REFERENCES

Blessing, L.T.M., Chakrabarti, A., Wallace, K.M. A design research methodology, in Proceedings of ICED '95, (Prague, Czech Republic, 1995), 502-507.
Buhler, C. Robotics for Rehabilitation - A European(?) Perspective. Robotica 16, 5 (Sept 1998), 487-490.
Card, S.K., Moran, T.P., Newell, A. The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates, 1983.
Keates, S., Clarkson P.J., Robinson P. Developing a methodology for the design of accessible interfaces, in Proceedings of the 4th ERCIM Workshop, (Stockholm, Sweden, 1998), 1-15.
Mahoney, R. Robotic products for rehabilitation: Status and strategy, in Proceedings of ICORR '97, (Bath, UK, 1997), 12-22.
Nielsen, J. Usability Inspection Methods, John Wiley & Sons, 1994.
Rosenbaum, D.A. Human Motor Control, Academic Press, 1991.
Stary, C. The role of design and evaluation principles for user interfaces for all, in Proceedings of HCI Int'l '97, (San Francisco, USA, 1997), 477-480.
Stephanidis, C. Towards the next generation of UIST: Developing for all users, in Proceedings of HCI Int'l '97, (San Francisco, USA, 1997) 473-476.

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