Role inconsistencies in elderly care robots “You are doing your health exercises well, but I think I will win”

Veron Wormeester - 0758754

Date: 18-03-2014

Supervisors

Raymond Cuijpers

Wijnand IJsselsteijn

Abstract

Future robots will move from doing a single task to doing multiple tasks. This could cause

inconsistencies when the robot adopts a wrong role in a certain task. An experiment has been

performed to see what would happen if a robot would adopt a wrong role in a specific setting. It is

hypothesized that incorrect behavior due to role inconsistency has consequences for the way the

robot is perceived by the participant in terms of trustworthiness and performance. To test this, two

contexts were created: a health exercise context where a fitness exercise guided by the robot had to

be performed, and a game setting. In the game context, a game of Battleships was played against the

robot. The participant would get either the health context or the game context and would do two

trials, one with the role-consistent feedback, and one with the inconsistent role feedback. The results

show that in case of an inconsistency in the roles, the robot is perceived as less safe, less intelligent

and slightly less trustworthy.

Introduction

Having robots in the elderly care is something that seems unavoidable for the future generation of

elderly. The population is aging and the lack of workforce to take care of the people in need of care is

becoming bigger and bigger (WHO, 2007). Elderly care robots could pose a solution for this issue.

Already in 1982, Feigenbaum and McCorduck have suggested to develop a wonderful "geriatric

robot" that could help the elderly as an assistant, coach and companion, all combined into one

"down-home useful" machine, (Feigenbaum & McCorduck, 1982, pp 92-93). Now, more than 30

years later the idea of using robots in elderly care is not science fiction anymore. Well developed and

programmed service robots could aid the care workers with their daily tasks and even replace the

care workers for some of these tasks. Elderly people are capable of staying longer at home with the

aid of a care robot and the amount of robots available for the elderly is virtually unlimited. Living for

a prolonged time at home has benefits for both the elderly care homes as the elderly people

themselves (Tarricone & Tsouros, 2008). Several researchers have been investigating the field of the

care robots and the implications it would have on the way the elderly perceive them, how the robots

should behave and what tasks would be suited for such a robot (Heerink, Kröse, Wielinga & Evers,

2006).

Role adoption under care robots

Assistive care robots can be distinguished in several ways: they could aid the elderly user with their

rehabilitation or they could pose as an assistant or companion. A review of several of these robots

can be found in Broekens, Heerink & Rosendal (2009). According to Broekens et al. (2009), these

identifications are not black and white. Slowly we are progressing to more complex robots capable of

performing multiple tasks. This shift works on multiple levels: a robot nowadays could have the

function of monitoring the person’s health and give health exercises at the same time. But robots in

elderly care should one day adopt several roles to improve the quality of the life of an elderly person

or a care worker. This shift to multiple role adopting robots might lead to a robot being capable of

doing health exercises with the person and serve food later on the day to the elderly person, adapted

to the dietary needs of that person. In this case, the robot acts as a coach and later as a service

robot. It is then also imaginable that a robot could behave as both an assistant and as a companion.

To date, it is still unclear how these roles should be exactly implemented and what is wished for by

the end user (Tapus, Mataric & Scassellati 2006).

There are several frameworks for classifying the role of a robot. Schultz and Goodrich (2007) based

the indication of robot roles on the level of dominance. In this dominance scale, the slave or servant

is positioned on one side and “master” is on the other side. Schultz et al. (2007) mention three

specific types of roles for their explanation: a mentor, peer or slave. Here: the mentor role is the role

a robot could take when it has the function to teach and guide the person, in the case of elderly this

could show in doing fitness exercises, explain about medical subjects or ensure that the person keeps

a healthy lifestyle. A peer role is often found in robots made for comforting the person, companion

robots could be identified as robots behaving in a peer role. Robots in a slave or servant role are

robots that perform tasks for their owner when instructed. It is not unthinkable that someday a

robot built for making and delivering a cup of tea for you will be available on the market (Takahashi,

Nakamura & Hirata, 1998). Other, more elaborate and distinct identifications are identified by

Dautenhahn, Woods, Kaouri, Walters, Koay & Werry (2005) and Scholtz (2002). Dautenhahn et al.

(2005) investigated the roles wished for in an elderly care robot, she used five roles in her research:

assistant, machine, servant, mate and friend. Scholtz (2002) defined role in a broader way by taking

non-autonomous robots into account in his taxonomy as well. Here the following roles were

described: Supervisor, Operator, Mechanic, Peer and Bystander.

Letting the robot adopt several roles in a sequence would greatly benefit the view of people on the

capabilities on robots but could also pose dangers when robots move from a more task oriented

single purpose robot to a context aware multipurpose robot. Because making devices and machines

context aware and the challenges it brings to do so correctly is hard, it is therefore understandable

that making robots this way is a difficult task (Greenberg, 2001; Breazeal, 2003). Robots should not

only be context aware but also make part of that context themselves, this fact will lead to new

challenges that need to be solved before a fully functional robot could replace a human (Fong,

Nourbakhsh & Dautenhahn, 2003). The step from going to robot pets to humanlike servants is very

large, and every detail has to be worked out in order to create a reliable and trustworthy robots

(Graefe & Bischoff, 2003). With that in mind, it is necessary that robots should perform consistent in

regard to the context they are in to ensure convenient interaction (Walters, Syrdal, Dautenhahn,

Boekhorst & Koay, 2008; Riek & Robinson, 2009). This is both of importance in the role the robot

takes in the interaction but also in the way of interacting itself. In the event of an inconsistency the

user might be confused causing a lesser degree of compliance and overall acceptance towards the

robot (Kiesler & Goetz, 2002; Riek & Robinson, 2009). As Riek states:

“The role(s) a robot will adopt during interaction with a user should always be made explicit and

should be immediately apparent. Furthermore, a robot should remain consistent with its advertised

role. A comforting companion robot in a nursing home serving in a peer role should not suddenly

adopt a mentor role and start lecturing residents about their health habits....” (Riek & Robinson,

2009)

This is also shown by Kiesler et al. (2002) who empirically investigated the consistency of robot

appearance and the message it conveys. She found that if the behavior of the robot did not match

the appearance, the acceptability of the robot would decrease. Since the robot is showing behavior

that does not fit into the context of the person, it is likely that the amount of trust in a robot with a

role conflict will decrease as well.

Trust and Performance

Trust is a multidimensional construct that is influenced by a lot of factors and is seen as one of the

most important indicators of successful human-robot communication (Hancock, Billings & Schaefer,

2011). As Hancock and colleagues also mention, the performance of a robot is the primary driver of

trust. For a successful interaction between humans and robots, a certain amount of trust is needed.

Here, the right balance between distrust and too much trust needs to be found. The risk is that if a

person would rely too much on the robot, and would put too much trust in it, it might cause issues

when the robot is malfunctioning. But on the other hand, if the person does not want to comply with

the requests of the robot because of mistrust, then other issues could appear. It is therefore

important that the amount of trust a person has in the robot is well calibrated. Broadbent,

Macdonald, Jago, Juergens & Mazharullah (2007) have shown that people react in different ways to

good or bad performing robots. They found that people reported more negative emotions when the

robot was performing poorly. This shows that inconsistencies in roles could be one factor that

decreases the feeling of trust in a robot. As stated above, the indicators of performance are factors

like behavior, reliability and predictability, and lesser performance could then also cause a lower

amount of trust in a robot.

To further investigate what would happen in case of a role inconsistency and if it indeed hampers

with the perceived performance and the amount of trust in the robot, an empirical study was done

to look into these claims. Expected is that if a robot would show any type of role inconsistency. The

performance and the amount of trust put into the robot is rates lower than if the robot would be

consistent in its role pattern.

Methods

Design

To investigate the claims that the amount of trust and performance in the robot is lower if

inconsistent behavior is shown will be investigated in a 2 (mentor role vs. peer role) x 2 (consistent

behavior vs. inconsistent behavior) mixed design experiment. Here, consistent behavior and

inconsistent role behavior was be tested within participants and the setting of the robot was tested

between the participants. Both these behaviors were tested in an experimental setting with the Nao

robot. The robot either adopted the role of a peer or the role of mentor during a trial. The behavior

change was counterbalanced to limit carryover effects and the effects of fatigue.

The participants were divided into two groups based on random assignment but ensuring a fair

division of age and sex. One group experienced the Nao robot in the mentor role setting; the other

group will experience the Nao robot in a peer setting.

Mentor Role Setting

In the mentor role setting, the Nao robot took the role of a health instructor robot. During this

setting, the robot asked the participant to copy the movements done by the Nao Robot and the

robot gave feedback on the results. The movements to be copied consisted of simple arm bend and

stretch exercises as described in Ebert (2012). Here, the robot first stretches the arms forward and

then bends them downward, repeating this process. In each trial, three different exercises were

performed twice with a small break in between.

Peer Role Setting

In the peer setting, the Nao robot took the role of game companion. During this setting a game of

Battleships was played with the participant. Battleships is a turn based strategy game where two

players have to track and shoot down enemy ships they have positioned on a grid with a predefined

size. This game was chosen because of the equal interaction it promotes; both players are equal in

terms of possibilities in the game. The game is relatively easy compared to other board games played

often by elderly like Scrabble and Rummikub. Besides the equality of interaction and the ease of use

it is also very scalable: the amount of ships and the size of the grid allow for different durations of the

game. The Nao robot used a simple solving algorithm created by the authors for playing Battleships

against the participant and gave suited verbal feedback during the game.

Verbal Feedback

In both settings the verbal feedback of the robot is depending on the condition. In the consistent

behavior condition, the robot used the set of feedback sentences that matched the role the robot. In

the inconsistent behavior condition, the set of feedback sentences from the other setting was used

as the sentences for feedback. Both sets of feedback were made in such a way that the feedback was

meaningful but inappropriate in the wrong setting. All feedback was performed with the robot

looking at the participant while talking. This was done to attract the attention to the robot for the

moment of feedback.

The chosen sentences or the feedback were defined by the authors and were validated through an

online survey. In this online survey, the participants had to rate the sentences on a 7 point Likert

scale about how much they would fit to a health instructor scenario as well to a game buddy

scenario. The sentences were presented in a random order. After the participant had rated the

sentences all sentences had to be labeled to either one scenario in a forced choice questionnaire.

The order of scenarios changed per participant, meaning that one participant would start with rating

the game buddy and the other one with rating the fitness instructor.

After the data gathering, sentences with a less clear preference for a certain scenario were removed

from the list of possible sentences. The mean score of the sentences was used to compare how much

a sentence is valid in one scenario, and unwanted in another. The ten sentences with the biggest

difference in means were chosen as sentences to use. See Table 1.

After the first run of the validation questionnaire spread under friends and acquaintances, 56 people

filled in the questionnaire (31 males, 26 females, average age 25.5 years) a selection of sentences

was found. However, results showed that there was an unequal division in sentences that would fit

to one of the roles, and more suitable sentences needed to be found. A second questionnaire with

more sentences was created and spread out on Facebook groups for a bigger sample size. 195 people

filled in the second questionnaire (40 males, 155 females, average age 39.1 years). There were no

differences found between the male group and the female group, so we averaged across gender. The

results are shown in Table 1. Due to the fact that the sentence “Heel goed, ga zo door” was not

explicitly tested, but is derived from the very similar sentence “Heel goed, probeer zo door te gaan”,

its mean score at the fitness instructor scenario was 6.69 and 95% of the people indicated it would

belong to the fitness instructor. The sentence is included to make the sentence more meaningful in

the inconsistent health exercise context.

Table 1: Chosen sentences and the mean difference between the subjective rating of appropriateness

between the settings

Used Sentence Translated Sentence Mean Difference

Mentor Role (Fitness setting)

Je wordt er gezond en fit van You are getting healthy and fit from it 2,797

Probeer je beste te doen Try to do your best 1,847

Je mag nu even op adem komen You can now catch a breath for a moment

2,900

Heel goed, probeer zo door te gaan Very good, try to continue like this 1,984

Heel goed, ga zo door! Very good, continue like this! *

Dit was alles wat ik met je wilde doen vandaag

This is all that I wanted to do with you today

2,067

Peer Role (Game setting)

Ik vraag me af of ik het goed doe I am wondering if I am doing well 1,812

Ik snap het niet I don’t get it 2,312

Ik vraag me af hoe ik het doe I am wondering how I am doing 1,262

Ik weet niet wat ik nu het beste kan doen I don’t know what I could do best now

3,000

Ik vind het moeilijk I find it hard 1,926

Ik vind het leuk om dit met je te doen I like to do this with you

1,221

Ik denk dat ik dit ga winnen I think I will win this 2,332

Dependent Variables

As the dependent variables for the experiment, the amount of trust were measured along the five

scales of the Godspeed Questionnaire (Bartneck, Croft, Kulic, & Zoghbi, 2009): anthropomorphism,

animacy, likeability, perceived intelligence and safety. The Godspeed questionnaire was used to

investigate the effects caused by the role inconsistencies on attitude towards robots. The amount of

trust was measured using the trust in automated systems questionnaire designed by Jian (2000).

Participants and Task

As the primary goal of the service robot is addressed to the elderly, the experiment was conducted

with elderly people as well. 40 participants in the age group of 50+ were recruited for this

experiment through local Facebook groups, handing out flyers on the market and the participant

database of the University of Eindhoven. Over half of the participants had no previous contact with

the robot before and previous knowledge was taken into account during the research. The

participants were rewarded with a monetary incentive as a thank you for their time.

Depending on the condition, the task the participants had to do was either to follow fitness

instructions by the robot or play battleship with the robot. In the fitness exercise the participant was

asked to copy the robots movements while the robot gave verbal feedback on their performance. In

the game exercise, the robot asked the participant to play a game with him/her. Here, a game of

Battleships was played with the participant. Both tasks ended with a questionnaire the participants

had to fill out. These tasks were done twice in an experiment.

40 participants aged between 49 and 76 (µ: 62.95, sd: 7.79) participated in the experiment of which

20 males and 20 females. 26 indicated they had participated in previous experiments with the robot.

Both contexts were performed by 20 persons of which 10 males and 10 females.

Apparatus and Materials

The Nao Robot by Aldebaran Robotics was used for this experiment. This is a 57cm tall humanoid

robot capable of walking, talking, gesturing and looking around, and has in total 25 degrees of

freedom. The Nao robot can use speech synthesis to talk but is also able to play prerecorded speech.

For this experiment, prerecorded speech was used as speech synthesis in Dutch was not available for

the Nao robot at the time of the experiment. The speech used was created by text to speech

software and was preloaded on the robot before the experiment. For the health exercise mentor

role, a simplified adaptation of Ebert’s implementation for the Nao was be used with two different

health exercises (Ebert, 2012). For the game setting, where the robot adopted a peer role, a simple

BattleShips script created by the author himself was implemented. A Wizard of Oz technique was

used to handle the speech recognition part and to help the participant whenever he or she got stuck.

The experimenter remotely controlled the robot through its inbuilt Wi-Fi connection.

The experiment was conducted in the UseLab of the University of Eindhoven, this lab resembles a

living room and contains a sofa, television, cabinets with books and indirect lighting. The Nao Robot

was placed on the ground on an open space in the health exercise setting and on the Table in the

game condition. For the health exercise the participant was standing on the opposite side of the

open space facing the robot. For the game setting the participant was seated on the couch with the

robot and the game in front of him/her.

Procedure

During the experiment, the experimenter welcomed the participant inside of the UseLab and gave

the participant the informed consent form to fill in. After this form had been filled in, a short

introduction to the robot was given. The participant was then either seated on the Table for the

game settings or guided to a chair in the room for the health exercise setting. Then he or she

interacted with the robot. For the health exercise setting this was a fixed time of 12 minutes. For the

game setting the time of the experiment lasted between 12 and 20 minutes, depending on the

progress of the game. During the interaction with the robot, the robot gave at least ten times verbal

feedback on the progress. After the session, the participant was guided back to the computer to fill in

the questionnaires. When finished with the questionnaire the second and last robot session started.

This session was identical with the first one with the exception that the other set of feedback

sentences was used. Afterwards, after the participant filled out the questionnaire again, the

participant was thanked for their time and received €7.50.

Results

Consistency check

The scales used were checked for internal consistency, the Trust in Automated Systems

questionnaire consisted of 14 items (α = .87). The Godspeed questionnaire consists of 5 scales: the

anthropomorphism consists of 5 items (α = .90), animacy consists of 5 items (α = .76), likeability

consists of 5 items (α = .87), perceived intelligence consists of 5 items (α = .86), safety consists of

three items (α = .539). One item (Calm) was therefore removed from the safety scale as it negatively

influenced the Cronbachs alpha, without it the new α = 0.73.

Trust

The trust questionnaire was filled in

completely by 36 participants. The trust

scores were calculated and after further

inspection on the scores, one outlier was

removed (Z-Score on the difference in trust

between the contexts: -4.06) leaving the

results of 35 participants. The results can be

found in Figure 1 and Table 2. Repeated

Measures ANOVA was performed to see if

the amount of trust between both samples

would change. A trend can be spotted

between the consistent feedback and the inconsistent feedback (F(1,33) = 3,066, p = .089). No

interaction effects were found. A check was made to see if order effects occurred, this turned out to

be not the case (p = .68).

Table 2: Difference in trust between contexts

n µ

Consistent SD

Consistent µ

Inconsistent SD

Inconsistent µ Diff.

SD Diff.

Sig.

Health Exercise 18 5,22 1,09 4,98 1,13 -0,24 0,77 0,238

Game 17 5,43 0,94 5,21 0,78 -0,22 0,77 0,228

Combined 35 5,33 1,01 5,10 0,97 -0,23 0,76 0,089

Figure 1: In both settings, the amount of trust decreases slightly in the Inconsistent condition. Error bars +/- 1 SE

Godspeed

The Godspeed questionnaire was filled in completely by 36 participants, some participants did not fill

in the complete questionnaire but their data was usable for some scales that fit within the Godspeed

questionnaire. No extra outliers were detected during the analysis.

A repeated measures ANOVA was performed on the data with both contexts and consistencies, no

significant results were found for Anthropomorphism, Animacy and Likeability. A main effect on

Perceived Intelligence ( F(1,35) = 8.448, p = .006 ) and Perceived Safety ( F(1,35) = 5.441, p = .025 )

was found, both perceived intelligence as perceived safety are significantly lower when the robot

used inconsistent feedback. The interaction effects for perceived intelligence and perceived safety

show that there is no significant difference between the different settings (intelligence: F(1,35) =

2.167, p = .15, safety F(1,37) = 0.33, p = .57). The difference between the other scales turned out to

be not significant and was not further investigated. Because correlations were found between

Godspeed results and perceived trust, a regression analysis was performed to see if the Godspeed

variables could predict trust. With the consistent feedback, it was found that two predictors

explained 52% of the variance (r² = .55, adjusted r² = .52, f(5,34) = 19.668 p < .01). It was found that

likeability (β = .83, p < .05) and perceived intelligence (β =.47, p <.06) significantly predicted

perceived trust. The other variables were found to be not significant. With the inconsistent

feedback, it was found that only likeability could significantly explain 34% of the variance (r²=.43,

adjusted r²=.34, f(5,35) = 4,589, p < .01, β = .97, p < .01).

Table 3: The results of the Godspeed Scales in the Health and Game contexts

n µ

Consistent SD

Consistent µ

Inconsistent SD

Inconsistent µ

Diff. SD

Diff. Sig.

Anthropomorphism 38 2,37 1,05 2,36 0,93 -0,01 0,50 0,90

Animacy 39 2,53 ,818 2,59 0,91 0,06 0,44 0,40

Likeability 38 3,70 ,733 3,68 0,77 -0,16 0,40 0,81

Intelligence 37 3,35 ,76 3,14 0,77 -0,21 0,43 0,006

Safety 39 3,87 ,77 3,64 1,01 -0,23 0,62 0,025

Figure 2: Results from the Godspeed scales per context, the perceived intelligence drops in the health context but stays equal

in the game context. Error bars = +/- 1 SE

Comparing the tasks, no significant differences were found between them. The order of presenting

however, did matter for the amount of anthropomorphism (µ difference: -0.34, p = .04), showing

that a small order of presenting effect has happened and that the amount of anthropomorphism

decreased in the second meeting with the robot. See also Table 3 and Figure 2.

From the extra questions added to the questionnaires, an increase in perceived bossiness could be

found in the game setting when the inconsistent feedback was given. (µ difference: 1.10, p = .053).

No changes were found in the health exercise context. See Table 4 and Figure 3.

Also, participants felt in general more at ease with the mentor feedback set compared to the game

feedback set, which follows from the fact that the feeling of being at ease was lower in both

contexts. The feeling of being at ease dropped significantly in the health context (t(18) = 2.249, p =

.037), the rise of the feeling of being at ease in the game setting was not significant (t(19) = -1.629, p

= 0.12).

Table 4: Perceived amount of bossiness per context

µ

Consistent SD

Consistent µ

Inconsistent SD

Inconsistent µ

Diff. SD Diff. Sig.

Health 2,53 1,611 2,30 1,342 -,211 1,813 0,485

Game 2,20 1,240 3,30 1,780 1,100 1,586 0,053

Figure 3: The perceived bossiness increases in the game context and decreases in the health context, showing that the

feedback in the health condition is being perceived as more bossy

Discussion

After testing human-robot interaction in two contexts it is clear that the right context does matter in

how a robot is perceived. The results show that putting a robot into a role that is not suited to the

context has influence on the perceived intelligence of the robot, the perceived safety and the

amount of trust in the robot. We found that, in line with our hypothesis, the users rated the robot as

less safe, less intelligent and slightly less trustworthy. For perceived intelligence, the inconsistent

behavior that the robot shows that people in general notice that something is odd and out of

context, and as they cannot put the feedback into context, they assume that it must be the robot

itself that is talking nonsense. This also explains why the perceived safety decreases with inconsistent

behavior. Although less direct, perceived safety is dependent on the predictability of a piece of

technology. Kulic & Croft (2005) already showed this in their research about the design of robots.

They measured through physiological measurements the amount of surprise a person exerted and

the amount of anxiety a person has. It appeared that the amount of surprise was a good predictor for

the amount of anxiety. While the robot used feedback inconsistent with its behavior, the message

the robot told changed both in the game setting as in the health exercise setting from something that

would fit in the context to an out of context sentence. These sentences were often perceived as odd

and either caused people to laugh about the robot or caused people to have some additional

questions about it. As with perceived safety and perceived intelligence, it is therefore

understandable that the perceived amount of trust exerted from the robot is decreasing as well.

Although not strong, the effect found is consistent over both contexts, meaning that further

investigation in the field of trust and role inconsistencies is needed to make this claim valid. The

reason why this effect is less strong compared to the perceived intelligence and perceived safety

could be attributed to the fact that trust is a multidimensional construct, and is influenced by other

factors like intelligence and likeability. As found, the perceived intelligence decreases with

inconsistent feedback but the likeability does not change. Since likeability is such a strong predictor

for trust, it is therefore understandable that the perceived trust did not decrease with the same

levels as perceived intelligence.

The clear change in perceived bossiness shows that the manipulation worked. The increase in

amount of bossiness in the game session shows this. This is in line with expectations, and can be

explained by the fact that coaching during a competitive game is less accepted behavior. People felt

less at ease with inconsistent feedback in the health exercise condition, this is probably due to the

way feedback was given. As the health exercise consisted mostly of one way interaction, statements

like “I don’t know what do to do next” caused some awkward silences under participants. Having two

way interaction in the health exercise would probably have caused this effect to disappear.

This research implicates that robots that are able to adopt more than one role have to be built in

such way that contextual discrepancies are being held as small as possible. Even though context

awareness is a hard subject to tackle it is an important one. This is definitely the case if a robot is

being used in the elderly care and gets responsibilities like reminding elderly to take their medicine.

The findings in this study have some limitations. Firstly, every subject only received one context and

were presented with feedback that they could not place in the situation as they did not know where

the feedback actually belonged. Further investigation is needed to see if the effect would hold in a

broader context. Also, the participants spent only a small time with the robot. It could be expected

that a longer time has influence in the way the communication goes between robot and human.

Here, a small nuisance that is happening more often could grow into a point of irritation, which

would enhance the feeling of distrust. This could only be researched when the robot used is capable

enough.

Creating multipurpose robots that will adopt multiple roles is a challenge that will take a long time

before it will be a “down-home useful machine” and would meet its expectations. Before we reach

this stage, robots will have its imperfections and its flaws, which will have consequences in how we

see and perceive them, but that might just make them a bit more humanlike.

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