Age and Gender Factors in User Acceptance of ...

The 18th IEEE International Symposium on Robot and Human Interactive Communication Toyama, Japan, Sept. 27-Oct. 2, 2009

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Age and gender factors in user acceptance of healthcare robots The University of Auckland, New Zealand

I. H. Kuo, J. M. Rabindran, E. Broadbent, Y. I. Lee, N. Kerse, R. M. Q. Stafford and B. A. MacDonald

Abstract—Human-robot interaction (HRI) and user acceptance become critical when service robots start to provide a variety of assistance to users on a personal level. Limited research to date has studied the influence of users’ attributes (such as age and gender) on the acceptance of service robots and the implications for HRI design. This paper describes the development of a social interactive healthcare robot named Charles, capable of measuring blood pressure. Using blood pressure monitoring as the service scenario, a user study was conducted to investigate the differences between two age groups (40 to 65 years and over 65 years) in attitudes and reactions before and after their interactions with Charles. The results showed few differences between the two age groups. A significant gender effect was found, with males having a more positive attitude toward robots in healthcare than females. This study reveals the importance of considering gender issues in the design of healthcare robots for older people. Overall, the performance of the robot was rated high, however the participants expressed desires to have more interactiveness and a better voice from the robot. According to our sample, age need not be a barrier to users’ acceptance of healthcare robots.

A

I. INTRODUCTION

CCORDING to the United Nations, most of the developed countries in the world are already or will become aged societies in about 10 years [1]. By 2050, the population aged 85 and over will reach 19 million, exceeding the younger proportion for the first time in human history [2]. As most countries in the world are already struggling with a shortage of medical workers, particularly nursing staff, the fast growing demands of the older population pose a huge challenge to current health services and residential aged care facilities (ACFs). These urgent issues have promoted a variety of research initiatives that have investigated the needs of older people and the ability of robotics technology to provide solutions. In the last two decades, many robots had been developed to promote independent living and provide enhancements to

Manuscript received March 15, 2009. This work was supported in part by the University of Auckland under Grant 3608916 and a doctoral scholarship for Kuo. I. H. Kuo and B. MacDonald, are with the Department of Electrical and Computer Engineering, E. Broadbent and R. M. Q. Stafford are with the Department of Psychological Medicine, N. Kerse is with the Department of General Practice and Primary Health Care and Y. I. Lee is with the School of Medicine, all at The University of Auckland, New Zealand, phone: +64 9 373 7999; e-mail: {t.kuo, b.macdonald, e.broadbent, n.kerse}@auckland.ac.nz and {rqui023, ylee150}@aucklanduni.ac.nz J. M. Rabindran., was with the University of Auckland, New Zealand. He is now with the Faculty of Medicine, at the University of Sydney, Australia (e-mail: [email protected]).

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the quality of life of older people. For example: both Pearl and Care-O-bot were developed to provide mobility supports and medication reminders to older people and people with disabilities [3, 4]. PARO, a seal robot with tactile sensors embedded in its skin was developed to provide similar therapeutic values to pets in places such as nursing homes where real pets are not practical [5]. Falls are the leading cause of accidental injuries and deaths among the older people [6], and robots such as U-bot are being developed to perform simple diagnostic checks after a suspicious fall event and contact emergency help. As robots’ functionalities and abilities continually to be improved, recent research has started to draw attention to the human-robot interaction and communication as they are critical components to users’ acceptance of robots especially robots that serve older people and people with disabilities. Due to the nature of healthcare services, which involve services and interactions on a personal level, many aspects of HRI such as social and psychological factors may influence the interaction between robots and users, and hence need to be considered by robot designers. Mutlu categorizes the design of HRI into three dimensions: robot attributes (e.g. appearance and character), user’s personal factors (age, gender and mental models of robots) and the nature of the task being performed (e.g. collaborative and competitive tasks) [7]. Recent research has tried to evaluate the effects of some of these factors on HRI and their implications for the design of robots. In one of the early studies, Nass manipulated a computer’s personality and investigated its effects on human-computer interaction (HCI) [8]. It was claimed that computer personality could be easily created due to the human tendency to assign personality and human qualities to non-human entities. Humans tend to respond socially to computers regardless of the extent to which they believe the computer actually has personality. As an extension to this work, Goetz investigated the influence of two different robot personalities on users’ behaviour in terms of their compliance to the robot’s instructions in two different task contexts [9]. The results suggested that a robot’s appearance and character should be designed carefully to match the nature of the robot’s task. Broadbent [10] showed that people’s emotional reactions to a robot depend on the robot’s behaviour. Gender has long been suggested to be an important factor in HRI due to the differences between men’s and women’s social behaviour and their social roles in the society [11]. Using Honda’s Asimo humanoid robot, Mutlu demonstrated

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a gender effect and its interaction with two opposite task structures: competitive and cooperative in an interactive two-player video game [7]. His results showed that men’s perception of and interaction experience with the robot very much depended on the task structure while little difference was found in women’s perceptions in both task structures. Furthermore, men showed greater desirability towards robot in cooperative tasks which suggests better acceptance. The influence of age on user acceptance is particularly important in the area of human assistance robotics, as older people tend to require the most assistance. In the related research fields of HCI and psychology, age differences in abilities, attitudes, behaviour and willingness to use new technologies have been shown. For example, older people tend to have more negative attitudes toward and are less comfortable with new technology, including computers, due to generally less experience or access, compared to the younger age groups [12]. In terms of abilities, older people are slower and tend to make more errors when performing computer-based tasks [13]. The influence of age, gender and education on robotic preferences was investigated in a pilot study by Khan [14]. This study explored users’ ideas (including robot appearance) and attitudes toward robotic assistance for several tasks in a home setting through interviews and questionnaires The study found that most people were generally positive toward the idea of service robots, however it suggested gender differences in attitudes toward robotic assistance in household tasks. Of note is that the majority of the participants were aged between 21 and 30 years. Using a similar approach, Giuliani investigated use of technology for assistance using questionnaires with 123 older people aged 62 to 94 [15]. It was found that older participants tended to adopt more passive strategies and were more likely to give up instead of using technological aids compared to younger participants (below 74 years). Younger people were found to be more positive and active in using technology and in adaptation of the home environment. In a separate study, Scopelliti [16] again used questionnaires to find out the similarities and differences between three age groups (young people, adults and older people) in their attitudes, perceptions and preferences for domestic robots. The older group showed more distrust in new technology, responded with more negative emotions toward robots, and preferred robots to be more serious with fewer autonomous features such as learning abilities. These questionnaire studies are important, however very few studies to date have investigated age as a factor in HRI through experiment or user trials. In one user trial, Kerstin investigated people’s attitudes toward the idea of having a future robotic companion and their preferred roles before and after interactive sessions with a robot based on a Peoplebot [17]. The results suggested that older people were more willing to accept a robot companion as an assistant or

appliance rather than as a friend or personal companion. From these recent studies in HRI, it was observed that the older population, which is the target user group for aged care robotics, is too often under-represented. In addition, only a few studies have used a robot in an interactive session to evaluate people’s reactions and experiences. More research is needed to investigate the older persons’ reactions to an assistive robot. In this paper, we present the development of a social assistive robot “Charles” for vital signs monitoring and the results of a user trial to study differences in user acceptability between middle-aged (45 to 65 years) and older persons (over 65 years). The groups were compared in terms of their social interaction experiences and ratings of the robot “Charles.” Implications of these differences on aspects of robotic design are presented. II. ROBOT PLATFORM The robot “Charles” is based on the Peoplebot from Mobile Robots as shown in Fig. 1. This robot is equipped with a wide range of hardware including a differential drive system, SICK laser range finder, sonar and infrared sensors. For HRI, it’s equipped with two microphones, speakers, a touch-screen, Pan-Tilt-Zoom camera and a 2 DOF gripper [18]. In the setup for the user study, the robot was additionally interfaced with an Omron (M10-IT) digital arm-cuff blood pressure monitor via a USB connection.

Fig. 1. Robot Charles A. Software Components in Charles Player is a popular control interface in robotics research and was used as the basis for all the software developments on Charles [19]. As shown in Fig. 2, the client/server model adopted by Player allows all hardware and software components to be controlled via server proxies concurrently from a client program over a network. The Festival proxy

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provides control to the speech synthesis; virtual face proxy for the 3D face; humanvitals proxy for the blood pressure monitor and p2os proxy and laser to the sonar and odometry sensor and the SICK laser range finder respectively. Player provides support to most of the components needed, including the laser range finder, ultrasonic sensors and the motors, as well as the Festival speech synthesis system. Two new Player drivers and interfaces were developed within the University of Auckland for the 3D face and vital sign monitors [20]-[22].

to validate the readings of each vital sign retrieved. In the setup for the user study, only two of the vitals signs (blood pressure and pulse readings) were enabled. It has to be noted that the driver of the Omron blood pressure monitor was not available at the time of the development and was reverse-engineered by traffic sniffing. The developed driver allows number of readings and results of a specific record to be retrieved from the Omron blood pressure monitor [22]. Due to the need of different behavior and personality for HRI studies, the designated blood pressure measurement procedure is specified in an XML script separately from the client program. The extensible markup language XML used, allowing the sequence and timings of the events including robot’s facial expression, speech as well as storage of results to be specified in a simple tag format [23]. III. EXPERIMENT

Fig. 2. System block diagram of Charles The virtual 3D face for user interaction, is capable of displaying nine different expressions including sadness, happiness, fear, surprise, confusion, disgust, ecstasy, thoughtfulness and anger and 14 visemes to visualize all 41 phonemes in English language [20]-[21]. The animation of the 3D face and the speech synthesis are synchronized using Festival and an XML library for conversion of phonemes to visemes. Before Charles speaks, each spoken sentence is first parsed through the Festival speech synthesis system to generate an audio (.wav) file and a phonetic transcript. Using the XML library, the phonetic transcript is then directly converted into a viseme transcript through the mappings defined in the XML library. The viseme transcript generated is composed of a series of visemes and their durations and is used for facial animation. Synchronization was achieved by playing back a generated audio file and the 3D face animation at the start of each spoke sentence. As speech is being delivered, subtitles are displayed at the bottom of the face. In order to provide a common interface to vital sign monitors, a new interface “humanvitals” was developed and added into Player to facilitate integration and transmission of vital sign data over the network [22]. In the interface, vital signs data that were considered and implemented are body temperature, blood pressure, pulse rate, blood oxygen and glucose level. The “humanvitals” interface also includes other values needed in the vital signs measurement procedure including time of the reading taken and a bit mask

The aims of the user study were to assess how people responded to a healthcare robot that measured their blood pressure, and to investigate whether middle-aged people had different reactions to the robot compared to older people. Differences in gender were also explored. Based on the findings of previous research, the first hypothesis was that the older group would be less experienced with technology and hence would have more negative reactions. A blood pressure monitoring service from the robot is not of a competitive or cooperative nature, but is a simple interaction involving education/learning and some collaboration from the participants. Hence, the second hypothesis was that there would be no gender effects in this experiment. The study also aimed to identify which features of the healthcare robot people considered desirable and undesirable to determine how to improve the robot. A. Participants and procedure 400 invitations were sent to patients of a local General Practitioner and gerontology group volunteers who lived close to the trial venue and met the inclusion criteria of being aged over 40 years. A total of 57 participants were recruited and divided into two age groups. There were 29 participants in group 1 (aged 40-64 years) of mean age 55.90 years (SD 4.65 years), which comprised of 20 females and 9 males. Group 2 (65 years or older) had 28 participants of mean age 73.07 years (SD 5.62 years) and had 13 females and 15 males. The large majority of participants were of New Zealand European origin (25 in each group), although there were also Chinese, Maori, Indian, English and Native American ethnic groups represented. The participants were introduced to the robot Charles and had their blood pressure measured by him. In the interaction, Charles first greeted the participants, and then gave step by step instructions as to how to put on and position the arm-cuff and press a button to start the measurement. At the


end of each measurement, Charles informed each participant of the result. After their interaction with Charles, each participant was required to fill out a questionnaire on their feelings and reactions. Every interaction was videotaped and played back to the participant following the completion of the second questionnaire. This was to assist recall of thoughts and feelings during the interaction. B. Psychology Scales Used The first questionnaire was administered prior to meeting the robot and included questions about age, gender, ethnicity, and how experienced the participants were with using computers on a scale from 1 ‘not at all’ to 8 ‘extremely’. In the second questionnaire, several social psychology psychometrically validated scales were used to assess the interaction. These included the Quality of Experience (QOE) Scale to assess the quality of the social experience [24], the Social Interaction Scale (SIS) to assess the quality and engagement of the social interaction [25], and a thoughts/feelings form for evaluating interactions between human dyads [26]. These have been used in HRI research [10]. An additional item assessed how comfortable participants felt about having their blood pressure taken by a robot from 0 ‘very comfortable’ to 8 ‘very uncomfortable’. To assess attitudes both before and after the interaction, the two-item Attitudes Towards Healthcare Robots scale (ATHR) was included [10]; Cronbach’s alpha was .85.In addition, a new 11 item Robot Attitudes Scale (RAS) was designed to assess participants' general attitudes towards robots, including usefulness, friendliness, ease of use, safety, and reliability, rated on scales from 1 to 8. All items were summed to create an overall score, where lower scores represent more positive perceptions. Cronbach’s alpha for the scale was .85. Several items asked the participants to rate the attributes of the robot after the interaction on scales, as shown in Table 1. C. Analysis Data analysis was performed using SPSS (Version 15). Normality was checked using Kolmogorov-Smirnov tests and non-parametric tests were performed where data did not meet criteria for parametric tests. A two-tailed significance level of .05 was maintained.

Whitney U = 377, p =.64) as shown in Fig. 3. When these scales were correlated with ages of participants, all correlations were small (QOE r=.10, p=.46; SIS Quality r=.12, p=.38; SIS Engagement=.13, p=.33). An independent t-test showed a non-significant trend for the older group to report feeling less comfortable with the robot taking their blood pressure (mean 6.82, SD 1.09) than the younger group (mean 7.34, SD 0.81), Mann Whitney U = 296, p =0.06.

Fig. 3. Differences in ratings of interaction between the two age groups (mean scores and standard deviations displayed). Attitudes towards Robots A repeated measures ANOVA showed no significant difference in Robot Attitudes from before to after the interaction (mean before 34.69 (9.76), after 34.61 (12.23), F(1,55) =.002, p=.97). There was no significant difference between the age groups (younger group mean before 34.79 (8.67), after 33.90 (10.79); older group mean before 34.59 (10.93), after 35.35 (13.71)), F (1, 55) =.06, p=.80. Similarly, there was no significant difference in Attitudes towards Healthcare Robots between groups from before (mean 34.69, SD 10.93) to after the interaction (mean 34.78, SD 8.67), F(1,55)=2.26, p=.14. There was no significant difference between age groups (younger group mean before 13.17 (2.76), after 13.83 (2.22)), and older group (mean before 12.61(3.19), after 12.82(3.24)), F(1,55)=1.24, p=.27). Feature

Scale Indication

IV. RESULTS

Face

Experience with computers: The older age group reported significantly less experience with computers (mean 4.36, SD 2.00) compared to the younger age group (mean 6.05, SD 1.27), Mann-Whitney U = 198.50, p = .001). Ratings of the Interaction: Independent samples t-tests and Mann-Whitney-U tests showed no statistically significant differences between the younger and older age groups for the QOE Scale (t (55) = .66, p = .52), the SIS Quality (t (55) =.27, p =.78), or SIS Engagement (Mann

Eyes

1=Did not like it at all; 8=Liked it a lot 1=Did not like them at all; 8=Liked them a lot 1=Did not like it at all; 8=Liked it a lot 1=Not clear at all; 8=Very clear 1=Very poor; 8=Excellent 1=Very poor; 8=Excellent

Expression on face Voice clarity Robot’s instructions Overall performance

Younger group Mean (SD) 4.21 (2.14)

Older group Mean (SD) 3.93 (1.92)

4.29 (1.88)

4.14 (1.78)

4.10 (2.01)

3.86 (1.60)

5.28 (1.98)

6.39 (1.37)

6.07 (2.05)

6.14 (1.63)

5.93 (1.60)

6.39 (1.42)

Table 1. Ratings of robot between age groups Ratings of robot: Table 1 shows the ratings of the robot


between the age groups. There were no statistically significant differences between the age groups on ratings of the robot’s face, eyes, expression on the robot’s face, and robot’s instructions for using the blood pressure device or overall performance on the task by the robot. However, an independent sample t-test showed a significant difference between the two age groups for voice clarity. The robot’s voice was rated clearer by the older group. Gender: When the ANOVAs were repeated using gender as a between subjects factor rather than age, a significant difference emerged in ATHR for gender, F (1, 55) = 8.88, p = .004. Males had a more positive attitude than females on the usefulness of a healthcare robot and toward the possibility of using them in the future (see Fig. 4). There was no significant difference in RAS (F (1, 55) = 1.24, p=.27). Gender was not associated with computer experience, QOE, SIS quality or engagement, or ratings of the robot (p’s>.05).

Fig. 4. Differences between gender in Attitude Towards Healthcare Robots before and after the interaction Thoughts and Feelings: The thoughts/feelings form completed by participants after the interaction highlighted some areas where the robot could be improved. The largest number of thoughts regarded the cuff (47 thoughts). During the blood pressure measurements with Charles, all participants were required to put on the cuff themselves by following the instructions given, and some of them found the repositioning and tightness correction difficult and awkward. The instruction given by the robot (38 thoughts) was another category in reported thoughts and feelings. Some older participants found it difficult to understand and follow the instructions that were given in a short amount of time. In addition, some participants did not like the robot’s voice and found the voice (34 thoughts) too robotic/artificial/monotone/boring. The next three largest thought categories were thoughts about the robot’s likeness/unlikeness to humans (18) such as “expecting it to be more human-like (including shape e.g. arms)”, about the robot’s facial features including the eyes (16) e.g. “face and/or name unexpected,” and about the button on the blood pressure monitor (15) such as “where is the blue button? there is no clear indication of where the button is located”.

V. DISCUSSION This experiment investigated age and gender differences in people’s attitudes and reactions towards robots before and after interacting with the healthcare robot Charles. While the results showed that older people were less experienced with computers than the middle-aged, they had similar attitudes towards robots and rated the interaction similarly. There was a non-significant trend for older adults to be less comfortable during the blood pressure measurement. Men had significantly more positive attitudes towards healthcare robots than women. These findings are somewhat contrary to the previous findings by Scopelliti and Giuliani in [16] where age was found to be the most significant factor in determining user acceptance and the design of domestic assistant robots compared to gender and education level. However, the age range of the groups studied by Scopelliti was much wider (young group aged 18-25; adults aged 40-50 and older group aged 65-75) than in this experiment. We investigated differences in the current older population compared with the middle aged population, because these are the people most likely to be exposed to health care and older care robots as they are introduced over the coming years. One possible explanation for the gender difference found in this experiment may be that people show stronger responses when the tasks performed by robots are healthcare-related rather than those involved in everyday activities in domestic settings which were investigated in [16]. The finding that men were more positive about healthcare robots than women may be important, considering that women have longer life expectancies [27], and make up the majority of residents in older care facilities (e.g. 74.6 percent of the nursing home resident aged over 65 years and over in the United States are female [28]. ) Regarding ratings of the robot, both age groups rated the robot similarly for all aspects except for the clarity of the robot’s voice, which the older group surprisingly rated clearer than the younger group. This may be because the younger group had heard better computer generated voices in their past experiences with computers, and so rated the robot’s voice as poor. Or the older people may have had more hearing difficulties, so the loud, slow, male voice of the robot would have been clearer for them compared with their normal conversations. For future improvements on the robot, the participants desired better interactivity from Charles including his abilities to error-check and confirm with users at steps of the blood pressure measurement. In the experiment, human robot communication features such as clarity of the robot’s voice and instructions were noticed and valued by the users as much as the robot’s appearance. The main limitations of this experiment were that participants were patients who volunteered to take part so it is not known whether these findings are generalisable to the wider population. In the experiment, the participants were


not given any time to become familiar with the robot and it was the first time for the majority of the participants to meet and interact with any robot. The novelty effects discussed in [29] may have affected the result, however this could not be evaluated in this study. VI. CONCLUSION

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In conclusion, our robot “Charles,” is capable of basic HRI and communication via an emotional 3D face with speech and of measuring blood pressure of human patients. Using blood pressure monitoring as the service scenario, the results of this experiment showed that there was no significant differences in attitudes toward and reactions to the robot between the two age groups in all aspects, except a non-significant trend showing that the older people were less comfortable with the robot measuring their BP. According to our sample, age need not be a barrier to user’s acceptance to healthcare robots. Gender appeared to have greater effects on attitudes towards healthcare robots than age. For future robotic design, gender issues may need to be considered especially in healthcare and older care. The face and voice of the healthcare robot, its instructions, and overall performance were high in users’ thoughts. From the ratings and the feedback from the participants, the robot’s interactivity and communication abilities were valued as much as the appearance of the robot. REFERENCES

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