An Investigation of Responses to Robot-Initiated Touch in a Nursing Context

Nurse-patient interaction serves as an important source of inspiration for our experiment. It both serves as a motivating application for robots that initiate touch, and a well-studied example of the role of touch in human-human interaction.

Caris-Verhallen et al. observed two types of touch between nurses and patients that they defined as follows: instrumental touch, which is “deliberate physical contact” that is necessary in performing a task such as wound dressing; and affective touch, which is “relatively spontaneous” and “not necessary for the completion of a task” [7]. In an accompanying study of 165 nurse-patient interactions, researchers observed affective touch in 42 % of the interactions and instrumental touch in 78 % of the interactions [7]. McCann and McKenna report on observations of touching interactions between nurses and older adults in hospice [27]. Most of the observed nurse-initiated touches were on the extremities (arm, hand, leg, foot), and most touches (95.3 %) were instrumental. Touches from nurses on the face, leg, and shoulders were perceived as uncomfortable by patients. Only instrumental touches on the shoulder and arm by a nurse were viewed as comfortable. The authors suggest that misinterpretation of a nurse’s intention may have contributed to patient discomfort during some touches.

In our experiment, we make the same distinction between instrumental and affective touch. By using a robot, we have the distinct advantage of being able to control the physical interaction, and thereby investigate the role of perceived intent through a controlled-laboratory experiment.

We classify the initiation of haptic interaction between a human and a robot into three categories: robot-initiated touch, human-initiated touch, and cooperatively-initiated touch. We define robot-initiated touch as a haptic interaction that the robot initiates by making physical contact with the human. Similarly, we define human-initiated touch as a haptic interaction that the human initiates by making physical contact with the robot. We define cooperatively-initiated touch as being a haptic interaction for which the initiator of the touch is ambiguous. For this paper, we also assume that the initiator of contact plays an active role during the interaction episode, while the other entity plays a primarily passive role.

Shaking hands [38] is an example of cooperatively-initiated touch, since both the human and robot can actively move towards each other. When people pet robots, such as Paro [17] or the Haptic Creature robot [44], it is an example of human-initiated touch, since the person actively moves towards a robot and makes physical contact with the robot’s body. Within this paper we focus on robot-initiated touch. Various robotic systems for healthcare involve robot-initiated touch, including facial massage [20], skin care [41], patient transfer [30], surgery [18], and hygiene [19].

There has been some prior work on studying people’s responses to robot-initiated touch. For example, Bickmore et al. have studied users’ perceptions of and responses to affective touch performed by a virtual agent [3]. The virtual agent included a robotic component capable of pneumatically applying pressure to the user’s hand. The user placed his or her hand in the robotic device and held it there. The pressure was initiated by the virtual agent to help convey empathy and comfort. They found marginal trends that suggested that touch increased participants’ perceptions of having a working relationship with the agent, if the participants were receptive to touch by humans. The opposite trend was observed for participants who were not receptive to touch by humans.

There has also been a video study that looked at the effect of human-robot touch and robot proactiveness on people’s perceptions of a small humanoid robot’s “machine-likeness” and dependability [9]. Participants watched videos of a robot and a person interacting. Among other results, participants perceived the robot in the video to be less machine-like when it touched the person while offering to help the person.

Contemporaneous research reported by Nakagawa et al. in [31] is especially relevant to our study. They investigated how a robot making contact with and wiping a participant’s hand affects the participant’s motivation in a dull task. They compared this interaction with no touch, and the human touching the robot’s hand. They found that participants performed the task significantly longer and with more activity when the robot touched and wiped their hands. Furthermore, participants felt that the robot was more friendly when touching them compared with no touch. They plan to use this interaction in healthcare applications, such as encouraging patients during rehabilitation. A number of factors may be responsible for the differences between their results and ours. We discuss these differences in the Discussion and Conclusions section (Sect. 8).

We are unaware of previous research that has directly investigated how the perceived intent of a robot influences a human’s subjective response to robot-initiated touch. Likewise, there has been little work on determining cues that robots can use to improve subjective responses to robot-initiated touch, such as a verbal warning.

Robot intention plays a critical role in our study. Within this section, we discuss several aspects of robot intention along with related work.

The robot Cody, shown in Fig. 1,¹ is a statically stable mobile manipulator. The components of the robot follow: two arms from MEKA Robotics (MEKA A1), a Segway omnidirectional base (RMP 50 Omni), and a 1 degree-of-freedom (DoF) Festo linear actuator. Each of the two arms is anthropomorphic with 7 DoF. Each arm joint has a series elastic actuator (SEA) that enables low-stiffness actuation. The robot’s wrists are equipped with 6-axis force/torque sensors (ATI Mini40). For this study, we used a custom 3D-printed, spatula-like end effector (7.8 cm×12.5 cm) which roughly resembles an extended human hand [19]. We cut a towel to fit the shape of the end effector and attached it to the bottom of the end effector. In our experiments, this towel makes contact with the participants’ forearms. The towel’s material can be interpreted as a material used for cleaning or a compliant exterior for the robot’s end effector.

For implementation details of the touching behavior, please refer to our previous work [19]. During all of the robot’s arm motions, the robot’s joints were commanded to have low stiffness. For example, when in contact with a participant’s forearm, the stiffness of the robot’s end effector in the direction normal to the surface of the forearm was less than 60 N/m. For all of the robot’s motions in the experiment, the commanded stiffness for the shoulder flexion/extension, shoulder abduction/adduction, shoulder internal/external rotation, elbow flexion/extension, and forearm pronation/supination motions were 20, 50, 15, 25, and 2.5 Nm/rad, respectively. We used position control for the abduction/adduction and flexion/extension motions at the robot’s wrist. Even during position control, the wrist joints have significant compliance due to the passive compliance of the SEA springs and cables that connect the SEAs to the joints. For this paper, we attempted to make the touching behavior consistent with both cleaning a person’s forearm and providing comfort, so that there would be ambiguity about the purpose of the behavior.

When the robot is in its standby position, its arms and end effectors are pointing down towards the floor. The touching behavior begins by executing what we refer to as the “Init” action. During this action, the robot uses a preprogrammed joint trajectory that moves the left arm to a position where the end effector is 15.4 cm above the mattress surface and directly above the participant’s forearm. The robot then moves its end effector downward until the force sensor on the wrist measures a force magnitude ≥2 N, indicating that the end effector has made contact with the arm. We designed the arm trajectory so that the “Init” action completed within approximately 7 seconds when tested on a lab member’s arm. During the experiment, we recorded the time it took for the robot to perform the “Init” action. The overall mean time for the robot to complete the action across all participants was 6.91 seconds (SD=0.10 sec).

After making contact, the robot performs what we refer to as the “Along” action. During this action, the arm moves the Cartesian equilibrium point (CEP) of the end effector at approximately 4 cm/s. We designed the CEP to travel 14 cm to the left, and then 14 cm to the right along the participant’s arm. A bang-bang controller attempts to keep the force magnitude measured by the force sensor on the wrist between 1 and 3 N by moving the CEP down towards the arm or up away from the arm. As a safety precaution, the robot terminates the touching behavior if the measured force magnitude exceeds 30 N. During the “Along” action, the robot exerted an overall mean force magnitude of 2.44 N (SD=0.18 N) across all participants. We also designed this trajectory to be completed in approximately 7 seconds. The overall mean time of the “Along” action across all participants was 6.92 seconds (SD=0.02 sec), and the mean distance the end effector traveled to the left and right was 13.71 cm (SD=0.07 cm) and 13.61 cm (SD=0.02 cm), respectively.

To complete the touching behavior, the robot performs what we refer to as the “Away” action. During this action, the robot lifts its end effector upward, so that it moves away from the person’s forearm. The robot then moves its arm back to the standby position. We designed this action to take approximately 7 seconds. The overall mean time for the robot to complete this action across all participants was 6.83 seconds (SD=0.08 sec).

Ensuring the safety of a person while interacting with a robot is important during any HRI scenario. Studies in which a robot makes physical contact with a human require special care. We took several precautions when designing the robot’s behavior and conducting the study to reduce the chance of injury. First, during the study an experimenter was always prepared to operate a run-stop button if undesirable contact with the robot were observed or anticipated. Second, the robot’s arm operated with low joint stiffness and low joint velocities. Third, the robot attempted to keep the magnitude of the force against the participant’s arm lower than 3 N.

For comparison, Tsumaki et al. reported that people experienced no pain when a skin care robot applied a downward force of 10 N [41]. Other researchers used a force magnitude threshold of 39.2 N with an oral rehabilitation robot [20]. Various factors could influence the force range that a person would find comfortable, including the contact surface over which the applied force is distributed, and the part of the person’s body with which contact has been made. As such, these values provide a coarse comparison with other research.

During the debriefing after the experiment, participants generally reported that the force the robot applied was comfortable. No participants indicated any pain or discomfort during the interaction. Furthermore, in their open-ended responses, several participants reported that the touch was surprisingly light and gentle.

In accordance with the Georgia Institute of Technology Central Institutional Review Board (IRB), we read from a script in order to inform each participant of risks associated with the study, including the potential for undesirable contact with the robot. We also notified each participant that an experimenter would be prepared to use a run-stop button to stop the robot in the event of undesirable contact.

In order to provide an objective measure of the participants’ arousal during their interactions with the robot, we measured their galvanic skin responses (GSR) using an S220 GSR Sensor from Qubit Systems in Kingston, ON, CA. Several researchers have employed GSR to characterize people’s responses during HRI or to enable a robot to respond to a human’s affective state [21, 28, 35, 39, 45].

When a person reacts to a stressful situation, the sympathetic nervous system is activated. This activation causes the sweat glands in the palms of the hands and soles of the feet to enlarge, which causes the skin to become more conductive. GSR is linearly correlated with arousal [23] and is generally associated with emotional response [4]. An increase in the voltage reading from the GSR sensor is associated with increased arousal. To use our off-the-shelf GSR sensor, we placed the participant’s middle finger and index finger from his or her left hand on the sensor’s two electrode plates and secured them with velcro. We attached leads to the electrodes with alligator clips and recorded the voltage reading using the proprietary software Logger Lite. We also recorded timestamps from a clock synchronized with the robot’s actions.

We analyzed the GSR signal during the 28-second interval between the baseline recordings described in Sect. 5.1 and shown in Fig. 3. During this time interval, we normalized the GSR signal for each participant to have a value between 0 and 1 inclusive using the following equation, as in [25]:

We conducted a gender-balanced, 2×2 between-subjects experiment (see Fig. 2). To test our hypotheses, we defined two independent variables: (1) the type of touch the robot executed (instrumental vs. affective) and (2) the warning condition (warning vs. no warning).

In each of the four treatment conditions, the robot executed the same touching behavior described in Sect. 4.2. The only difference between the instrumental and affective treatment conditions was what the robot said to the participant. The robot used the following utterances:

With this design, each participant experienced very similar physical interactions, but associated different intentions with the interaction, depending upon what the robot said. As we describe in detail in Sect. 5.3.4, we asked questions in order to exclude participants who did not interpret the robot’s intentions correctly, which resulted in the exclusion of six people. We also controlled the length of time the robot spoke to be approximately 7 seconds for all verbal utterances.

For the warning and no warning treatment conditions, we changed the timing of when the robot spoke. Figure 3 illustrates the ordering and timing of the robot’s action and speech in the warning and no warning conditions. For warning, the robot spoke before it touched the participant’s arm. For no warning, the robot touched the participant’s arm and spoke after the haptic interaction was over (i.e. once it was no longer in contact with the participant’s body). We changed the grammatical construction of the utterances to be appropriate for these two cases.

We recruited 63 students from the Georgia Tech campus through various student email lists, flyers, and word of mouth. We required participants to be at least 18 years of age, a United States citizen, and a native English speaker. We excluded six participants because they did not correctly interpret the robot’s intentions (see Sect. 5.3.4) and one participant due to a software malfunction while collecting her questionnaire data. We assigned participants to each of the four treatment groups on a rolling basis according to gender.

In total, we included the data from 56 of the participants (28 males and 28 females) in the analysis for this paper, ranging in age from 18–29 years (M=22.7, SD=2.7). The self-reported ethnicities of these participants were White (31), Asian (19), African American (2), Hispanic (2), Native Amer./Pac. Islander (1), and Other (1). 87.5 % of the participants were engineering students.

We performed our experiment in the Healthcare Robotics Lab in a 4.3 m×3.7 m, climate-controlled simulated hospital room (see Fig. 4). We placed a fully functional Hill-Rom 1000 patient bed, an I.V. pole, an overbed table, a living room chair, and a side table in the room. Participants filled out all paperwork and questionnaires within the simulated hospital room. We placed the robot 17 cm away from the edge of the patient bed.

Two experimenters (the first and second authors of this paper) conducted all of the trials and remained in the room throughout the experiment to ensure the participant’s safety. One experimenter, the first author, ran each experiment by reading from a script. While each trial was taking place, the experimenters sat at the far side of the room and looked at a computer monitor and at the robot, rather than at the participant (see Fig. 4). We used a script and the same two experimenters for all trials in order to maintain consistency and avoid confounding factors.

When a participant arrived at the lab, the experimenters welcomed the participant and introduced themselves. Then, the participant signed a consent form, filled out a demographic survey, and filled out a pre-task questionnaire. Afterward, the experimenter explained that the robot was capable of performing several different simulated nursing duties, and that the robot would mimic doing so by gesturing with its arms and end effectors. It is important to note that the participants were unaware that the robot would reach out and make contact with them. Then, the experimenter asked the participant to lay down on the patient bed, and if a female participant was wearing a skirt, the experimenter offered her a blanket to cover her legs. The experimenter then asked the participant to place his or her right arm between two lines of tape marked on the mattress and to place his or her elbow directly on top of a third line of tape on the mattress. This arm placement ensured that the robot would make contact with the person’s forearm. If the participant were wearing a long-sleeve shirt or sweater, the experimenter asked the participant to roll up his or her sleeve past the elbow or to remove the sweater, if possible. We asked the participant to place his or her left arm on the mattress and affixed a galvanic skin response (GSR) sensor to his or her fingers. We collected one minute of baseline data from this sensor and then asked the participant to fill out a brief questionnaire while laying on the bed (measures are detailed in Sect. 5.3).

We then asked the participant to keep his or her head facing a camera during the experiment. Then, we collected 2 additional minutes of baseline GSR data, initiated the robot interaction (described in Sect. 5.1), and collected another 2 minutes of baseline GSR data. We then asked the participant to get off the bed and fill out the post-task questionnaire for trial 1. Next, we asked the participant to lay down in the bed again and performed a repeated trial of the same interaction he or she had just experienced. Then, we asked the participant to fill out a post-task questionnaire for trial 2.

We measured several subjective variables both before and after the participant interacted with the robot by administering pre-task and post-task questionnaires, respectively. In this section, we describe the measured subjective variables that we use in this paper.

Within this section, we describe the outcomes we would expect if our hypotheses were true.

With respect to the expected outcomes discussed in Sect. 5.4.1, the results were consistent and in support of Hypothesis 1. All 9 dependent measures changed in the anticipated directions, although the changes associated with four of the dependent measures were not statistically significant.

Two dependent measures were significant with the Bonferroni corrected α= .0055. Most importantly, more people agreed with the statement, “I would have preferred that the robot did not touch my arm.” with affective touch than with instrumental touch (10 participants vs. 1 participant), and there was a statistically significant difference (F(1,52)=9.01, p= .004, \(\eta_{p}^{2} = 0.15\)) in the responses to this question. This clearly supports Hypothesis 1. Participants also reported that the instrumental touch was significantly more necessary than the affective touch (F(1,52)=18.29, p>.001, \(\eta_{p}^{2} = 0.26\)). On average, participants viewed the instrumental touch as slightly necessary with a score of M=4.8, SD=1.6 and viewed the affective touch as slightly unnecessary with a score of M=2.9, SD=1.6.

Three other dependent measures were only significant with α= .05. Participants were less aroused during the experiment when the robot performed an instrumental touch compared with when it performed an affective touch (F(1,52)=5.92, p= .018, \(\eta_{p}^{2} = 0.10\)). They also enjoyed the touch more (F(1,52)=4.68, p= .035, \(\eta_{p}^{2} = 0.08\)) and would be more willing to let the robot touch them again when the touch was instrumental as opposed to affective (F(1,52)=7.05, p= .01, \(\eta_{p}^{2} = 0.12\)). These results are also consistent with Hypothesis 1.

On average, participants were generally open to allowing the robot interact with them and touch them again, regardless of the touch type. As shown in Fig. 6, participants reported on average that they would let the robot touch them again for both types of touch. Moreover, all 56 participants allowed the robot to touch them in the second trial.

Surprisingly, with respect to the expected outcomes discussed in Sect. 5.4.2, the results support the contrary assertion that no warning results in more favorable subjective responses. 9 out of the 11 dependent measures relevant to Hypothesis 2 changed in the opposite direction from what we anticipated, although the changes associated with six of these dependent measures were not statistically significant. Only the mean rating of confusion changed in the anticipated direction, since people tended to be more confused in the no warning case, albeit not significantly. The average dominance was identical for the warning and no warning conditions.

Only one dependent measure was significant with the Bonferroni corrected α= .0045. Participants were significantly more aroused when the robot warned them prior to contact (F(1,52)=10.71, p= .002, \(\eta_{p}^{2} = 0.17\)), which is in contradiction to Hypothesis 2.

Two other dependent measures were only significant with α= .05. Participants had a higher positive affect rating when the robot did not warn them (F(1,52)=5.19, p= .027, \(\eta_{p}^{2} = 0.09\)). When the robot warned them, participants had a greater preference for the robot not to touch them (F(1,52)=6.26, p= .016, \(\eta_{p}^{2} = 0.11\)). These results are in opposition to Hypothesis 2. The changes associated with the remaining eight dependent measures were not significant.

In this section, we describe post-hoc analyses of participants’ negative attitudes towards robots, PANAS scores, a repeated interaction with the robot, GSR, and open-ended responses about the interaction.

In our study, the robot touched a relatively innocuous location on the participant’s body. We would anticipate a stronger effect size if the robot were touching a more sensitive part of the body, such as during an actual bed bath. We would also expect the role of perceived intent to play a larger role during contact with more sensitive locations. For example, if a nursing robot made contact with a particularly sensitive part of a patient’s body, the patient’s response would likely depend on whether or not he or she believed the contact was a mistake, was intended to provide comfort, or was intended to achieve a medical goal.

Exploring ways to reinforce desired interpretations of robot-initiated touch could be a worthwhile direction for future research. In our study, we used the robot’s speech, the actions of its arm, and the nursing scenario to convey intent. Many other cues, including high-level, low-level, implicit, and explicit cues, could plausibly be used to influence perceived intent. Alternatively, developing robots to which people are unlikely to attribute intentions may be an effective approach. As we discussed in the related work section (Sect. 3.3), however, this may not be feasible as healthcare robots become more advanced and less specialized.

Studies of interactions between human nurses and human patients have found that patients respond more favorably to instrumental touch. Similarly, with our robot in a simulated healthcare task, participants responded much more favorably to instrumental touch than to affective touch. However, the extent to which this result would generalize to other robots in other scenarios remains an open question.

It may be possible to create a nursing robot from which people would welcome comforting touch and instrumental touch. For example, the robot’s appearance and behavior might be altered to better match the tasks [13, 37]. As we described earlier, Nakagawa et al. have recently shown that a robot touching and wiping the hand of a participant can positively motivate the participant in a task [31]. Many factors may have led to the positive responses they observed. They used a small, cute, child-sized robot designed specifically for social interaction, while we used a human-scale mobile manipulator that we primarily designed to perform instrumental healthcare-related tasks. Their participants were seated upright and looking down at their robot, which was placed on a table. This dominant posture, similar to an adult interacting with a child, is in contrast to the supine posture of our participants who were looking up at the robot from a hospital bed. Cultural differences may also have been a factor, since their participants were from Japan while ours were native English speakers residing in the Atlanta area of the USA. Additionally, their robot asked the participants to first hold its right hand, which resulted in human-initiated touch prior to robot-initiated touch. In our study, the robot made first contact with participants.

All of these factors might make a difference in people’s responses to robot-initiated touch, and some of them would be difficult or impossible for a roboticist to control. For example, robotic nurses may need to interact with people of many cultures who are in a supine posture. Likewise, the demands of instrumental tasks will place requirements on the robot’s design, and may call for human-scale or larger robots.

Fortunately, with respect to the goal of instrumental touch, our results suggest that favorable responses can be achieved with a robot lacking strong social design elements. Participants from the instrumental touch conditions had a generally positive response to the first trial. Notably, only 1 participant out of 28 agreed with the statement “I would have preferred that the robot did not touch my arm.” (see Fig. 10). Whether or not the addition of social design elements would improve responses remains an open question. Some healthcare robots have integrated social design elements, such as the large teddy-bear-like RIBA robot that is designed for lifting patients [30]. One potential risk is that people might not respond well to some socially-oriented design choices in a large healthcare robot. The open-ended responses from our participants indicate that adding human-like characteristics may be beneficial.

We found that participants tended to respond more favorably when no verbal warning was given by the robot prior to contact. The open-ended responses and GSR data suggest that the movement of the robot as it reached out towards the person served as a form of gestural warning, and that it was preferred to the robot’s spoken warning. The robot’s unexpected physical movement and contact with the person after a long period of stillness may have been less jarring than the robot’s unexpected speech after a long period of silence. It seems likely that factors such as the velocity of the arm movement and the loudness of the speech play a role in this type of interaction. As suggested in participants’ open-ended responses, having the robot speak to the person ahead of time in a more natural manner, or otherwise reducing surprise, might lead to different results.

Another interpretation of our results could be that leaving the intent behind robot-initiated touch ambiguous while the interaction is occurring leads to more favorable responses. The robot explicitly stating its high-level intentions may have caused apprehension for the participants. This would seem to go against common bedside manner as practiced by human nurses. The speech prior to the robot’s actions may have also resulted in a stronger tendency for participants to anthropomorphize the robot, which may have influenced their responses to the robot. Similarly, no warning may have resulted in participants perceiving the robot as more machine-like. Interestingly, in the open-ended responses, 11 participants who had not been warned stated that they would have liked to have been warned.

Further research will be required to confidently interpret these surprising results. For now, our results suggest that verbal warnings prior to contact should be carefully designed, if used at all, and that gesture can serve as a form of warning. When a robot should perform low-level communication of intention via gestures versus high-level communication of intention via speech remains an open question.

Our post-hoc analyses suggest that participants who have less positive attitudes towards emotions in interactions with robots as measured by NARS respond less favorably to robot-initiated touch. These results suggest that robot-initiated touch is related to emotional elements of interaction, and S3 subscale scores could be informative when a robot engages a person in such an interaction. However, we administered the NARS instrument after the experiment in order to avoid biasing the participants. As such, we found an association in the responses to NARS and other measures after the interaction, but we do not know if NARS responses collected prior to the interaction would have been predictive.

Perhaps the most notable result from the repeated trial is that all participants allowed the robot to touch them again. Participants responded less favorably to the second interaction with the robot. It is likely that the nature of the repeated experiment resulted in people responding less favorably. The total experiment with both trials took approximately 45 minutes, included long periods of waiting, and involved filling out numerous questionnaires. In addition, the second trial was identical to the first, and consequently could have been less interesting to the participants.

Our results provide evidence that robot-initiated touch can be a practical form of human-robot interaction. Procedures associated with health and medicine often entail discomfort, such as when having blood drawn, receiving a bed bath, undergoing dental work, or being in the confined space of an MRI scanner. Our results suggest that robot-initiated touch can play a role in healthcare but that it need not be distressing for patients. However, further research will be required to generalize our results to real patients, including patients from other demographics who have not explicitly chosen to interact with a robot.