# Expectation vs. Reality: Attitudes Towards a Socially Assistive Robot in Cardiac Rehabilitation
## 2 Background
### User Acceptance Assessment  
Technology acceptance has been commonly described as the favorable reception and ongoing use of newly introduced devices and systems [26]. The Technology Acceptance Model (TAM) [27] has been adopted as one of the bases to evaluate acceptance in different applications (e.g., e-commerce acceptance \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline
**Type of Interaction** & **Behavior** & **Description** \\ \hline Patient–Robot indirect interaction & verbal and gestural feedback & The robot performs different poses to express the patient that the work done is well. Phrases as “You are doing better today”, “Do not give up” and “You can do it”, are randomly chosen in order to positive influence the patient. \\ \hline Patient–Robot direct interaction & verbal feedback & The patient registers the Borg scale (6—low exertion, to 20—high exertion) and it is compared with the high rate. The robot manages the following cases: When there are a normal heart rate and the patient perceives a low exertion, the robot says, “Thank you, it seems everything is fine”. When there is a high heart rate and the patient perceives a high exertion, the robot uses the Therapist–Robot interaction type. When there is a high heart rate and the patient perceives a low exertion or the inverse way. The robot says. “Are you OK?, please register again the Borg scale.” If the heart rate continues having high values the Therapist–Robot interaction is activated. \\ \hline Therapist–Robot interaction & verbal and gestural feedback & The robot gives advice to the therapist when an emergency event occurs. In the case of the Cardiac Rehabilitation program, when the heart rate increases too much and exceeds the maximum heart rate allowed (configured by the medical staff). The robot calls the therapist shaking its hands and also uses verbal phrases such as: “Doctor, the patient is feeling tired, can you come?” and “Doctor, the patient needs help.” The end of this behavior is triggered when the doctor/therapist touches the robot head. \\ \hline \end{tabular}
\end{table} 
Table 2: Robot type of interactions. model (EAM) to evaluate the technology acceptance associated to mobile health devices [28]). However, the TAM has been criticized as it lacks precision and ignores influential factors (e.g., complexity of the technology and user characteristics) that are relevant on many applications [26]. On response of this limitation, in [17], the Unified Theory of Acceptance and Use of Technology (UTAUT) was developed. This model has been successfully used in healthcare to evaluate different applications. For example, acceptance of web-based aftercare devices [29], therapist acceptance of new technology for rehabilitation [30], new models based on UTAUT for rehabilitation technologies [31], among others. In the context of social robotic agents, different models have been used. For a physical robotic coach the Coach-Athlete Relationship Questionnaire (CART-Q) was used to evaluate relations between the robotic agent and the athlete [32]. Furthermore, in [33], the Godspeed questionnaire was implemented to assess different perceptions of the robot working as a trainer assistant. Similarly, the UTAUT has been also used to evaluate acceptance of social robots, such as the study presented in [34], where two scenarios were evaluated (i.e., an expressive robot personality vs. an introvert robot personality). With this model, the authors found that participants were more likely to accept the expressive personality over the introvert. Those evidences position the UTAUT model as a reliable basis to develop models that adjust better to the assessment of social robots. However, people perceive social autonomous robots differently from other computer technologies due to the nature of the interaction (i.e., social robots seek to interact as humans do) [35]. Therefore, in some cases, conventional models of perception are limited, which have led researchers to develop adaptations of these models to meet their needs [5; 12; 16; 36].