Self-administered questionnaires enhance emotion estimation of individuals with autism spectrum disorders in a robotic interview setting.

Background: Robots offer many unique opportunities for helping individuals with autism spectrum disorders (ASD). Determining the optimal motion of robots when interacting with individuals with ASD is important for achieving more natural human-robot interactions and for exploiting the full potential of robotic interventions. Most prior studies have used supervised machine learning (ML) of user behavioral data to enable robot perception of affective states (i.e., arousal and valence) and engagement. It has previously been suggested that including personal demographic information in the identification of individuals with ASD is important for developing an automated system to perceive individual affective states and engagement. In this study, we hypothesized that assessing self-administered questionnaire data would contribute to the development of an automated estimation of the affective state and engagement when individuals with ASD are interviewed by an Android robot, which will be linked to implementing long-term interventions and maintaining the motivation of participants. Methods: Participants sat across a table from an android robot that played the role of the interviewer. Each participant underwent a mock job interview. Twenty-five participants with ASD (males 22, females 3, average chronological age = 22.8, average IQ = 94.04) completed the experiment. We collected multimodal data (i.e., audio, motion, gaze, and self-administered questionnaire data) to train a model to correctly classify the state of individuals with ASD when interviewed by an android robot. We demonstrated the technical feasibility of using ML to enable robot perception of affect and engagement of individuals with ASD based on multimodal data. Results: For arousal and engagement, the area under the curve (AUC) values of the model estimates and expert coding were relatively high. Overall, the AUC values of arousal, valence, and engagement were improved by including self-administered questionnaire data in the classification. Discussion: These findings support the hypothesis that assessing self-administered questionnaire data contributes to the development of an automated estimation of an individual's affective state and engagement. Given the efficacy of including self-administered questionnaire data, future studies should confirm the effectiveness of such long-term intervention with a robot to maintain participants' motivation based on the proposed method of emotion estimation.

Social skills training using multiple humanoid robots for individuals with autism spectrum conditions.

Introduction: Social skills training (SST) is used to help individuals with autism spectrum conditions (ASC) better understand the perspectives of others and social interactions, develop empathy skills, and learn how to engage with others socially. However, many individuals with ASC cannot easily sustain high motivation and concentration during such an intervention when it is administered by humans. We developed a social skills training program using multiple humanoid robots (STUH), including an android robot, that aimed to enable individuals with ASC to become familiar with the perspectives of others and improve their sociability and empathy skills. The objective of the present study was to investigate the effectiveness of STUH for these individuals. Methods: In STUH, we prepared 50 social exercises that consisted of conversations and behavioral interactions between an android robot and a simple humanoid robot. We prepared another humanoid robot that featured a cartoon-like and mechanical design, which played the role of host. In the first half-session of STUH, participants worked on the exercise from the perspective of an outsider. In the second half-session of STUH, they simulated experience by using robots as their avatars. The intervention associated with STUH was conducted for five days in total. We conducted an analysis of variance (ANOVA) featuring the intervention time point as the independent variable to examine changes in each score on the sociability index items. Results: In total, 14 individuals with ASC participated in the study. The results of multiple comparison tests using the Bonferroni method indicated that all sociability index items improved between preintervention and follow-up. Our program enabled the participants to become familiar with the perspectives of others and improve their sociability. Discussion: Given the promising results of this study, future studies featuring long-term follow-up should be conducted to draw definitive conclusions about the efficacy of our training system.

Android Robot Promotes Disclosure of Negative Narratives by Individuals With Autism Spectrum Disorders.

Many individuals with autism spectrum disorders (ASD) demonstrate some challenges with personal narrative writing. Sentence completion tests (SCT) is a class of semi-structured projective techniques and encourage respondents to disclose their private narratives. Even in SCT, only providing beginning of sentences is inadequate to compensate atypicalities in their creativity and imagination, and self-disclosure is difficult for many individuals with ASD. It is reported that many individuals with ASD often achieve a higher degree of task engagement through interactions with robots and that robotic systems may be useful in eliciting and promoting social communication such as self-disclosure for some individuals with ASD. There is a possibility that exemplification by android robots in place of human interviewers can result in a higher degree of task engagement for individuals with ASD. The objective of this study was to investigate whether additional exemplifications by android robots in the SCT can prompt self-disclosure for individuals with ASD. We compared the difference in disclosure statements and subjective emotion in the testing paper of the SCT in additional exemplification by an android robot and a human interviewer. In addition, we assessed the disclosure statements and subjective emotions in the SCT, for which exemplifications were written on testing paper to make the comparison. Our quantitative data suggested that exemplification by android robot promoted more self-disclosure, especially about the negative topic compared to exemplification by a human interviewer and that written on test paper. In addition, the level of participant embarrassment in response to exemplification by the android robot seemed to be lower compared to that in the human interviewer condition. In the assessment and support for individuals with ASD, eliciting self-disclosure is a pressing issue. It is hoped that the appropriate use of robots will lead to a better understanding and support for their application.

Differences in the Optimal Motion of Android Robots for the Ease of Communications Among Individuals With Autism Spectrum Disorders.

Android robots are employed in various fields. Many individuals with autism spectrum disorders (ASD) have the motivation and aptitude for using such robots. Interactions with these robots are structured to resemble social situations in which certain social behaviors can occur and to simulate daily life. Considering that individuals with ASD have strong likes and dislikes, ensuring not only the optimal appearance but also the optimal motion of robots is important to achieve smooth interaction and to draw out the potential of robotic interventions. We investigated whether individuals with ASD found it easier to talk to an android robot with little motion (i.e., only opening and closing its mouth during speech) or an android robot with much motion (i.e., in addition to opening and closing its mouth during speech, moving its eyes from side to side and up and down, blinking, deeply breathing, and turning or moving its head or body at random). This was a crossover study in which a total of 25 participants with ASD experienced mock interviews conducted by an android robot with much spontaneous facial and bodily motion and an android robot with little motion. We compared demographic data between participants who answered that the android robot with much motion was easier to talk to than android robot with little motion and those who answered the opposite. In addition, we investigated how each type of demographic data was related to participants' feeling of comfort in an interview setting with an android robot. Fourteen participants indicated that the android robot with little motion was easier to talk to than the robot with much motion, whereas 11 participants answered the opposite. There were significant differences between these two groups in the sensory sensitivity score, which reflects the tendency to show a low neurological threshold. In addition, we found correlations between the sensation seeking score, which reflects the tendency to show a high neurological threshold, and self-report ratings of comfort in each condition. These results provide preliminary support for the importance of setting the motion of an android robot considering the sensory traits of ASD.

Simple Three-Dimensional Motion Measurement System using Marker-IMU System.

Image-based motion capture system have a limited measurement range, and the inertial motion capture system cannot directly acquire the position information. In addition, simple and robust measurement is required in the realization field, but it is difficult with the conventional motion capture system. Therefore, in this research, we constructed a system that robustly measures human movements by combining images and IMUs. High-accuracy visual markers were used to measure the position and orientation by images. By combining this with an IMU, we have established a robust measurement method even for hiding. In addition, by using the environmental reference marker, it is possible to acquire the absolute position even if the camera moves. In fact, we measured the movement and walking movements of human upper limbs, and realized continuous and smooth movement measurement by this method.

Group-Based Online Job Interview Training Program Using Virtual Robot for Individuals With Autism Spectrum Disorders.

The rapid expansion of online job interviews during the COVID-19 pandemic is expected to continue after the pandemic has subsided. These interviews are a significant barrier for individuals with autism spectrum disorders (ASD). There is little evidence-based training for online job interviews for individuals with ASD, and the development of new trainings is expected. In an effort to facilitate online job interview skill acquisition for individuals with ASD, we developed a group-based online job interview training program using a virtual robot (GOT). In GOT, the interviewer and interviewee are projected as virtual robots on the screen. Five participants were grouped and performed the role of interviewee, interviewer, and evaluator. The participants performed all roles in a random order. Each session consisted of a first job interview session, feedback session, and second job interview session. The participants experienced 25 sessions. Before and after GOT, the participants underwent a mock online job interview with a human professional interviewer (MOH) to evaluate the effect of GOT. In total, 15 individuals with ASD took part in the study. The GOT improved self-confidence, motivation, the understanding of others' perspectives, verbal competence, non-verbal competence, and interview performance scores. There was also a significant increase in the recognition of the importance of the point of view of interviewers and evaluators after the second MOH compared to after the first MOH. Using a VR robot and learning the importance of interview skills by experiencing other perspectives (i.e., viewpoint of interviewer and evaluator) may have sustained their motivation and enabled greater self-confidence. Given the promising results of this study and to draw definitive conclusions regarding the efficacy of virtual reality (VR) robots for mock online job interview training, further studies with larger, more diverse samples of individuals with ASD using a longitudinal design are warranted.

Telmisartan Exacerbates Cisplatin-Induced Nephrotoxicity in a Mouse Model.

Cisplatin (CDDP; cis-diamine dichloroplatinum)-induced nephrotoxicity is the main reason for dose limitations, which can reduce the efficacy of cancer treatment. Lower blood pressure and administration of renin angiotensin system (RAS) inhibitors have been reported as factors that exacerbate CDDP-induced nephrotoxicity; however, the detailed mechanisms remain unknown and the results of previous studies are conflicting. In this study, we examined the influence of various hypotensive drugs, including RAS inhibitors and calcium channel blockers, on CDDP-induced nephrotoxicity in BALB/c mice. The mice were divided into nine groups: (1) CDDP group (15 mg/kg CDDP), (2) AML group (5 mg/kg amlodipine), (3) ENA group (2.5 mg/kg enalapril), (4) telmisartan (TEL) group (10 mg/kg telmisartan), (5) LOS group (10 mg/kg losartan), (6) CDDP + AML group, (7) CDDP + ENA group, (8) CDDP + TEL group, and (9) CDDP + LOS group. Nephrotoxicity was evaluated by measuring serum creatinine (CRE) and blood urea nitrogen (BUN) levels. In addition, the kidney sections were stained with Masson's trichrome and terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick-end labeling (TUNEL) to assess the renal fibrosis area and apoptotic area. Serum CRE and BUN levels were increased in the CDDP + ENA, CDDP + LOS, and CDDP + TEL groups compared to those in the CDDP alone group, and the CDDP + AML group showed an increasing trend. However, there was no correlation between ∆CRE or ∆BUN levels and ∆ systolic blood pressure. The CDDP + TEL group showed a significant increase in the renal fibrosis area. These results suggest that exacerbation of CDDP-induced nephrotoxicity is not correlated with systolic blood pressure but is associated with administration of RAS inhibitors, particularly TEL.

Optimal robot for intervention for individuals with autism spectrum disorders.

With recent rapid advances in technology, human-like robots have begun functioning in a variety of ways. As increasing anecdotal evidence suggests, robots may offer many unique opportunities for helping individuals with autism spectrum disorders (ASD). Individuals with ASD often achieve a higher degree of task engagement through the interaction with robots than through interactions with human trainees. The type and form of robots to be used for individuals with ASD have been meticulously considered. Simple robots and animal robots are acceptable because of their simplicity and the ease of interesting and engaging interactions. Android robots have the benefit of the potential of generalization into daily life to some extent. Considering the affinity between robots and users is important to draw out the potential capabilities of robotic intervention to the fullest extent. In the robotic condition, factors such as the appearance, biological motion, clothes, hairstyle, and disposition are important. Many factors of a user, such as age, sex, and IQ, may also affect the affinity of individuals with ASD toward a robot. The potential end-users of this technology may be unaware or unconvinced of the potential roles of robots in ASD interventions. If trainers have extensive experience in using robots, they can identify many potential roles of robots based on their experience. To date, only a few studies have been conducted in the field of robotics for providing assistance to individuals with ASD, and future studies are needed to realize an optimal robot for this purpose.

Enhancing Communication Skills of Individuals With Autism Spectrum Disorders While Maintaining Social Distancing Using Two Tele-Operated Robots.

COVID-19 has affected many areas of daily life, including communication and learning. Social distancing is essential to prevent the spread of COVID-19. In these situations, teaching communication skills is essential for helping individuals with autism spectrum disorders (ASD) reach their full potential. To provide communication education while maintaining social distancing, we developed a communication training system using a tele-operated robot. In this system, we prepared a PC and a robot for each participant. The participants were grouped in pairs and communicated with each other through the tele-operated robot. The objective of this study was to test whether this system can maintain motivation for training in individuals with ASD and whether our system was useful for improving communication skills. Participants were randomly assigned to one of two groups: the taking a class by teachers alone (TCT) group or robot-mediated communication exercise (RMC) group. Participants in the TCT group took a class about communication skills from their teacher. Participants in the RMC group, in addition to taking a class by teacher, were grouped in pairs and communicated with each other through the tele-operated robot once a week over 4 weeks (for a total of five sessions). In total, twenty individuals with ASD participated in the study. One-way ANOVA revealed that there were significantly greater improvements in being good at describing their thoughts to others, which was self-rated (F = 6.583; p = 0.019), and good at listening to the thoughts or feelings of others, which was rated by themselves (F = 5.635; p = 0.029) and their teacher (F = 5.333; p = 0.033). As expected, the motivation for training using this system was maintained during a session. Overall, this study revealed that our system was useful for improving communication skills (e.g., listening to the thoughts or feelings of others). Teaching communication skills under pandemic conditions is important, and this study demonstrated the feasibility of communication training using tele-operated robots.

Relaxing Gaze Aversion of Adolescents With Autism Spectrum Disorder in Consecutive Conversations With Human and Android Robot-A Preliminary Study.

Establishing a treatment method for individuals with autism spectrum disorder (ASD) not only to increase their frequency or duration of eye contact but also to maintain it after ceasing the intervention, and furthermore generalize it across communication partners, is a formidable challenge. Android robots, which are a type of humanoid robot with appearances quite similar to that of humans, are expected to adapt to the role of training partners of face-to-face communication for individuals with ASD and to create easier experiences transferrable to humans. To evaluate this possibility, four male adolescents with ASD and six without ASD were asked to participate a pilot experiment in which there were consecutive sessions of semistructured conversation where they alternately faced either a human female or a female-type android robot interlocutor five times in total. Although it is limited by the small sample size, the preliminary results of analysis of their fixation pattern during the conversations indicated positive signs; the subjects tended to look more at the face of the android robot than that of the human interlocutor regardless of whether they had ASD. However, the individuals with ASD looked more at the area around the eyes of the android robot than at the human, and also looked less at that of the human than the individuals without ASD did. An increasing tendency of looking at the area around the human eyes, which could be a positive sign of the transferability of the experiences with an android robot to a human interlocutor, was only weakly observed as the sessions progressed.

How the Realism of Robot Is Needed for Individuals With Autism Spectrum Disorders in an Interview Setting.

The preliminary efficacy of interview training using an android robot whose appearance and movements resemble those of an actual human for treating social and communication difficulties in individuals with autism spectrum disorders (ASD) has been demonstrated. Patient preferences regarding the appearance of robots are crucial for incentivizing them to undergo robot-assisted therapy. However, very little is known about how the realistic nature of an android robot is related to incentivizing individuals with ASD in an interview setting. In this study, individuals with ASD underwent an interview with a human interviewer and an android robot. Twenty-three individuals with ASD (age, 17-25 years) participated in this study. After the interview, the participants were evaluated in terms of their motivation to practice an interview with an android robot and their impression of the nature of the android robot in terms of humanness. As expected, subjects exhibited higher motivation to undergo interview training with an android robot than with a human interviewer. Higher motivation to undergo an interview with the android robot was negatively correlated with the participants' impressions of the extent to which the android robot exhibited humanness. This study brings us one step closer to understanding how such an android robot should be designed and implemented to provide sufficiently realistic interview training that can be of therapeutic value.

Role-Play-Based Guidance for Job Interviews Using an Android Robot for Individuals With Autism Spectrum Disorders.

Interventions for job interviews targeting the impaired theory of mind observed in individuals with autism spectrum disorders (ASD) are limited. We developed a role-play-based guidance system for job interviews using an android robot resembling a real person. Individuals with ASD worked in pairs and played dual roles in mock job interviews. Specifically, one participant acted as the interviewee, while the other participant operated the android robot and acted as the interviewer. Eight individuals with high-functioning ASD participated in this study. After the training sessions, participants learned to understand the point of view of the interviewer, which may contribute to increased recognition of the importance of gestures and the motivation to learn how to behave in a job interview. In addition, participants reported improved self-confidence. These results provide preliminary support for the efficacy of playing dual roles using android robots.

Job interview training targeting nonverbal communication using an android robot for individuals with autism spectrum disorder.

Job interviews are significant barriers for individuals with autism spectrum disorder because these individuals lack good nonverbal communication skills. We developed a job interview training program using an android robot. The job interview training program using an android robot consists the following three stages: (1) tele-operating an android robot and conversing with others through the android robot, (2) a face-to-face mock job interview with the android robot, and (3) feedback based on the mock job interview and nonverbal communication exercises using the android robot. The participants were randomly assigned to the following two groups: one group received a combined intervention with "interview guidance by teachers and job interview training program using an android robot" (n = 13), and the other group received an intervention with interview guidance by teachers alone (n = 16). Before and after the intervention, the participants in both groups underwent a mock job interview with a human interviewer, who provided outcome measurements of nonverbal communication, self-confidence, and salivary cortisol. After the training sessions, the participants who received the combined interview guidance by teachers and the job interview training program using an android robot intervention displayed improved nonverbal communication skills and self-confidence and had significantly lower levels of salivary cortisol than the participants who only received interview guidance by teachers. The job interview training program using an android robot improved various measures of job interview skills in individuals with autism spectrum disorder.

Estimating Movements of Human Body for the Shirt-Type Wearable Device Mounted on the Strain Sensors Based on Convolutional Neural Networks.

To measure the life log of humans and enjoy virtual or augmented reality video games, several wearable devices have been developed that allow users to intuitively input commands. However, monitoring and estimating three-dimensional human motions for extended periods using the wearable devices is difficult. Therefore, this study aims to develop a method that estimates the joint angles of the upper human body using a wearable suit implanted with strain sensors with a nonlinear characteristic. We used a convolutional neural network (CNN) to estimate the joint angles. We established a CNN estimator based on the training data of two adult males and confirmed that this estimator could estimate the joint angles of other adult males. To monitor the caretakers in a care facility, we measure the care-working motion, such as motions that care workers transform the elder persons, estimate each joint angle, and visualize the motions on Unity.

Brief Report: Evaluating the Utility of Varied Technological Agents to Elicit Social Attention from Children with Autism Spectrum Disorders.

Technological agents could be effective tools to be used in interventions for enhancing social orienting for some young children with ASD. We examined response to social bids in preschool children with ASD and typical development (TD) at a very early age (i.e., around 3 years) using social prompts presented by technological agents of various forms and human comparisons. Children with ASD demonstrated less response overall to social bids compared to TD controls, across agents or human. They responded more often to a simple humanoid robot and the simple avatar compared to the human. These results support the potential utilization of specific robotic and technological agents for harnessing and potentially increasing motivation to socially-relevant behaviors in some young children with ASD.

Validation of Improvements of Robotic Devices for Nursing Care using a Sensing Dummy Simulating Human Body.

The population in advanced countries is rapidly aging, and these countries are faced with various issues pertaining to the rapidly aging population. It is expected that manpower shortages for nursing care of the elderly will be resolved using robotic devices. A method is necessary for quantifying the load of the human body that is envisaged to encourage the widespread use of robotic devices for nursing care. Therefore, we developed a sensing human dummy consisted of several sensors that emulated the stiffness of the human body and its shape. In a previous study, we developed the buttock dummy that consisted of emulated bones and soft tissue. In this study, we mounted force sensors, and emulated blade bones and soft skin. We compared the robotic beds "Resyone" with "Resyone Plus" and showed that the latter elicited improved comfort with respect to the former. In these experiments, we used the sensing dummy and the visual markers to evaluate the load and the posture of users. From the validation experiments, the shear force of the sacral bone of Resyone Plus was determined to be lower than that for Resyone owing to the movement of the foot.

The impact of robotic intervention on joint attention in children with autism spectrum disorders.

Background: A growing body of anecdotal evidence indicates that the use of robots may provide unique opportunities for assisting children with autism spectrum disorders (ASD). However, previous studies investigating the effects of interventions using robots on joint attention (JA) in children with ASD have shown insufficient results. The robots used in these studies could not turn their eyes, which was a limitation preventing the robot from resembling a human agent. Methods: We compared the behavior of children with ASD with that of children with typical development (TD) during a JA elicitation task while the children interacted with either a human or a robotic agent. We used the robot "CommU," which has clear eyes and can turn its eyes, for the robotic intervention. The age range of the participants was limited to 5-6 years. Results: Sixty-eight participants participated in this study, including 30 (10 females and 20 males) children with ASD and 38 (13 females and 25 males) children with TD. The participants were randomly assigned to one of the following two groups: the robotic intervention group or the control group. JA in the children with ASD was better during the robotic intervention than during the human agent intervention. These children exhibited improved performance in the JA task with human after interacting with the robot CommU. JA was differentially facilitated by the human and robotic agents between the ASD and TD children. Conclusions: The findings of this study significantly contribute to the literature on the impact of robots on JA and provide information regarding the suitability of specific robot types for therapeutic use.

Can Robotic Systems Promote Self-Disclosure in Adolescents with Autism Spectrum Disorder? A Pilot Study.

Research suggests that many individuals with autism spectrum disorder (ASD) often demonstrate challenges providing appropriate levels of information during conversational interchanges. Considering the preference of individuals with ASD, and recent rapid technological advances, robotic systems may yield promise in promoting certain aspects of conversation and interaction such as self-disclosure of appropriate personal information. In the current work, we evaluated personal disclosures of events with specific emotional content across two differing robotic systems (android and simplistic humanoid) and human interactions. Nineteen participants were enrolled in this study: 11 (2 women and 9 men) adolescents with ASD and 8 (4 women and 4 men) adolescents with TD. Each participant completed a sequence of three interactions in a random order. Results indicated differences regarding comfort level and length of disclosures between adolescents with ASD and typically developing (TD) controls in relation to system interactions. Specifically, adolescents with ASD showed a preference for interacting with the robotic systems compared to TD controls and demonstrated lengthier disclosures when interacting with the visually simple humanoid robot compared to interacting with human interviewer. The findings suggest that robotic systems may be useful in eliciting and promoting aspects of social communication such as self-disclosure for some individuals with ASD.