Advances in Neurodevelopmental Disorders https://doi.org/10.1007/s41252-018-0066-4
ORIGINAL PAPER
Using a Humanoid Robot as a Complement to Interventions for Children with Autism Spectrum Disorder: a Pilot Study Lorenzo Desideri 1,2 & Marco Negrini 3 & Massimiliano Malavasi 1,3 & Daniela Tanzini 3 & Aziz Rouame 3 & Maria Cristina Cutrone 4 & Paola Bonifacci 2 & Evert-Jan Hoogerwerf 1,3
# Springer International Publishing AG, part of Springer Nature 2018
Abstract Emerging evidence documents that social robots may increase motivation in children with autism spectrum disorder (ASD) when participating in educational activities. This study reports on the results of a pilot test conducted in a public child and adolescent mental health service (CAMHS) aimed at exploring whether a social robot could increase engagement and learning achievement in two 9-year-old male children with ASD with accompanying intellectual disability, language and communication impairments, and low adaptive skills. Using an ABA1 single-case design, children participated in educational sessions targeting developmental and social skills (e.g., motor imitation, expressive/receptive language, spontaneous requests). The results indicated that interacting with a social robot enhanced engagement (d = 0.78) and goal achievement in one case (d = 2.19), and only goal achievement in the second case (d = 2). The results from the present investigation are discussed in light of their implications for the design of a more robust translational research protocol aimed at assessing the effectiveness of robot-based ASD intervention scenarios. Keywords Robotics . Autism spectrum disorder . Human-robot interaction . Education
Advances in assistive and information technologies are changing the ways mental health services will be delivered in the near future (National Institute of Health 2017), and there are no doubts that innovations in the field of robotics will be one of the main drivers of such change (Rabbitt et al. 2015). Social robots are considered Brelational artifacts^ (Turkle et al. 2006) that differ from other information technologies since they can physically interact with real-world objects and people through verbal, nonverbal, or affective modalities (Breazeal 2003). Accumulating evidence indicates that people experience physical interactions with robots as more engaging and
* Lorenzo Desideri
[email protected] 1
Regional Centre for Assistive Technology, ASL Bologna, Az, Via Sant’Isaia, 90, 40123 Bologna, Italy
2
Department of Psychology, University of Bologna, Bologna, Italy
3
AIAS Bologna, Bologna, Italy
4
UOSD Programma Integrato Disabilità e Salute, ASL Bologna, Bologna, Italy
motivating than interactions with other screen-based information technologies (Matarić 2017), probably because social robots are perceived as social entities that evoke people’s emotional reactions potentially leading to specific emotional bonds between human and machine (Eyssel 2017). Socially assistive robotics (SAR) is a term that indicates the use of social robots whose purpose is to provide assistance to human users by facilitating social interactions (Feil-Seifer and Mataric 2005; Matarić 2017). In this perspective, SAR has been successfully applied for a variety of intents in mental healthcare scenarios (Riek 2015), most notably as companion robots for older adults, to improve psychosocial outcomes (Bemelmans et al. 2012) and prevent cognitive decline (Shibata and Wada 2011), or to improve the effectiveness of interventions targeting children with neurodevelopmental disorders, such as autism spectrum disorders (hereafter ASD; for a recent review, see Pennisi et al. 2016). According to the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association 2013), children with ASD have persistent deficits in social communication and interaction across multiple contexts; they also present restricted, repetitive patterns of behavior, e.g., stereotyped or repetitive motor movements or adherence to routines.
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They find it difficult to recognize body language, to make eye contact, to talk about personal feelings, and to understand other people’s emotions (Lord and Bishop 2015). Given these impairments, health and educational professionals often struggle in motivating and engaging children with ASD in treatment and learning activities (Weiss and Harris 2001). Engagement in a variety of social, play, educational, and therapeutic activities is crucial to acquiring knowledge critical to cognitive and social development (Corsello 2005; Iovannone et al. 2003). Children with ASD show greater difficulties in engaging than their typically developing peers or children with other developmental disabilities (Simpson et al. 2013), and this increases with ASD severity (Kishida et al. 2008). Nevertheless, several studies suggest that children with ASD can successfully engage in social interactions and other activities if such activities are presented in an attractive manner (Simut et al. 2016). Recent literature has highlighted that children with ASD appear to have a special interest in computerized activities (Grynszpan et al. 2014), and has identified the advantages that computers provide with regard to the core deficits of ASD. These include consistency in a clearly defined task and the usually specific focus of attention due to reduced distractions from unnecessary sensory stimuli (Grynszpan et al. 2014). In this vein, a variety of information technologies have been used to engage and motivate children with ASD in therapeutic and play scenarios over the last decade (Shic and Goodwin 2015), with current evidence suggesting that children with ASD show a preference for robotic devices over non-robotic objects and humans (Simut et al. 2016). This has encouraged researchers to introduce different robotic devices (e.g., human-like, animal-like, featureless) into treatment interventions targeting children with ASD, thus opening promising new scenarios for their treatment and education (Begum et al. 2016; Scassellati et al. 2012). Specifically, the use of robots, with characteristics differing from people’s social behavior—which can be very subtle and unpredictable— may contribute to Ba simplified, safe, predictable and reliable environment where the complexity of interaction can be controlled and gradually increased^ (Robins et al. 2005; p. 108). In particular, social robots may promote children’s engagement in therapy-like scenarios by taking on the role, for example, of embedded reinforcer (Kim et al. 2013) or interaction partner, or by mediating the relationship between the therapist and the child with ASD (Huijnen et al. 2016; Robins et al. 2010). The aim, thus, is to draw the child’s attention and interests towards both the robot and the tasks, while maintaining the prolonged therapy or educational sessions (Rudovic et al. 2017). Regardless of robot appearance and capabilities, overall positive effects of robot presence on the engagement of children with ASD in proposed activities have been found for interventions targeting the development of communication
skills (Dautenhahn and Werry 2004), imitative behaviors (Pierno et al. 2008; Suzuki et al. 2017), and collaborative behaviors (Wainer et al. 2010). Less clear, however, is whether improving the engagement of children with ASD in robotbased intervention scenarios necessarily leads to an improvement in intervention outcomes. The evidence gathered so far on the extent to which robots may represent an added value in ASD interventions is indeed still considered mixed, and is mainly based on small-sample case studies (Begum et al. 2016; Broadbent 2017; Diehl et al. 2012); therefore, it is still questionable whether robots may be considered useful in addressing core vulnerabilities related to ASD (Begum et al. 2016). For example, Pop et al. (2014) found that children with ASD did not perform better in a functional play task with a robot than with an adult interaction partner. In addition, the study also showed that children initiated less verbal communication in the robot group than in the adult interaction group, even if their level of engagement was higher when interacting with the robot than with an adult interlocutor (for similar mixed results, see also Simut et al. 2016; Tapus et al. 2012). To summarize, despite their showing great promise, concerns remain regarding the potential of robots in clinical as well as educational practices. As noted by Oldenziel et al. (2005), the interest in adopting new technologies quickly decreases when existing practices require too many changes, and this may be especially true for the introduction of robotic applications in several fields, including education and health care (Royakkers and van Est 2015). We assume here that professionals involved in the care and treatment of children with ASD need to acquire firsthand experience of the devices available, so that they may better understand the possible roles of social robots in clinical practice. In this context, a recent single-case research study conducted in a public child and adolescent mental health service (CAMHS) and involving three children with ASD with mild intellectual disabilities and mild adaptive behavior skill deficits. (Desideri et al. 2017) showed the feasibility of introducing a humanoid robot into a clinical setting. Overall, the results suggested that introduction of a robot is not detrimental to ASD educational interventions; in one case, it led to a significant increase in both engagement and goal achievement (Desideri et al. 2017). Although it showed promising results, the study, along with other studies involving larger samples (e.g., Simut et al. 2016), left open the question as to whether a robot could serve as a facilitator also in interventions targeting children with more severe forms of ASD. The current study reports on a pre-clinical investigation conducted within an Italian CAMHS to explore whether a humanoid robot could be used as a complement to usual educational interventions to heighten engagement and learning outcomes in two children with ASD and severe to profound intellectual and developmental disabilities.
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Method Participants Two children with ASD participated in the study. Inclusion criteria were (1) age 4–10 years; (2) a diagnosis of ASD from the local CAMHS; (3) a diagnosis of severe to profound intellectual disability from a clinician working in the local public ASD service; and (4) no previous exposure to a humanoid robot. Table 1 reports the participants’ demographic and clinical information. Their level of intellectual disability was considered severe to profound by expert clinicians since no formal cognitive testing was available at the time of the present study. Neither participant had prior experience with any humanoid or non-humanoid robot. Parental consent was obtained for each child before the study took place. Parents had the right to stop their child’s participation at any time during the intervention. For both cases, target behaviors were selected and developed in collaboration with the clinical psychologist of the CAMHS multidisciplinary team. Case 1 received a diagnosis of pervasive developmental disorder - not otherwise specified (ICD 10: F84.9) associated to profound intellectual disability, macrocephaly, severe bilateral hearing loss, and dysgenesis of the corpus callosum. He wears hearing aids and is able to orient his attention towards the source of the sound. He has severe limitations in interpersonal communication and personal autonomy. Verbal language is absent, and intentional communication is rare. He is more interested in interacting with objects than with other people, and he shows stereotyped movements of the arms when positively aroused. He is particularly attracted by sounds and colored lights. The interventions provided in the present study included educational activities to target the Table 1 Study participants’ demographic and clinical information
subsequent motor imitation, to stimulate a reaction to the child’s name, and to stimulate expressive and receptive language. Case 2 (ICD 10: F84.0) has limited verbal skills and severe intellectual disability. He makes simple requests, names everyday objects, uses phrase speech, and understands very simple requests. Eye contact is seldom present and he occasionally reacts to his name. When positively excited, he shows stereotyped movements of the arms. In contrast, frustration is expressed with task avoidance. Interactions with his peers are very limited. The educational activities available within the sessions were aimed at stimulating expressive and receptive language, and evoking motor and vocal imitation.
Procedure Experimental Design The study followed an ABA1 single-case design. Single-case designs are a useful starting point for establishing efficacy because they yield evidence that the technique has a clear, replicable effect on a specific behavior. In an ABA1 design, data gathered in the post-intervention phase (A1) indicate whether the effects of the intervention (B) continue when the intervention is no longer in place, or if removal of the intervention (a robotic device in our case) results in a return to baseline (A) behaviors. Each of the three study conditions lasted 1 week, with an interval of 7 days between the intervention and the baseline phases. In total, the entire study lasted 4 consecutive weeks for each participant. Baseline and intervention sessions included the same educational activities. In the baseline phase (A), participants attended four consecutive sessions (each lasting
Case 1
Case 2
Age Gender Intellectual disability Sensory impairments Vineland-II1 Communication Daily living skills Socialization
9;0 M Profound Bilateral hearing loss
9;8 M Severe None
20 28 29
20 41 31
Composite score CARS22 Target behaviors
20 40.5 Motor imitation; expressive/receptive language; reaction to name
21 45
Engagement behaviors observed
Eye gaze; touch
Expressive/receptive language; motor/vocal imitation; spontaneous request Eye gaze; touch
1
Only standard scores are reported
2
Childhood Autism Rating Scale – Second Version; scores > 37 are characterized as severe autism
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5–15 min) of educational activities. Multiple target behaviors were focused on within the same session, including three target behaviors for Case 1 (motor imitation, expressive/ receptive language, reaction to name) and three target behaviors for Case 2 (expressive/receptive language, motor/vocal imitation, spontaneous request). Similarly, in the intervention phase (B), the participants attended four sessions (5–15 min) of educational activities during which the educator used a humanoid robot. In the post-intervention phase (A1), both participants returned to their usual four sessions of educational activities without the robot. In total, 12 sessions were held for each study participant. Table 2 provides an outline of the educational activities performed for each target behavior. Settings and Materials All intervention activities took place in a dedicated therapy room at the local CAMHS. Adults in the room included an educator and a second observer. A table was placed at the center of the therapy room together with two chairs, one for the child and one for the educator. Activities took place mostly at the table or on the floor, depending on the child’s preference. A video camera was placed on a tripod, always in the same corner of the room. The robot used was a NAO NextGen (Model H25, Version 4) humanoid robot, produced by Aldebaran Robotics. The decision to use a humanoid robot was taken on the basis of current evidence suggesting that robots resembling human appearance and having moving limbs are more effective in eliciting social responses in children with ASD (Lee et al. 2012; Robins et al. 2006). According to the producer (Aldebaran documentation 2018), NAO is 57.4 cm (22.5 in.) tall and weighs 5.4 kg (about 11.4 lb). NAO has 25 degrees of freedom and a humanoid shape, which enable it to move and produce gestures and grasps small objects. It is manufactured with a wide range of behaviors, including walking, standing up and sitting down, dancing, and recognizing speech, sounds and objects (through two video cameras), as well as the scope to produce speech from text and to play sound files. These behaviors can all be programmed into the robot through a user-friendly graphical interface that allows users to control the robot wirelessly from a tablet, laptop, or desktop computer, and to create behavior sequences. In Italy, the cost price is about € 7000 (about $8500). Educator An educator from the local CAMHS, with more than 6 years of experience in ASD treatment and who attended a 1-day course on the use of the NAO robot conducted all the intervention sessions. The educator was involved in every phase of the research process, including the development of the robotbased activities proposed during the interventions. He also
served as a contact between the research team and the children’s parents. Baseline Baseline consisted of four one-to-one sessions using a wide variety of everyday objects (e.g., picture cards, a music box) and ordinary toys (e.g., puppets, balls). The educator engaged in his usual educational activities as required by the CAMHS service model (e.g., stimulating imitation of body movements, eliciting a spontaneous request; see Table 2 for details). No changes were made to the usual educational setting except for the presence of a second observer who recorded all the sessions but never interacted with the educational environment. Each activity consisted of a training period and a target period. The training periods were developed to include gradually fading prompts. By way of example, to elicit a spontaneous request, the educator said the word Bmusic^ during the training period and a music box started to play on this cue; the educator repeated this several times. In the target period, the educator randomly interrupted the music while it was playing. The music could start again only if the child said the word Bmusic,^ in which case the educator provided a positive feedback. If the child did not say the target word, the educator prompted the correct word and started the activity from the beginning. The target goal for this activity was thus to increase the frequency of spontaneous requests to activate the music. The robot was not present in the baseline sessions. Robot-Based Intervention Baseline and intervention sessions used the same sequence of educational activities and focused on the same target behaviors with the sole exception that educational material was completely or partially replaced by the robot, depending on the educational activity proposed (see Table 2). As in the baseline phase, each activity in the intervention phase consisted of a training period and a target period including gradually fading prompts. To elicit the spontaneous request mentioned earlier, for instance, NAO was programmed to dance or produce music in response to a specific word (Bmusic^). During the training period, the educator said Bmusic^ and NAO immediately started to play music and move. The educator repeated this several times. In the target period, the educator randomly interrupted the music while it was playing. The music could start again only if the child said the word Bmusic,^ in which case the robot provided a positive feedback. If the child did not say the target word, the educator prompted the correct word and started the activity from the beginning. The target goal for this activity was to increase the frequency of spontaneous requests to activate the music. To ensure an immediate reaction from NAO, the educator used an iPad™ to initiate the positive reinforcement once the child had
Adv Neurodev Disord Table 2
Description of baseline, post-intervention, and robot-based activities in relation to each target behavior
Target behavior Baseline and post-intervention Vocal imitation
Motor imitation
Expressive language
Training: While listening to kids’ music (e.g., Old McDonald), Training: While listening to kids’ music (e.g., Old McDonald), the educator makes onomatopoeic vocalizations. NAO makes onomatopoeic vocalizations. Target: The educator says to the child: BRepeat what I say: Target: NAO says to the child: BRepeat what I say: [vocalization] [vocalization] [Italian, BRipeti quello che ti dico^]. [Italian, BRipeti quello che ti dico^]. Prompt: Vocal. Material: None.
Prompt: Vocal. Material: NAO.
Training: The educator makes body movements during motivating activities (e.g., listening to kids’ music).
Training: NAO moves its body (e.g., dancing) during motivating activities (e.g., listening to kids’ music).
Target: The educator makes a body movement (e.g., raising his arms) and says: BDo that^ [Italian, BFai così^].
Target: NAO makes a body movement (e.g., raising its arms) and says: BDo that^ [Italian, BFai così^].
Prompt: Physical. Material: None. Training: From a set of cards depicting everyday objects, the educator picks up one card at a time and names the object represented, waiting for the child to repeat the sound.
Prompt: Physical (from the educator). Material: NAO.
Target: Using a set of three cards placed on a table (or on the floor), the educator indicates one card and says: BWhat is it?^ [Italian, BChe cos’è?^].
Prompt: Vocal. Material: Picture cards. Receptive language
React to name
Spontaneous request
Robot-based intervention
Training: From a set of cards depicting everyday objects, the educator picks up one card at a time and NAO names the object represented, waiting for the child to repeat the sound. Target: Using a set of three cards placed on a table (or on the floor), NAO moves an arm indicating one of the three cards and says: BWhat is it?^ [Italian, BChe cos’è?^]. Prompt: NAO vocally prompts the correct answer through remote control. Material: NAO; picture cards.
Training: From a set of cards depicting everyday objects, the educator names and indicates the objects.
Training: From a set of cards depicting everyday objects, NAO asks the educator to touch a target card. After the educator has carried out the instruction, NAO produces a positive feedback (e.g., saying BWell done!^) [Italian, BBravo!^].
Target: Using a set of three cards, the educator says to the child: BTouch… [name of the card]…^ [Italian, BTocca…^]. Prompt: Gesture (to point).
Target: Using a set of three cards, NAO says to the child: BTouch…^ [Italian, BTocca…^]. Prompt: Gesture (to point).
Material: Picture cards. Training: During an engaging cause-effect play activity (e.g., playing music), the educator stops the activity and calls the child by name, waiting for his eye contact to start the activity again. Target: During a play session, the educator is positioned out of the child’s visual field, maybe behind him. The educator calls the child by name and waits for his reaction (e.g., he turns his head).
Material: NAO; picture cards. Training: During an engaging cause-effect play activity with NAO (e.g., playing music), the robot frequently repeats the child’s name.
Prompt: The educator moves closer to the child and repeats the child’s name. Material: None. Training: During a motivating activity (e.g., playing music), the educator stops the activity and waits for the child to react by denoting attention (e.g., eye contact). Then the educator says the target word (e.g., Bmusic!^) and starts the activity again. Target: The educator stops an activity (e.g., playing music) and waits for the child to say the target word (e.g., Bmusic!^). Prompt: Vocal. Material: Various objects used during the child’s favorite activities.
Target: The child and the educator are engaged in an activity without the robot. NAO is positioned out of the child’s visual field, maybe behind him. NAO calls the child by name and waits for his reaction (e.g., he turns his head). Prompt: The educator turns the volume up on NAO’s speech synthesis and the robot repeats the child’s name Material: NAO. Training: During a motivating activity (e.g., playing music), NAO dances for 10–15 s, then the educator stops NAO, by means of the iPad™. The educator says the target word again, and activates NAO again. Target: The educator stops NAO and waits for the child to say the target word (e.g., Bdance!^). Prompt: Vocal. Material: NAO used during the child’s favorite activities.
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produced the target word. In this way, it was possible to avoid any lag in robot response, thus increasing the effectiveness of the reinforcement. Furthermore, remote control via tablet was also useful to differentiating reinforcements, so that the same target word would produce different NAO behaviors depending on the child’s preferences. For the purposes of our study, the robot assumed mainly the role of positive reinforcer or interaction partner (e.g., for activities stimulating a child’s reaction to his name). Post-intervention During the post-intervention phase, the educator proposed the same educational activities as in the baseline phase. This procedure allowed to determine whether the participants could perform the educational activities without the robot present.
Measures Data Collection and Response Categories All the sessions were videotaped and the videos analyzed. The whole data set comprised 300 min of video recordings; only the target periods were analyzed, for a total of 119 min (39.6%). Data were collected by the first author of this study, using continuous measurement. This method was chosen as it allows every behavioral occurrence as well as its exact duration in each single period to be captured (Fiske and Delmolino 2012). Video analysis and data logging were conducted using the Obansys mobile application for iPad™ (https://www. mangold-international.com/en/). Behaviors observed included (1) state of engagement and (2) goal achievement. For the purposes of the present study, engagement was broadly defined as Ban individual’s sustained attention or appropriate interaction with people or objects in Table 3
their environment^ (Simpson et al. 2013; p. 1489). States of engagement were assessed according to the framework developed by Kishida et al. (2008). Table 3 summarizes the response categories related to the engagement observed for the purposes of this study. The scheme contained four codes for the engagement state, namely engagement (active, passive) and non-engagement (active, passive). The framework was pilot-tested in a series of single-case studies to explore the engagement of three children with ASD in robot-based interventions to ascertain that the engagement states observed did represent the engagement of the children involved in the educational activities and proved their usefulness when applied to a clinical setting (Desideri et al. 2017). Each engagement state was measured as the ratio between its total duration (in seconds) and the total duration of the whole target period. A further category, termed Bneutral,^ was added to record the target periods in which there were technical interruptions (e.g., to activate the robot, to change position), pauses or changes in the activities proposed. Goal achievement refers to the frequency with which a desired (target) behavior occurred without any prompt during each target period; it was calculated as the ratio between the total number of correct answers provided without prompt and the total number of requests made by the educator during each of the one-to-one sessions. Coding Procedure Reliability The first author of the study coded the whole data set. For the purposes of the current study, the first author and the educator independently rated randomly selected 1-min samples of video recordings across all study conditions. Only target periods were analyzed. For each 1-min sample, the occurrence or nonoccurrence of dependent variables (engagement states and goal achievement) was scored using 15-s partial-interval
Definitions of codes used for the video analyses (adapted from Kishida et al. (2008)
Engagement
Definition
Active engagement
A child actively participates in the activity by interacting with (a) A child holds and moves the brush to paint, with his/her eyes on the brush and paper; (b) a child looks at the robot the learning environment appropriately by manipulating or and smiles when it pronounces his/her name gazing at the materials. The child does not demonstrate repetitive and/or inappropriate behaviors. A child interacts with the environment or the learning activity (a) A child applauds in reaction to an event without looking at but in a repetitive or stereotyped manner the object which caused it, thus demonstrating that clapping is a stereotyped behavior; (b) a child perseveres in behaving appropriately for a previous activity but not in the current one (e.g., asking to play music when the activity is to name a color); (c) even if a child provides a correct answer, he/she does not pay overt attention to the ongoing activity A child interacts with the environment in an inappropriate A child wanders around or interacts with objects in a manner that manner by manipulation/movement and/or vocalization. is not pertinent to the ongoing activity. A child does not interact with the environment and does not A child does not interact with any material. do what is expected from her/him during the activity.
Passive engagement
Active non-engagement Passive non-engagement
Example
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recordings. Then, for each sample, the intra-class correlation coefficient (ICC) was calculated by the first author as a measure of inter-rater reliability reflecting the accuracy of the rating process (Streiner and Norman 2008). The ICC results were interpreted respecting the guidelines proposed by Fleiss (Streiner and Norman 2008) with ICC values of > 0.75, 0.40–0.75, and < 0.40 taken as corresponding respectively to Bexcellent,^ Bfair to good,^ and Bpoor.^ Acceptable reliability thresholds are most often set at 0.80 or better (Harrigan 2005). Instruction-giving and recording were rehearsed by the first author and the educator conducting the interventions, until ICC registered as above 0.80 on two consecutive samples for all dependent variables observed. A total of 13 samples (21% of the whole video sample) for Case 1 and 9 samples (15%) for Case 2 were scored to achieve the target threshold in two consecutive scoring sessions.
Data Analyses All data are expressed as means (M) and standard deviations (SD). Data analysis consisted of visual analysis aided by quasi-statistical techniques (Manolov et al. 2016). Data analysis involved the calculation of the mean percentages for the behavior categories (engagement states and goals achieved) across conditions. Because of the variability in period length in each study condition, we also used a weighted mean of the active engagement state in order to obtain a reliable estimation of the time spent actively engaged. This was calculated as the ratio between the time spent actively engaged and the sum of the remaining engagement-state variables. Due to the relatively low number of sessions performed, frequency of goal achievement was calculated for the whole educational intervention and not for each single educational activity. To determine the magnitude of the change in engagement states and goal achievement, considering the small number of both baseline and post-intervention sessions, we calculated a variation on Cohen’s (1988) d statistic as proposed by Beeson Table 4
and Robey (2006). Effect size is calculated by subtracting the average score obtained in A from the average score obtained in A1, and the result is divided by the standard deviation of A. Cohen (1988) has provided benchmarks that serve to guide the interpretation of effect sizes: 0.20 is considered poor, 0.50 moderate, and 0.80 or more high. For explorative purposes, to test the relationship between engagement and goal achievement, Pearson correlations were performed between active engagement and goal achievement rates across all conditions. All the analyses were conducted using Microsoft Excel and JASP (jasp-stats.org), and the significance level was set at p < 0.05. All data are available at the Open Science Framework (https://osf.io/83pqc/).
Results As illustrated in Table 4 and Fig. 1, Case 1 showed a moderate increase in active engagement from baseline to postintervention sessions (d = 0.61). On average, his level of engagement in the baseline phase was M = 30.68% (SD = 22.43); this then improved in the intervention phase with the robot to up to M = 53.98% (SD = 12) and remained above the baseline in the post-intervention phase, at M = 44.49% (SD = 4.17). Such a positive trend in the level of engagement also remained when weighting the active engagement levels with the other states of engagement observed (d = 0.78). Furthermore, active non-engagement showed a substantial decrease (d = 5.71), indicating an improved capability to stay on task and respect the educational setting for longer periods. This interpretation is supported by the fact that frequency of goals achieved almost doubled from baseline (M = 18.93, SD = 13.70) to post-intervention (M = 49.04, SD = 35.23) and markedly increased in the intervention phase (M = 21.03, SD = 13.87). Notably, despite a large increase in the levels of active engagement and a decrease in active non-engagement, passive engagement increased greatly from
Means, standard deviations, and effect sizes (ES) of states of engagement and goals achieved Case 1
Active engagement Passive engagement Active non-engagement Passive non-engagement Weighted engagement score Goals achieved
Case 2
A
B
A1
ESA-A1
A
B
A1
ESA- A1
30.68 (22.43) 10.55 (10.71) 27.45 (3.99) 2.64 (3.24) 0.75 (0.57) 18.93 (13.70)
53.98 (12) 28.98 (13.27) 15.42 (5.16) 0 1.31 (0.51) 21.03 (13.87)
44.49 (4.17) 27.96 (10.21) 4.65 (6.98) 4.14 (4.9) 1.2 (0.08) 49.04 (35.23)
0.61 1.62 5.71 0.46 0.78 2.19
57.34 (16.86) 24.25 (17.9) 4.92 (3.88) 0 3.59 (3.2) 40.45 (10.29)
59.69 (18.01) 25.82 (6.94) 5.62 (7.62) 0 2.44 (1.85) 40.77 (3.42)
57.71 (22.38) 23.65 (7.46) 10.34 (19.06) 0 2.52 (2.05) 61.05 (18.81)
0.02 0.03 1.39 – 0.33 2.00
The sum of the frequency of each state of engagement in every phase is lower than 100 because data related to the Bneutral^ category were excluded from the analyses A, baseline phase; B, robot-based intervention phase; A1 , post-intervention phase
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Fig. 1 Frequency of states of engagement observed and goals achieved during baseline, robot-based intervention, and post-intervention for Case 1
baseline (M = 10.55, SD = 10.71) to post-intervention (M = 27.96, SD = 10.21), with d = 1.62. Exploratory analyses revealed a positive but not significant association between active engagement and goals achieved (r = 0.31, p = 0.31). Differently from Case 1, Case 2 (see Fig. 2) showed no improvement in active engagement from baseline to postintervention (d = 0.02). Furthermore, when considering the weighted engagement score, the results indicate a small decrease (d = 0.33) in the level of overall engagement across sessions. In contrast with Case 1, Case 2 showed a large increase (d = 1.39) in the levels of active non-engagement observed, a result that denotes frequent activity interruptions and the child’s refusal to stay on task, especially in the postintervention phase. Nevertheless, while frequency of goals
achieved resulted on average below the chance level (M = 40.45, SD = 10.29) in the baseline phase, it increased significantly (M = 61.05, SD = 18.81) in the post-intervention phase, with d = 2. It should be noted that frequency of goals achieved did not change between the baseline and intervention phases. Again, exploratory analyses revealed a positive but not significant association between active engagement and goals achieved (r = 0.30, p = 0.34).
Discussion This study focused on exploring whether a humanoid robot implemented in routine clinical practices could improve
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Fig. 2 Frequency of states of engagement observed and goals achieved during baseline, robot-based intervention, and post-intervention for Case 2. Passive non-engagement is not displayed as it resulted equal to zero in all conditions
engagement and goal achievement in children with ASD during educational interventions provided by a public CAMHS. Despite levels of engagement showing mixed results, frequency of goals achieved did however result as homogeneous in both cases. In particular, we observed a marked increase in frequency of goals achieved between the intervention performed with the robot and the post-intervention sessions, an effect that could be attributed to the children’s interactions with the robot. Although large, this effect should however be considered with caution. Data gathered in the course of this study should only be considered as a preparatory exercise providing information for the design of a more robust translational research protocol aimed at assessing the effectiveness of robot-based ASD intervention scenarios (Wainer et al. 2014). Regarding levels of engagement, while we observed a linear progression in levels of active engagement in Case 1, this was not so for Case 2, who showed a small decrease over the sessions, as evidenced by the weighted engagement score. Individual factors, such as differences in intellectual disability severity and varying educational objectives, may have exerted an influence. Looking at the levels of engagement observed at baseline, however, it should be noted that there is a marked difference between the two participants, with Case 1 starting the sessions with very low levels of engagement (< 50%) and Case 2 well above 50%. In this view, to explain the differences in engagement states between the two participants, we could
hypothesize that robot-based interventions may be more effective when the aim of the intervention is to increase the levels of engagement from very low baseline levels, rather than in those situations in which the child already shows a mild-to-moderate interest in the activities proposed. Our results point to the need to ensure homogeneity in terms of individual characteristics, and diagnosis and educational outcomes, as well as regarding the children’s preferences, in the samples recruited to investigate effectiveness of robot-based interventions. The trends observed in the other engagement states can be interpreted in light of those observed for active engagement. Not surprisingly, active non-engagement in Case 1 greatly decreased as active engagement increased, demonstrating that by increasing interest in the activity proposed, the possibility that the child interacts with the educational environment in an inappropriate manner (e.g., wandering around the room) subsides. On the contrary, the active non-engagement observed in Case 2, which greatly increased from baseline to post-intervention, may reflect the relatively low levels of active engagement shown during the sessions. Surprisingly, the passive engagement observed in Case 1 was incongruent with what we would have expected by looking at the levels of active engagement states, as it almost doubled from baseline to intervention. This apparently incongruent result may be explained by the fact that the increased
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mastery in responding to proposed tasks (indicated by the rise in goal achievement) led to a progressive reduction in attention towards the educational activities. In addition, a factor that further contributed to the higher rates of passive engagement observed was an increase in frequency of stereotyped behaviors (e.g., vocalizations), which however did not negatively affect the execution of the educational activities. Whether such an increase in stereotyped behaviors was due to the specific characteristics of the robot or other individual and contextual factors (e.g., tiredness) is difficult to clarify within the limits of the present study. It should be noted that, overall, passive engagement remained stable between the intervention and the post-intervention sessions conducted without the robot; this result opens the possibility that other individual variables may have contributed to changes in stereotyped behaviors. The lack of significant association found in the present study between active engagement and rates of goal achievement, although unexpected, mirrors findings from other similar studies in which heightened engagement states during child-robot interaction did not lead to an improvement in intervention outcomes (Kim et al. 2013; Pop et al. 2014; Simut et al. 2016; Tapus et al. 2012). A potential explanation may be the way in which the sessions were structured, with multiple target behaviors pursued within the same session. This approach may have increased the complexity of the intervention setting by making it less repetitive and predictive, thus reducing the probability for the child to produce a target behavior during the child-robot interactions even when the child was overtly engaged (e.g., looking at the robot). A final consideration that emerges from the results of this study concerns the use of different information technologies within the same clinical setting. Occasionally, during the debriefing sessions between the research team and the educator who conducted the interventions described in the present study, the educator reported to the team that the children were also attracted by the iPad™ used to remotely control the robot. On these occasions, the tablet was a source of distraction that prevented the child from keeping his focus on the robot-based activity. Improving the autonomy of the robot (e.g., through better speech and gesture recognition systems) would certainly allow professionals to avoid the use of remote controllers to activate the robot in order to progress with the activity (Esteban et al. 2017). Here, however, instead of considering the two platforms (robot and tablet) as a source of competition for the children’s attentional resources, we wish to stress the scope to combine the two platforms to improve ASD intervention outcomes. Further research is needed, in our opinion, to explore whether designing interventions in which the child is involved in activities delivered through a combination of interactive information technologies such as tablets and robots could represent a valuable and effective complement to ASD interventions.
Limitations and Future Research Directions Given the exploratory nature of this study, there are several limitations to discuss. The research design used here does not enable distinction between effects from the children becoming more familiar with the proposed activities, and the effects from the children having interacted with the humanoid robot before the post-intervention sessions. Moreover, we did not control type and frequency of the requests (e.g., highprobability vs. low-probability requests) as sessions progressed. Future investigations may benefit from the use of more robust designs such as, for instance, a multiple baseline or multiple probes across participation design in order to ensure that changes in the dependent variables can be attributed to the effects of the intervention in which all the factors potentially influencing goal achievement are more carefully monitored and recorded. As already evidenced, we observed a lack of significant associations between levels of active engagement and frequency of goals achieved in both cases in this study. This may suggest a lack of clinical validity in the engagement categories used to assess participants’ behavior. Available coding procedures to assess engagement in child-robot interaction in the context of ASD treatments are scant. Kim et al. (2012), for instance, used a Likert-type scale from 0 to 5 to judge video recordings for engagement. Though widely used to rate engagement of children with ASD in robot-based therapy sessions (e.g., Rudovic et al. 2017), this scale was not developed for clinical purposes. By contrast, the classification of engagement states proposed by Kishida et al. (2008) used in the current study has been validated in educational and clinical settings. It allowed us to conduct a more fine-grained analysis of the children’s engagement behaviors by distinguishing, for instance, whether the child was actively engaged in the proposed activities or was merely observing the activity (passive engagement). In light of these considerations, the nonsignificant association between engagement states and frequency of goal achievement in both cases may be due, in our opinion, to the short duration of the present study, as evidenced by the observation of positive associations in both cases. Future investigations using longer intervention durations are warranted to properly assess the relationship between the child’s engagement and goal achievement. In addition, the use of a multidimensional approach in which both observational and more objective data are combined (e.g., the child’s heart rate variability) to measure engagement states may increase the validity of the results. A topic that requires further inquiry concerns intervention length and its effect on the quality of the child-robot interactions and the outcome of the whole intervention. Clinical evidence suggests that, to enhance developmental outcomes, behavioral and developmental interventions targeting children with ASD may require the delivery of intensive and long-
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lasting (often years-long) intervention programs (e.g., Reichow et al. 2012). For practical reasons, in keeping with the purposes of the present investigation, the study included a total of 12 sessions delivered within 1 month. Thus, questions may arise concerning the possibility that the overall effects of the robot on engagement and goal achievement found in our study could vanish if the device was used for longer interventions. Compared to other studies involving humanoid robots, we limited the role of the robot exclusively to its being a positive reinforcer to increase a desired behavior. Another possible way to benefit from the introduction of a robot in ASD treatment or educational scenarios, as documented in the literature, is to use it as a mediator of the relationship between the child and the adult/s accompanying the child (e.g., Robins et al. 2004). In this vein, Kozima et al. (2007) observed the emergence of triadic interactions when a 3-year-old girl with ASD and moderate intellectual disability wanted to share with an adult the surprise she had experienced with Keepon, the robot used in the trials. Future studies should clarify in which role (e.g., reinforcer vs. mediator) a robot could be more effective in improving intervention outcomes for children with ASD. Lastly, the landscape of assistive technology is rapidly changing as new and emerging technologies are becoming available to professionals involved in the treatment, education, and care of people with neurodevelopmental disabilities (Lancioni 2017). Advancements in socially assistive robotics, in particular, have already opened new promising intervention scenarios for children with ASD (Pennisi et al. 2016), and data gathered from the current study reaffirm that the use of humanoid robots may be considered not detrimental to both engagement and learning outcomes when compared to more traditional interventions (for similar results, see Yun et al. 2017). Stronger partnerships between the technological and clinical fields are recommended in order to further the understanding of the effects of robot-based interventions on the development of cognitive and social skills in children with ASD. Acknowledgements The authors wish to thank participants and their families for their invaluable contribution. Author Contributions LD, MN, MCC: designed the study. LD: performed data analyses and wrote the paper. MN: collaborated on data analyses and conducted the interventions. DT, MM, AR: collaborated for the design, staging, and writing up of the study. PB, EH: collaborated for the writing and editing of the final manuscript. Funding Information This study has been conducted in connection with the Educational Robotics for Students with Learning Disabilities (EDUROB) project (543577-LLP-1-2013-1-UK-KA3-KA3MP) and BProgetto di sviluppo e diffusione di competenze su Ausili Informatici e Tecnologie di supporto ai Disturbi della comunicazione nei Disturbi Pervasivi dello Sviluppo e della Disabilità Intellettiva^ (Regione Emilia Romagna – Azienda USL Bologna).
Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflict of interest. Informed Consent Statement All parents of participating children signed a written informed consent. Ethics Statement The study was approved by the Ethical Committee of the Bologna Local Health Trust (Comitato Etico Interaziendale BolognaImola) and has been assigned number CE 16022.
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