=Paper=
{{Paper
|id=Vol-3100/paper14
|storemode=property
|title=Design and development of HRI-based intervention for ASD children using ADDIE model and ABA: A preliminary study
|pdfUrl=https://ceur-ws.org/Vol-3100/paper14.pdf
|volume=Vol-3100
|authors=Sarah Afiqah Mohd Zabidi,Hazlina Md. Yusof,Shahrul Naim Sidek,Aimi Shazwani Ghazali
|dblpUrl=https://dblp.org/rec/conf/psychobit/ZabidiYSG21
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==Design and development of HRI-based intervention for ASD children using ADDIE model and ABA: A preliminary study==
https://ceur-ws.org/Vol-3100/paper14.pdf
Design and Development of HRI-based Intervention for ASD
Children using ADDIE Model and ABA: A Preliminary Study
Sarah Afiqah Mohd Zabidia, Hazlina Md. Yusofb, Shahrul Naim Sidekc, Aimi Shazwani
Ghazalid
a
International Islamic University Malaysia, Jalan Gombak, Kuala Lumpur, 0000-0002-9744-0429, Malaysia
b
International Islamic University Malaysia, Jalan Gombak, Kuala Lumpur, 0000-0002-6349-2028, Malaysia
c
International Islamic University Malaysia, Jalan Gombak, Kuala Lumpur, 0000-0002-3204-1347, Malaysia
d
International Islamic University Malaysia, Jalan Gombak, Kuala Lumpur, 0000-0001-5042-3536, Malaysia
Abstract
The growing interest in Human-Robot Interaction (HRI) based early intervention therapies for
autism spectrum disorder (ASD) children brings about the prospect of adapting this platform
as assistive tool during therapy. Though this field has garnered myriad of attention, more
research is needed in developing systematic design for human-robot interaction using
standardized process models. One of the most well-established systematic process models in
developing instructions is the Analysis, Design, Develop, Implement and Evaluate (ADDIE)
model. While the ADDIE model was used to design the interaction during the first phase,
Applied Behavioral Analysis (ABA) technique, an evidence-based practice for ASD children
were also integrated into our modified model. This preliminary study discusses three subjects:
1) guideline in adapting ADDIE model for HRI-based ASD intervention, 2) a preliminary
modified ADDIE model for human-robot interaction with ASD children and 3) robot’s
behavior integrated with ABA technique. Preliminary evaluations on the developed framework
were done by a therapist with experience working with ASD children on the severe end of the
spectrum.
Keywords
Autism Spectrum Disorder, ASD, Human-robot interaction, HRI, Applied Behavioral
Analysis, ABA
1. Introduction
Autism Spectrum Disorder (ASD) is a pervasive neurodevelopmental disorder that is characterized
by difficulties with social and communicative skills, restricted interests, repetitive behavior, and sensory
issues [1]. Current prevalence data estimated that 16.8 per 1,000 (one in 59) children aged eight years
in 2016 has autism. This is approximately 2.5 times higher than the first ADDM Network ASD
prevalence estimates of 6.7 (one in 150) from 2000 and 2004 [2]. As there are no known medical cure
for ASD, main method for interventions are focused on behavioral modifications such as Applied
Behavioral Analysis (ABA)[3], TEACCH Autism Program [4], LEAP [5] and Sensory Integration
Therapy [6].
This has also caused for increasing popularity in utilizing technologies, especially robots as assistive
tools for educators/therapies during therapy. Although there are various robotic platforms available,
studies on ASD children in this domain usually focuses on humanoid robotic research [7] due to their
ability to mimic, behave and interact like a human. Educators also assumed that ASD children will be
able to generalize skills learned with robots due to their consistent predictability and human-like
features [8]. However, as many studies are still focusing on robots’ technologies, more research is
needed in systematic Human-Robot interaction (HRI) development using process models. This step is
important in ensuring HRI-based interventions are accepted as an evidence-based practice (EBP) by the
clinical community.
____________________________________
Proceedings of the Third Symposium on Psychology-Based Technologies (PSYCHOBIT2021), October 4–5, 2021, Naples, Italy
EMAIL: sarahafiqah,zabidi@live.iium.edu.my (SAM Zabidi) ; myhazlina@iium.edu.my (HM Yusof); snaim@iium.edu.my (SN Sidek);
aimighazali@iium.edu.my (AS Ghazali)
ORCID: 0000-0002-9744-0429 (SAM Zabidi); 0000-0002-6349-2028 (HM Yusof); 0000-0002-3204-1347 (SN Sidek); 0000-0001-5042-
3536 (AS Ghazali)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
In instructional design community, the Analysis, Design, Develop, Implement and Evaluate
(ADDIE) model is one of the most widely used systematic process model. There are five elements to
ADDIE model: analysis, design, develop, implement, and evaluate. This method was chosen as the five
elements are ongoing activities that continue throughout the process. The process can be referred to in
Figure 1. Clear and effective intervention programs can be designed using this method. Although it is
originally developed to be hierarchical, it can also be tailored to be a continuous ‘iterative’ approach.
The first five phases are followed in order, then, once complete, the data obtained can be used as a
guideline and researchers may restart from the analysis phase, improving the final product. Considering
that HRI-based intervention requires continuous rigorous tests and evaluation, the systematic nature of
ADDIE process model can be applied to the HRI-based intervention for future performance
improvement.
Analysis
Evaluation Design
Implementation Development
Figure 1. The ADDIE model.
While the ADDIE model was used during development and design of the research, Applied
Behavioral Analysis (ABA) technique were used to program robot’s behavior. This is to ensure the
robot is predictable and consistent, which is important in effective learning for ASD children. ABA is
based on operant conditioning where consequences influence behavior. The first step in ABA clinical
practice is to perform mental/developmental assessments for ASD children (ASDC) in determining the
baseline and appropriate goals for each individual. The step is followed by the development of a clear
intervention plan which includes clearly defined procedures for instruction, error correction, prompt
levels, reinforcement, and performance data collection. Our project proposes on the development of
HRI-based intervention by utilizing the systematic ADDIE model during research development and
building framework for robot’s behavior using ABA’s core technique.
2. Human-Robot Interaction (HRI)
HRI is an interdisciplinary study of interaction dynamics between humans and robots [9]. HRI has
been recognized as a new probable approach in the research on autism research. It is the changing
relationship between intelligent robots and humans, which are done through social interaction. HRI can
be described both by the user’s behaviors and by the role of the robots during the therapy session. The
goal of HRI for ASDC involves encouraging imitative behaviors, mediating turn-taking, extracting
referencing and enhancing joint attention between ASDC and another human.
2.1. Robots for ASD children
As mentioned in many studies, the use of HRI for ASDC is not to replace therapists, but the
main aim is to successfully integrate robot into a normal therapy as a mediator between
therapist and ASDC [10,11]. Based on previous literatures, various types of robot were used.
The robots are generally used to improve foundational skills such as imitation (I), joint
attention (JA), social (S) and other (O) skills. Other skills taught by the robot include reading,
comprehension, and literacy skills. Example of robots used in the earlier studies, and the skills
targeted can be referred to in Table 1.
Table 1. List of current robots used and the skills targeted.
Robot JA S I O Studies
NAO / / / / [12–17]
Pleo / [18]
Probo / [11]
QTRobot / / [19]
Zeno / / / [20–22]
RoboParrot / [23]
TurtleBot / [24]
Popchilla / [25]
IROMEC / [26]
3. The ADDIE Model
ADDIE model is the most common model used in instructional field design. This is due to it being
generic enough to create any type of learning experience for many learners. Although this model is
usually used by training instructors, we believe the model can be modified according to ASD children’s
needs. The systematic nature of this model may help facilitate researchers in the HRI and ASD domain
to move in the correct direction. The model comprises of five phases as shown in Figure 1. Each phase
is critical as the researcher must make crucial decisions after thorough analyzation to deliver an
effective training/intervention. Figure 2 defines each phase of the model. The entire intervention can be
designed thoroughly by following the guideline. ADDIE model for HRI were referred from [27] and
were further improvised to suit our research objectives.
This phase involves: In this phase, the During development, the For implementation, the In the last phase, the
specification of issues in researcher selects and designer develops and researcher conducts the HRI effectiveness of the
HRI-based intervention for documents the objectives, completes the actual session to specified target interaction and the
ASD (problem statement), evaluation tools (outcome creation of the HRI to meet (in this case: ASD children). achievement of goals are
HRI goals and objectives are measures), HRI plan and the interaction objectives. A In this phase, training is evaluated. One unique
specified, understanding of control modalities. This step by step procedure for provided to the robot feature of this model is that
Analysis
Design
Development
Implementation
Evaluation
research gaps, robot’s process must be both implementation of the operator (should it not be it can be used iteratively.
requirement, systematic and specific. interaction strategy is the same person For example, feedback from
understanding of the gaps completed. This phase also developing the robot) on the evaluation phase can be
between current condition includes the developmental the operation. used right back in the
and desired outcomes. A Output: Module of work, debugging, testing, Analysis phase, which starts
quality analysis can interaction, overall design review and revision of an entirely new iteration of
contribute to identifying of robot’s behavior and strategies. Output: Conduct the HRI- the end product.
learning goals and needs, outline of based intervention with
objectives. intervention, outcome ASD children.
measures. Output: A completed HRI Output: Detailed evaluation
platform. on the efficacy of the
Output: Clear views on the intervention, information
research gaps, objectives, on aspects that may need
desired outcomes. revision and can be used for
to improvise future
research.
Figure 2. Details for each elements/phase in the ADDIE model.
3.1. Applied Behavioral Analysis (ABA)
ABA is based on operant conditioning where consequences influence behavior. The first step in
ABA clinical practice is to perform assessments in determining the appropriate goals for each individual
using several tools such as the Verbal Behavior Milestones Assessment and Placement Program [28]
and Vineland Adaptive Behavior Scale [29]. Assessment is then followed by the development of a clear
intervention plan which includes clearly defined procedures for instruction, error correction, prompt
levels, reinforcement, and performance data collection.
3.1.1. Teaching Technique (Discrete Trial Training)
ABA approach uses discrete trial teaching (DTT) to teach skills. Discrete trials or the three-
part teaching unit is a special behavioural sequence used to maximize learning. DTT is used to
make teaching session clearer, let the child know when he/she is right or wrong, helps teacher
maintain consistency and makes assessment of progress simpler [30]. Operant conditioning
implies that a behaviour that results in something that is liked (reinforcement) will be repeated
[31]. As reinforcer is something that is liked by ASDC, the reinforcers are a critical tool to be
embedded in ABA program. The reinforcers are given as consequence whenever the child
respond correctly. Figure 3 shows the sequence of DTT and its’ component.
Discriminative Stimulus Response Consequence
/Instruction (R) (𝑆 𝐶 )
(𝑆 𝐷 )
Figure 3. Components of DTT.
3.1.2. Prompting Techniques
As shown in Figure 3, prompting is a means to induce an individual with added stimuli (prompts) to
perform a desired behavior. Prompting is provided when an ordinary antecedent is ineffective and is
extensively used in behavior shaping and skill acquisition. It provides learners with assistance to
increase the probability that the desired behavior will occur. Successful performance of a desired
behavior elicits positive reinforcement, therefore reinforcing learning. A prompt is used as a cue to
support and encourage a desired behavior that otherwise does not occur.
Discriminative Stimulus Response Consequence
/Instruction (R) (𝑆𝐶 )
(𝑆 𝐷 )
Prompt (P)
Figure 4. Prompting as part of DTT.
3.1.3. Application of ABA in HRI
Existing literatures were reviewed to identify studies that adapted ABA technique into HRI
applications in the past. There are a few techniques that have been frequently used by
researchers in the past which are reinforcement (R), prompt levels (PL) and discrete-trial
teaching (DTT) technique. Results may be referred to in Table 2.
Table 2. ABA-based HRI studies.
Studies Robot R PL DTT
[32] NAO /
[33, 34] NAO / /
[7,33,35] NAO / /
[36] PABI /
[37] NAO / /
[38–40] NAO / /
[41, 42] NAO / /
[43] KASPAR / /
[44] KASPAR / /
[45] ReRO /
[46] NAO / / /
All studies reviewed applied the reinforcement technique (reward). The reward can be in the form
of social praise (“Good job!”, “Well done!”) or music played by the robot. Prompt levels are also one
of the most applied ABA techniques. If the child answered wrongly, the robot will ask the child to try
again and prompt will be provided. DTT is a structured teaching technique used to help ASD child learn
effectively. However, there are more to DTT than following the special behavioral sequence. An
effective DTT must follow the correct technique in terms of differential reinforcement, intonation, and
timing. [47] focused more on how to use social stories and visual schedule instead of using those two
techniques in a therapy.
4. Preliminary output based on ADDIE model.
4.1. Analysis Phase
In this phase, our focus lies on analyzing and identifying test subjects to determine interaction goals
and learning context. More precisely, we assessed the developmental age of the children by using
Vineland Adaptive Behavior Scale (VABS-3) to determine suitable module of interaction. Having a
clear view on the baseline of each child is important as no ASDC is the same and their chronological
age does not necessarily determine their developmental/mental age. Knowing the baseline can guide
decision for the next phase and provide realistic approach for the intervention. Therefore, all available
information on the ASD children from a center in Malaysia were identified. Three main issues that were
analyzed in this phase include:
a) Selection of center
Due to the movement-controlled order (MCO) imposed by Malaysian government during the period
of this study, a center for ASD children located near the university were chosen. This issue has limited
the sample size of this study.
b) Target age of ASD children
In the center chosen, although all ASD children were diagnosed with ASD, no records on their
exact developmental age were available. Therefore, help from the clinical team were obtained to
administer VABS-3 to the children. While this center consists of child aged from 4-9 years old, only
child with physical age 6-9 were selected as participants. Participants went through VABS-3 assessment
and developmental age for all participants were obtained.
c) Selection of robot
Existing literatures were reviewed to obtain the most optimal robot suitable for ASD children.
Studies in HRI for ASDC combines both robotic and clinical teams, therefore buying a commercial
robot is the better choice to bridge this gap [18]. Besides, parents, clinicians and teachers can easily buy
and customize the robot for certain needs due to the availability of the robot in the market. To
summarise, commercial robots have more advantages in terms of cost, robustness, and their lower
failure rate. During selection phase, recommendations from previous studies were adapted for inclusion
criteria. Some commercial robots that have been shortlisted are Bioloid, DARWIN, NAO and QTRobot.
However, for this study, Bioloid and Darwin were eliminated due to its small size (39.7cm and 45.5cm
respectively). As our target audience are ASDC aged 6-9 years old, robot with a similar size with the
child will be used. This is so that the child can see the robot at his or her eye-level. In addition, imitation
tasks are intuitively easier for ASDC to follow compared to using robots that are extremely large or
small in size [48]. The robot must also be easily integrated into the existing therapy framework and
agenda (objectives, setting, timing, etc.). To conclude, the selection criteria for the work are as follows:
(a) The robot must be easily programmable by therapists (has block-based coding); (b) Has developer
mode for researchers to embed a more complex program using programming languages such as
Python/C++/Java; (c) Able to display facial expressions, changes in emotional expression in the robot
must be able to be subtly observed. Although both NAO and QTRobot has almost the same features,
QTRobot was chosen because of its’ ability to subtly show emotional expression. The importance of
this aspect is discussed in the next sub-section.
4.2. Design Phase
In this phase, learning objectives were defined and modules of interactions were also constructed.
Based on our preliminary VABS-3 assessment, most of the participants were mostly lacking in the
social domain. Therefore, learning objectives and modules of interaction were designed to focus on
learning emotions as it is an important aspect in building social reciprocity.
Table 3. Modules of interaction developed based on findings from analysis phase.
Modules Methods Objectives
Introduction Robot slowly starts movement To measure robot’s
according to child’s response and approachability and create a
one-way communication using friendly environment for the
voice recognition. child during interaction.
Module 1 Robot will perform simple To assess child’s
(Identify emotion) actions and child are expected to engagement with the robot and
imitate. child’s ability to listen to
robot’s instruction.
Module 2 Robot asks the learner to
(Express emotion) identify the picture that is the
correct match of a sample picture.
Goodbye Robot says goodbye and wave To communicate robot’s
its arms. Thanks child and attempts limitation to the child.
handshake.
4.3. Development Phase
4.3.1. Robotic Platform (QTRobot)
QTRobot is a 58cm tall humanoid robot that is. QTRobot Studio were initially used to program
QTRobot’s behaviours. QTRobot Studio is a development environment provided by the robot
manufacturer, LUX AI. The interface is mainly drag and drop, and allows the programmer to create a
sequenced combination of predefined or custom behavior boxes to manipulate the QTRobot’s joints or
attributes. QTrobot has an expressive social appearance and its screen allows the presentation of
animated faces. It has 12 degrees of freedom to present upper-body gestures. Eight degrees of freedom
are motor-controlled, two in each shoulder, one in each arm plus pitch and yaw movements of the head.
The other four degrees of freedom, one in each wrist and one in each hand, are manually configured.
QTrobot has a RealSense 3D camera mounted on its forehead and is provided with a microphone
array. QTrobot is powered with an Intel NUC processor and Ubuntu 16.04 LTS, and provides a native
ROS interface to program it in Python or C++ programming languages. QTrobot also provides a visual
programming interface for IT non-experts, used in this study, to easily script custom applications and
control the robot by an Android application from tablets and smart phones. In the present study, the
robot’s interactions with the children were pre-scripted and controlled by the experimenter via a tablet.
During the development phase, this robot will first be programmed using QTRobot Studio to test for
the framework validity. Feedback on the framework validity will be done by a therapist. The equivalent
module will then be converted to a Python/C++ equivalent program in ROS environment.
4.3.2. System Architecture
The system architecture may be referred to in Figure 5. A researcher will operate the supervisory
controller for procedure control, robot feedback decision and data acquisition.
Figure 5. System Architecture.
4.3.3. Robot Teaching Structure
Note that the robot follows the DTT sequence (Sd -> R -> Sc). Prompt level (PL) was
programmed to help child feel successful. Reinforcement given in this context is social praise, gesture
an expression. The previous three actions have categorized according to prompt levels as differential
reinforcement Table 4. ASD child values consistency, the robot was made sure to give consistent
feedback to the child based on their response. This aspect was something that the therapist considers
important, as this is even difficult to maintain by experienced therapist. Consistency by human therapist
may vary according to their level of stress, moods, or environment.
Table 4. Differential reinforcement technique.
Prompt Level Expression Gesture Tone/Praise
No Prompt Clap Enthusiastic. (Exclamation
Hands up mark used)
First Prompt Clap Less enthusiastic response. (no
exclamation mark)
Second Prompt No gesture Praises are ended with ‘try’ at
the end of sentence to signify this
is not the expected behavior.
Figure 6. Example of programming block for correct response.
Figure 7. Example flowchart for imitation's interaction module.
Figure 7 summarizes the flow of robot’s behavior. Sentence with exclamation mark at
the end will be said by the robot with an enthusiastic tone. To summarize, the more PL the
child need, the less enthusiastic the robot’s response will be. Being consistent in this aspect
will help child learn targeted behaviors effectively. Even when the child does not give the
correct answer, the robot will help ASD child feel successful by giving prompts.
4.4. Implementation Phase
This phase involves the interaction between robot and ASDC. The experiment will be
conducted at the child’s center to maintain familiarity and ensure ASDC is comfortable. Researcher
will be hidden to minimize the number of unfamiliar people during HRI session. The flow of
intervention, preparation and challenges that might emerge during intervention must also be considered.
4.5. Evaluation Phase
The interactions will be recorded via Intel Realsense D435 embedded with the robot. The child’s
behavior will be analyzed via post session video analysis. In measuring the behavioral engagement,
proxemics imaging method is used to measure the distance between the subject and the robot to identify
their interest with the robot and levels of engagement throughout intervention. The same method was
previously applied by [49] and [50] who also measured the engagement between children with autism
and a humanoid robot using distance. Proxemics behavior includes interpersonal distancing, eye gazing,
and body gesture that can express the behavior of the person towards the engagement.
5. Conclusion
The present work introduces the design of a HRI platform aimed for ASD children by using ADDIE
model and ABA. Our purpose is to support children with ASD and the educators by introducing robots
as assistive tool. We would also like to propose for future researchers in this discipline to follow a more
standardized model to increase replicability and acceptability by the clinical community with our
design.
6. Acknowledgements
The authors gratefully acknowledge the Ministry of Education Malaysia (MOE) for funding the
research project through the Transdisciplinary Research Grant Scheme (TRGS) [Ref. No
TRGS/1/2019/UIAM/02/4/3].
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