Development and Validation of a System for the Anticipation of Challenging Behaviors of People With Autism Spectrum Disorder Based on a Smart Shirt: a Mixed-method Design
Conditions: Autism Spectrum Disorder
Sponsor: Ariel University

Study Protocol - Development and Validation of a System for the Prediction of Challenging 
Behaviors of People with Autism Spectrum Disorder Based on a Smart Shirt: a mixed-method 
design 
 
Document date: 09/04/2022 
 

INTRODUCTION 
Autism spectrum disorder (ASD) refers to a heterogeneous neurodevelopmental condition whose 
symptoms range from mild to severe. ASD is childhood-onset and lifelong. It affects between 1 and 
2% of the population[1]  and is characterized by social communication deficits, repetitive and unusual 
sensory-motor behaviors, as well as restricted interests [2]. The literature indicates an estimation that 
about 55% of people diagnosed with ASD also exhibit intellectual disabilities [3], and about 25% of 
this population are pre-or minimally verbal [4]. The sensory processing disturbances and the 
communication difficulties of people with ASD can cause challenging behaviors (CBs) to emerge [5–
10]. 
The term CB is used to describe a broad range of unusual behaviors shown by individuals with 
intellectual disabilities and ASD. They include aggression, destructiveness, self-injurious, and a range 
of other behaviors such as unacceptable social and sexual conduct, screaming, non-compliance, and 
eating inedible objects [11,12]. The fundamental forms of CBs are, among others, self-injurious and 
aggression, with the prevalence of these increasing into the teenage years [11]. Most of the studies 
report on high rates of CBs among individuals with ASD, with a prevalence of up to 94% of these 
individuals presenting at least one type of CB [13,14]. Other studies reported on the appearance of 
CBs among 82% of participants, with 32.5% involving aggressive behavior towards themselves or 
others  [15,16]. CBs may significantly impair the physical and mental health and or quality of life of 
the persons presenting such behaviors, those who care for them, those who live nearby, and, in 
extreme cases, may even result in death [17–20]. 
Moreover, the consequences of CBs can go far beyond their immediate physical impact since 
individuals with CBs are significantly more likely to be excluded from community-based services 
and be retained in institutional settings [21]. Moreover, such individuals are likely to be excluded 
from services provided within these settings [15]. CBs may also be an obstacle to learning new skills, 
particularly in school settings [22]. These behaviors place the child and others around them at risk for 
physical injury while limiting involvement in educational activities [23]. CBs are considered as 
constant distress to the people supporting individuals with ASD, such as caregivers, family members, 
and friends. 
On top of the adverse physical outcomes of aggressive behaviors, these behaviors present an 
emotional burden expressed by depression and anxiety, leading to the enhancement of support teams, 
thereby enhancing financial burden for the family and the educational/rehabilitation systems 
supporting these individuals [24]. Moreover, another extra burden of aggressive CBs is the common 
usage of potent antipsychotic drugs with significant side effects [25]. These behaviors might also 
prevent the parents from paying attention to siblings of the child with ASD and might lead to the 
child's removal from the home environment and placing him in an institution. 
Applying therapeutic strategies in cases where the person with ASD experiences CBs is intensely 
warranted forasmuch as the CB becomes established as part of a child's behavioral repertoire, it is 
unlikely to decrease and will typically remain or worsen without intervention [26]. An effective 
intervention alerting the outburst of CB and positively lessening the severity of CB might enhance 
the involvement of the person with ASD within society, reduce the financial and emotional burden 
of the family and caregivers, decreasing the need for medications [27]. 
Several forms of intervention have been proposed for established CB in people with intellectual 
disabilities as well as ASD, including medications [28–31], behavioral intervention [32–41], 
cognitive/emotion-oriented interventions [42–47], sensory stimulation/integration interventions [48–

53], music therapy [54–58], psychosocial intervention, [59–63] communication training [64–69], 
physical exercises, [70–74] and others [75–80]. Despite the wide availability of intervention modes, 
no consensus has been reached for established and high effective CB treatment. However, most 
interventions aim to prevent the occurrence of CB, guiding the person to more adaptive behaviors 
avoiding having to manage the consequences of CB. 
In line with this prevention strategy, the literature recently enriched with the proposal of using 
technological devices to predict the CB occurrence based on the individual with ASD's physiological 
parameters. The presence of atypical physiological arousal in people with ASD has been known for 
a long time, and the functional relation between homeostatic regulation and CB has already been 
hypothesized [81–83]. More recently, atypical autonomic reactivity was reported as a common 
feature in people with ASD, possibly occasioning CB when environmental demand exceeds the 
person's coping ability [84–87]. However, high variability of physiological reactivity to external 
stimuli was reported along with the possible existence of subtypes of people with ASD characterized 
by different physiological profiles (e.g., hypo-aroused, "normally" aroused, and hyper-aroused) 
[86,88,89]. The use of physiological-biological signals such as the electrocardiogram (ECG), heart 
rate (HR) and HR variability (HRV), respiratory rate (and changes in respiratory rate), as well as body 
movements are reiterated in several articles as markers for CB in people with autism [90–94]. 
Smart Wearable Shirts (SWS) are wearable medical devices (WMD) that are considered as a 
technological breakthrough, enabling continuous surveillance regarding human vital physiological 
signs without any disturbance to the activities of daily living (ADL). The SWS technology mentioned 
above has been used in clinical research for the last two decades. During the last few years, SWS 
enabled the collection of varied physiological data outside the laboratory for long durations (weeks). 
The constant surveillance enabled by these devices allows the identification of physiological 
anomalies that deviate from the typical individual's behaviors that can be received, analyzed, and 
treated [95]. Furthermore, garments, such as t-shirts, have been found as the second most preferred 
device to be used by individuals with ASD [96], with a moderate to high suitability index for this 
population [94]. However, few studies have used SWS with individuals with ASD so far [94,97]. 
The analysis of physiological data can be achieved by Machine Learning (ML) concept. It is 
recognized as a method to provide automated approaches and tools for data analysis [98]. It utilizes 
the algorithms by machines to detect aspecific patterns in the data through a training process [99]. 
Deep learning (DL) is a subfield of ML that attempts to learn high-level abstractions in the data by 
utilizing hierarchical architectures. It is perceived as an emerging approach and has been widely 
applied in traditional artificial intelligence domains, such as semantic parsing, transfer learning, NLP, 
computer vision, and many more [100]. Supervised learning is a discipline in ML and DL in which 
the data provides labeled targets for the algorithms. The overall aim of supervised learning is to find 
a function f(x) that predicts a target y [98]. Several studies aimed to classify emotional stress and CBs 
using ML and DL-based methods based on bio-signals and wearable devices. While some gained 
impressive results [101–103], none of them tried to predict CBs among participants with ASD. The 
current study presents the utilization of a learning algorithm to classify if a measured series of input 
bio-signal variations is considered as a current CB or if a CB is likely to be developed among adults 
with ASD.  
The current protocol consists of three phases, each with a specific goal. The first aim is to assess the 
differences in physiological reaction measured with an SWS between adults with high-functioning 
ASD and typically developed peers. The second goal is to learn which physiological parameters can 
best predict the CB. The prediction power of a suggested parameter will be confirmed by using a 

learning algorithm utilized for real-time CB prediction, combined with an ad-hoc developed 
smartphone application that sends an alert notification when the CB is likely to occur. Finally, the 
present investigation aims to test the developed system with people with ASD and assess its 
acceptability and usefulness for users with ASD and their caregivers. 
MATERIALS AND METHODS 
Study design 
An observational study design will be implemented in the first two phases of the current research. In 
phase one, participants' physiological reactions to two visual stimuli will be collected and analyzed. 
The physiological characteristics of CBs presented by people with ASD will be collected in phase 
two. A single case study with a mixed-method design will be implemented in phase three, where the 
system validity proof of concept will be performed. 
Ethics and safety issues 
The research proposal was approved by the Ariel University institutional review board (AU-HEA-
ML-20201203). The study will be carried out following the Declaration of Helsinki principles. At the 
recruitment, written informed consent will be collected from all participants or their legal guardians. 
The SWS planned to use in the current protocol is a non-invasive medical device. However, if a 
participant does not tolerate SWS, he can withdraw from the study at any time without any 
repercussions.  
Participants 
Sample size calculation was performed to identify the number of participants needed to obtain the 
statistical significance. According to the performed calculation, a group of 20 subjects diagnosed with 
high functioning ASD whose age will range between 20 and 40 years residing at home (observation 
group – OG) along with an age and sex-matched control group (CG) of 20 typical developed peers 
will be enrolled in the first protocol phase. For the study's second phase, 10 people with ASD 
presenting aggressive or disruptive CBs whose age will range between 20 and 40 years and their 
caregivers will be recruited. Finally, one participant with ASD whose age will be between 20 and 40 
years attending a special school and living at home, which exhibits aggressive or disruptive CBs, will 
participate in the third phase of the research. 
Data collection 
Smart Wearable Shirt 
The Hexoskin SWS (Hexoskin Inc., Montreal, QC, Canada) is a wearable device shirt that has several 
sensors aimed to measure physiological signals. The shirts will be used in the current study. The 
wearable shirt is declared by its producer as a non-invasive SWS with textile embedded sensors that 
allow the collection of multiple parameters. A detailed description of sensors equipped in the 
Hexoskin SWS is available on the producer's website (https://www.hexoskin.com).  The data that will 
be collected from the Hexoskin SWS will be ECG, R-R intervals, and N-N intervals. ECG is defined 
as a raw time-voltage signal that represents the heart's electrical activity from one instant to the next 
[104]. The ECG is collected by the Hexoskin SWS, with a sampling frequency of 256Hz, and 12-bit 
resolution. R-R interval is defined as the heart function performed by two main steps: the activation 
step, called depolarization and labeled as QRS complex, followed by the resting state, the 
repolarization. The R-R interval is defined as the time interval between two consecutive QRS 
complexes [104]. The Hexoskin SWS provides an in-build analysis of R-R interval performed off-

line and given as a CSV file. The R-R interval analysis of an ECG segment consists of both normal 
and abnormal R-R interval samples. The normal R-R interval is called N-N interval and represents 
ideally pure sinus beats and is best for ECG analysis purposes. The abnormal R-R interval differs 
from sinus rhythm in their length, and they represent disturbances of both technical and physiological 
origins [105]. The N-N intervals analysis is performed off-line by the Hexoskin SWS and is provided 
as a CSV file. The Hexoskin SWS will be used in all three phases of the research. 
Behavioral diary 
The caregivers of participants enrolled in phase two will be asked to fill out a daily behavioral diary 
reporting on the arousal level of the subject. Three arousal levels will be collected: quiet, agitated, 
and CB. "Quiet" state refers to a period in which the subject is relaxed or doing a minimum activity 
peacefully (e.g., resting on the sofa; playing peacefully with puzzles; quietly doing homework). 
Moments in which the participant is sleeping, is excited, is carrying out an activity that involves him 
a lot (e.g., activities in which he laughs a lot), and is doing sports (even in calm) will not be referred 
to the "quiet" state. "Agitated" describes a behavioral activation state higher than "Quiet". It can 
correspond to situations in which a physiological reaction is observed, such as redness, sweating, 
increased respiratory rate, and others. It can occur in cases of euphoria (e.g., the subject is watching 
a show that he enjoys very much), intense activity (e.g., doing a sport activity), anger (e.g., the subject 
has been told that he cannot do an activity that he has requested and therefore vigorously protests) or 
tiredness (e.g., the subject has had a long day and is intensely protesting because he wants to go 
home), but cannot be defined as CB. "CB" arousal state will be defined as an extreme agitation state 
with an intense physiological response (redness, sweating, or increased respiratory rate) which can 
be accompanied by fierce anger (with or without aggressive or disruptive behaviors), strong states of 
anxiety, or a need to move. In general, “CB” should correspond to reactions identified as exaggerated, 
excessive for the situation, and inadequate to the social context. Aggression behaviors will include 
self- or other-directed physical or verbal aggression. For each arousal level reported, caregivers will 
be asked to report on the following items: the beginning and ending time and date of the state, and a 
brief specification note describing the type of activity that the participant is doing and the topography 
of the occurred behavior. The behavioral diary will be collected within the second protocol phase. 
Quebec User Evaluation of Satisfaction with Assistive Technology 
The Quebec User Evaluation of Satisfaction with Assistive Technology second edition (QUEST 2.0) 
[106] is defined as a 12-item questionnaire designed to assess users' satisfaction with a wide range of 
assistive technology. The items were designed based on the literature referred to the assistive 
technology assessment and the Matching Person and Technology model [107]. The 12 items are 
grouped in two areas representing the user satisfaction with the assistive technologies related to the 
assistive device (eight items) and provided service (four items). A score on a five-point Likert scale 
is given to each item from 1 ("not satisfied at all") to 5 ("very satisfied"). A space for comments is 
provided next to each item to identify the causes of user satisfaction or dissatisfaction. At the end of 
the QUEST 2.0 form, a list with the 12 investigated aspects of satisfaction is presented, and the user 
is asked to choose the three aspects most important to him. Strong psychometric proprieties were 
published for the QUEST 2.0 [108]. The QUEST 2.0 will be administered in phase three of the current 
protocol with the participant's caregivers. 
Focus group 
The focus group is defined as a qualitative data collection method often used in health research. The 
technique is used to produce a controlled discussion on specific issues within a group of people who 

share different relations with the focused topics [109,110]. Under the focus group method, the group 
discussion is recorded, transcribed, and analyzed. In addition, a search for themes relevant to the 
investigated topic and the group agreement on such themes is performed [111]. Research questions 
that will be raised during the focus group of the current protocol are as follows:  
• Did wearing the SWS upset the participants? 
• Was the system able to detect all relevant CB? 
• Was the system operation speed sufficient to allow the in-time application of appropriate 
prevention strategies? 
• Has the use of the system reduced the amount of CB? 
• What improvements can be applied to the system to increase its effectiveness? 
Procedure 
The research procedure outlined in Figure 1. The protocol's expected starting date is June 2022. 
---------------------------------------INSERT FIGURE 1 ABOUT HERE------------------------------------- 
Phase one – comparison between people with and without ASD physiological outcomes 
For the first phase of the current protocol, the physiological parameters of the people in the OG and 
CG will be acquired using the Hexoskin SWS while participants watch two different five-minute 
videos. One video will show relaxing images and will emit relaxing music (relaxing video). The 
second video will present impressive human body deformities accompanied by anxious music 
(disturbing video). Both videos will be presented to the participant when he is in a sitting position. 
Before starting the relaxing video, the participant will be invited to relax and lean back onto the chair's 
backrest. The participant can close his eyes or keep them open at his discretion to promote relaxation. 
To watch the disturbing video, participants will be asked not to lean against the chair backrest and 
keep their eyes open for the duration of the video watching. The first video watched by each 
participant will be chosen randomly between the two videos. The duration of the entire session will 
be approximately 15 minutes. 
Phase two - classify the variations of the physiological parameters in people with ASD 
Each participant enrolled in phase two of the protocol will be asked to wear the Hexoskin SWS for 
seven consecutive days during waking hours, performing his usual daily activities. During the same 
seven days, the caregivers who care for the participant will be asked to report the participant's status 
in the behavioral diary. Each evening the data collected by the Hexoskin SWS will be uploaded in an 
online cloud along with the behavioral diary of the day. Once the data from all the 10 participants 
have been collected, an DL algorithm will be developed to learn the variations in the individual's 
physiological parameters that occur before a CB and predict future CB. Moreover, a smartphone 
application will be developed to receive the Hexoskin SWS data in real-time and send it to a remote 
server where it is analyzed through the developed algorithm and the classified CB events will be 
extracted and presented on the applications’ notification screen. In other words: if the algorithm 
detects the possibility of an incoming CB, a notification is sent to the caregiver's smartphone to inform 
of the possible advent of a CB, therefore enabling the implementation of the selected intervention 
strategy. The system architecture is explained in Figure 2. 
 ---------------------------------------INSERT FIGURE 2 ABOUT HERE------------------------------------ 
Phase three – System proof of concept  

The developed system prototype and its efficacy will be tested on one participant with ASD for seven 
days with healthcare professionals and teachers in a special school setting for one week. The 
participant will wear the Hexoskin SWS during all hours of attendance at the special school. At the 
end of the seven days, the QUEST 2.0 will be administered to each professional and teacher who will 
interact with the system, in addition, to a focus group that will be carried out with them to address the 
research questions mentioned above. Information obtained from the QUEST 2.0 administration will 
be discussed within the focus group. In the last part of the focus group, a summation of solutions to 
each research question will be proposed to the group, and the number of participants that agree or 
disagree with the proposed summation answers will be collected. 
Data analyses 
Phase one 
Data collected by the Hexoskin SWS from participants in the OG and CG will be analyzed and 
compared. From ECG received data, HR will be calculated between two consecutive QRS complexes. 
Considering the time interval between two QRS complexes as “t”, the corresponding temporal HR 
will be 60/t [112]. In order to remove unwanted artifacts from HR, a percentage threshold value will 
be set using the sliding window method and minimum allowed peak width will be identify.  The 
removal process will be performed for positive and negative peaks in two rounds. A window will be 
slid over the HR signal, and its median value will be calculated. The maximum (positive and negative) 
allowed peak amplitude will be determined for every window by multiplying the window’s median 
value for a threshold value. Threshold value for positive peaks was set at 30% (for the first removal 
round) and 25% (for the second removal round) of the window’s mean value. For negative peaks, 
threshold value was set at 50% (for the first removal round) and 30% (for the second removal round) 
of the window’s mean value. Then, all peaks with amplitude bigger than the allowed value will be 
identified from every window. If one of these peaks is narrower than the minimum allowed peak 
width, it will be replaced with the reference window median value. Otherwise, if an identified peak 
width is bigger than the allowed peak width, it value will be replaced with the maximal allowed HR 
(for positive peaks) or the minimal allowed HR (for negative peaks). The maximal allowed HR will 
be calculated with the following formula: "209 −(0.7 × (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑎𝑎𝑎𝑎𝑎𝑎))" [113]. The minimal 
allowed HR will be 60 bpm [114]. After removing abnormal peaks, the signal will be filtered with a 
Gaussian filter with a sigma equal to 1. After the HR signal filtering process, the obtained cleaned 
HR signal will be used to classify the participant stress within the following levels: “no stress”, “mild 
stress”, “moderate stress”, and “high stress”. Each stress level will refer to an HR signal that is 
positioned within a specific range of values. The “no stress” level will include the HR values below 
90% of the cleaned HR signal's lowest peak. If this HR value is lower than  The “high stress” level 
will comprise values above 90% of the cleaned HR signal’s highest peak. If this value is above the 
maximal allowed HR, it is substitute with 90% of the maximal allowed HR value. The range left 
between these two thresholds will be divided into two equal parts (lower and upper half). The HR 
data positioned in the lower half of this range will be classified as "mild stress" and those positioned 
in the upper half as "moderate stress". Each HR value will be classified and assigned with a numerical 
value corresponding to a stress level (“no stress” = 0, “mild stress” = 1, “moderate stress” = 2, and 
“high stress” = 3). After acquiring the sequences of the stress levels of all participants of Phase one, 
the sequences of the subjects in the OG and CG will be compared using a version of the Smith-
Waterman algorithm adapted for the analysis of the obtained data.  
Phase two 

The data gathered by the Hexoskin SWS from participants enrolled in phase two will be analyzed as 
described above and a DL algorithm will be developed in order to predict the incoming participants’ 
stress levels. To find CBs patterns among subjects, the authors intend to construct a classifier based 
on supervised learning to find anomalies in the subject's data that might indicate on CB that is about 
to occur. A long-short term memory (LSTM) algorithm will be taught to recognize data patterns 
corresponding to CBs occurrence using the data collected by the participants' caregivers through the 
behavioral diary and the information collected in Phase one.  
LSTM is an extension of the recurrent neural network (RNN). In contrast to other application of ML 
and DL, in the process of analyzing and predicting time series information, each data point is based 
on previous information, which  must be examined as well. RNN is the most used network for time 
series applications since it can form the target vector observing the current input data history, using 
shared wights among the hiding units of the network across each time step of the data. The authors 
choose the use of LSTM, and not the RNN, because RNN has one significant problem that is called 
the vanishing gradient, where gradient of output error is based on previous inputs vanishes when time 
lags between inputs and errors increases. To overcome this problem the LSTM is introduced. LSTM 
has a memory, which come to practice by replacing the nonlinear units of RNN in the hidden layers 
with memory blocks. The network propagate error throughout the entire network, and as a result, it 
is capable of learning long-term dependencies, and forgetting unnecessary information based on the 
data at hand [115]. 
The accuracy of the prediction model will be calculated according to common estimation methods 
such as the confusion matrix and the area under the curve (AUC) Values. These values range from 
0.5 to 1, with 1 being perfect classification and 0.5 being no better than luck. 
Phase three 
The themes that will emerge from the focus group will be extracted from the discussion transcription. 
Axial coding strategy will be applied to calculate the extensiveness of each theme discussion. This 
qualitative data analysis consists of assigning a reference number to each theme and then marking 
any sentence related to that theme with that number. A reliability check for the code-to-sentence 
matches will be applied by giving the list of codes to an independent researcher experienced in 
qualitative analysis and asking him to identify the sentence that matches each code.[111] The level 
of agreement to each summation answer to the research questions will be obtained by calculating the 
percentage of participants that agree with the proposed statement. The authors will discuss the 
developed answers to the research questions in the light of the relevant emerged themes and level of 
agreement of the discussion group.  
Dissemination 
The authors plan to submit a manuscript on the identified differences in physiological arousal between 
people with and without ASD. A second manuscript will describe the system development procedure 
and results obtained from the proof of concept (phase three). These manuscripts will be submitted to 
peer-reviewed journals for publication. Obtained results will be presented at academic conferences 
and used for professional training. The developed smartphone application will be freely available for 
download from the main android-based application distribution sources (e.g., Google Play) at the end 
of the study. The authors' institutions and networks will support the result diffusion with 
dissemination strategies. 
DISCUSSION 

Many individuals with ASD present aggressive or disruptive CB. Although interventions were 
proposed to cope with such behaviors, they frequently occur unexpectedly. There is a need for 
effective strategies to support such interventions anticipating the occurrence of CB. To the authors' 
knowledge, this study represents the first attempt of using SWS and simultaneous multiple 
physiological parameters analysis to predict CBs. The system that will be developed within the 
current project can improve the quality of life of people with ASD presenting aggressive and 
disruptive CBs as well as their caregivers and health professionals, helping in carrying out the selected 
intervention modality to reduce CB and prevent the onset of burnout symptoms. Moreover, the 
proposed system will be cost-effective, easy to use even for non-expert users, and widely accessible 
by buying the SWS and downloading the smartphone application. Furthermore, the results that will 
be obtained at the end of this project can be helpful for further development of wearable devices to 
predict CB, a field of growing interest among health and medical researchers. Finally, the mixed-
method design proposed for phase three of the protocol will involve health care professionals that 
cope daily with CB. Their participation will allow the integration of their clinical experience and 
perceived needs in the proposed system improvement and provide valuable information to be kept 
into consideration for future development of similar devices. 
AUTHORS’ CONTRIBUTIONS 
MZ obtain the funds for the current research project. MZ and ML designed the protocol and selected 
the SWS to be used. MZ and HH identified the adequate data analysis for the algorithm development. 
ML and AR defined the methodology for the clinical part of the data collection (behavioral diary and 
focus group). AR wrote the protocol text. All the authors read the protocol draft and suggested 
improvement until consensus was reached. 
FUNDING STATEMENT 
This work was supported by the Israeli Innovation Authority. 
COMPETING INTERESTS STATEMENT 
The authors have no competing interest to declare. 
 

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Figure 1. Protocol flow-chart. OG = Observation group; CG = Control group; SWS = Smart wearable 
shirt; ASD = Autism spectrum disorder; CB = Challenging behavior; QUEST 2.0 = Quebec User 
Evaluation of Satisfaction with Assistive Technology 2nd edition. 
 
 

Figure 2. Brief system architecture description. Physiological signals are captured by the Hexoskin 
smart wearable shirt (SWS) and recorded by the provided data recorder. Recorded data are transferred 
in real-time to the caregiver smartphone via Bluetooth and then sent to a remote server via an internet 
connection where they are analyzed. If the ad-hoc developed algorithm detects the incoming 
occurrence of challenging behavior (CB), the remote server immediately sends a notification alert on 
the caregiver's smartphone.