Detection of Challenging Behaviours of Children with Autism Using Wearable Sensors during Interactions with Social Robots

Alban, Ahmad Qadeib; Ayesh, Malek; Alhaddad, Ahmad Yaser; Al‐Ali, Abdulaziz; So, Wing-Chee; Connor, Olcay; Cabibihan, John-John

doi:10.1109/ro-man50785.2021.9515459

Cited by 19 publications

(14 citation statements)

References 25 publications

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“…Technology development is often specific to a single population, as is the case with autism spectrum disorder (e.g., Ref. 72 ) or dementia (e.g., Ref. 45 ), and not tested with other populations.…”

Section: Discussionmentioning

confidence: 99%

“…79 Hence, future technologies could act as a caregiver strategy to manage adult challenging behaviours, such as wearable sensors and social robots developed for other specific paediatric populations to detect real time challenging behaviours and intervene early. 72 Conversely, attention must be given not to over-involve family caregivers, as challenging behaviours may also present when individuals with TBI experience a lack of control. 26 In other words, individuals with TBI must remain at the centre of care, as encouraged by highly individualised clinical models.…”

Section: The Involvement Of Family Caregivers In the Use Of Technologymentioning

confidence: 99%

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Managing challenging behaviours in adults with traumatic brain injury: A scoping review of technology-based interventions

Hendryckx,

Nalder,

Drake

et al. 2023

Journal of Rehabilitation and Assistive Technologies Engineerin

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Challenging behaviours are one of the most serious sequelae after a traumatic brain injury (TBI). These chronic behaviours must be managed to reduce the associated burden for caregivers, and people with TBI. Though technology-based interventions have shown potential for managing challenging behaviours, no review has synthesised evidence of technology aided behaviour management in the TBI population. The objective of this scoping review was to explore what technology-based interventions are being used to manage challenging behaviours in people with TBI. Two independent reviewers analysed 3505 studies conducted between 2000 and 2023. Studies were selected from five databases using search strategies developed in collaboration with a university librarian. Sixteen studies were selected. Most studies used biofeedback and mobile applications, primarily targeting emotional dysregulation. These technologies were tested in a variety of settings. Two interventions involved both people with TBI and their family caregivers. This review found that technology-based interventions have the potential to support behavioural management, though research and technology development is at an early stage. Future research is needed to further develop technology-based interventions that target diverse challenging behaviours, and to document their effectiveness and acceptability for use by people with TBI and their families.

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Section: Discussionmentioning

confidence: 99%

Section: The Involvement Of Family Caregivers In the Use Of Technologymentioning

confidence: 99%

Managing challenging behaviours in adults with traumatic brain injury: A scoping review of technology-based interventions

Hendryckx,

Nalder,

Drake

et al. 2023

Journal of Rehabilitation and Assistive Technologies Engineerin

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“…SVM has been considered in different areas and in many different applications such as in cancer genomics ( Huang et al, 2018 ), chemistry ( Ivanciuc, 2007 ), autism therapy ( Alban et al, 2021 ), and bioinformatics ( Byvatov and Schneider, 2003 ). A custom-built optical sensor was used to investigate the relationship between wavelengths and glucose concentrations in aqueous solutions containing different glucose concentrations ranging from 40 to 250 mg/dl ( Shokrekhodaei et al, 2021 ).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

Alhaddad

Aly

Gad

et al. 2022

Front. Bioeng. Biotechnol.

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Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.

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“…Social robots can be used to address the issue of aggression and undesirable behaviours among children to prevent progression and potential harm. In combination with other sensors and wearables, a social robot can identify the occurrence of these negative interactions of children with their surroundings (e.g., toys) and respond appropriately to that action [24,30]. The robot's responses to the child can take on different forms (e.g., gestures and sounds) and should be clear enough to the child to comprehend [31].…”

Section: Introductionmentioning

confidence: 99%

“…For example, a study used an accelerometer embedded in a belt to detect falls with an accuracy of 99.4% using a machine learning classifier [39]. In another application in healthcare, a study considered using a wearable device to predict the occurrence of challenging behaviours among children with autism using machine learning techniques [30]. Motion sensors are also being considered in applications that require direct interactions with robots, such as robot-based games [40,41].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Social Robot’s Responses to Undesired Interactions Between Children and their Surroundings

Alhaddad

Cabibihan

Bonarini

2022

Int J of Soc Robotics

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Aggression in children is frequent during the early years of childhood. Among children with psychiatric disorders in general, and autism in particular, challenging behaviours and aggression rates are higher. These can take on different forms, such as hitting, kicking, and throwing objects. Social robots that are able to detect undesirable interactions within its surroundings can be used to target such behaviours. In this study, we evaluate the performance of five machine learning techniques in characterizing five possible undesired interactions between a child and a social robot. We examine the effects of adding different combinations of raw data and extracted features acquired from two sensors on the performance and speed of prediction. Additionally, we evaluate the performance of the best developed model with children. Machine learning algorithms experiments showed that XGBoost achieved the best performance across all metrics (e.g., accuracy of 90%) and provided fast predictions (i.e., 0.004 s) for the test samples. Experiments with features showed that acceleration data were the most contributing factor on the prediction compared to gyroscope data and that combined data of raw and extracted features provided a better overall performance. Testing the best model with data acquired from children performing interactions with toys produced a promising performance for the shake and throw behaviours. The findings of this work can be used by social robot developers to address undesirable interactions in their robotic designs.

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Detection of Challenging Behaviours of Children with Autism Using Wearable Sensors during Interactions with Social Robots

Cited by 19 publications

References 25 publications

Managing challenging behaviours in adults with traumatic brain injury: A scoping review of technology-based interventions

Managing challenging behaviours in adults with traumatic brain injury: A scoping review of technology-based interventions

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

Real-Time Social Robot’s Responses to Undesired Interactions Between Children and their Surroundings

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