Automatic detection of stereotyped movements in autistic children using the Kinect sensor

Jazouli, Maha; Majda, Aicha; Merad, Djamal; Aalouane, Rachid; Zarghili, Arsalane

doi:10.1504/ijbet.2019.097621

Cited by 12 publications

(7 citation statements)

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“…One of them [ 32 ] has the goal of detecting the hand wave (flapping) gesture with the Kinect sensor, achieving an accuracy of 51%. Another work [ 52 ] tried to detect other stereotypes behaviors besides the hand wave. In order to have the same basis of comparison, Table 6 compares both works with the present work in terms of accuracy for the similar studied behaviors ‘HAND_WAVE’ and ‘ROCKING’.…”

Section: Resultsmentioning

confidence: 99%

Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural Network

Silva

Soares

Leão

et al. 2021

Sensors

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Individuals with Autism Spectrum Disorder (ASD) typically present difficulties in engaging and interacting with their peers. Thus, researchers have been developing different technological solutions as support tools for children with ASD. Social robots, one example of these technological solutions, are often unaware of their game partners, preventing the automatic adaptation of their behavior to the user. Information that can be used to enrich this interaction and, consequently, adapt the system behavior is the recognition of different actions of the user by using RGB cameras or/and depth sensors. The present work proposes a method to automatically detect in real-time typical and stereotypical actions of children with ASD by using the Intel RealSense and the Nuitrack SDK to detect and extract the user joint coordinates. The pipeline starts by mapping the temporal and spatial joints dynamics onto a color image-based representation. Usually, the position of the joints in the final image is clustered into groups. In order to verify if the sequence of the joints in the final image representation can influence the model’s performance, two main experiments were conducted where in the first, the order of the grouped joints in the sequence was changed, and in the second, the joints were randomly ordered. In each experiment, statistical methods were used in the analysis. Based on the experiments conducted, it was found statistically significant differences concerning the joints sequence in the image, indicating that the order of the joints might impact the model’s performance. The final model, a Convolutional Neural Network (CNN), trained on the different actions (typical and stereotypical), was used to classify the different patterns of behavior, achieving a mean accuracy of 92.4% ± 0.0% on the test data. The entire pipeline ran on average at 31 FPS.

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Section: Resultsmentioning

confidence: 99%

Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural Network

Silva

Soares

Leão

et al. 2021

Sensors

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“…Validation of these data with 2D video data suggested that automatic ‘hand flapping’ detection delivers valuable information for monitoring autistic children, as in the case of special needs schools. Jazouli et al [47] also used the same sensor but based their analysis on a $P Point-Cloud Recogniser to automatically detect body rocking, hand flapping, fingers flapping, hand on the face and hands behind back with an overall mean accuracy of 94%.…”

Section: Supporting Assessment: Identification Of Asd Related Featmentioning

confidence: 99%

“…Rynkiewicz et al [47] studied the role of non-verbal communication in the setting of an ADOS-2 assessment for children aged 5–10. They used a 3D sensor for automatic gesture analysis of the upper body, while the boys ( N = 17) and girls ( N = 16) with high-functioning ASD performed two assessment-related tasks.…”

Section: Supporting Assessment: Identification Of Asd Related Featmentioning

confidence: 99%

Sensor-Based Technology for Social Information Processing in Autism: A Review

Kowallik

Schweinberger

2019

Sensors

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The prevalence of autism spectrum disorders (ASD) has increased strongly over the past decades, and so has the demand for adequate behavioral assessment and support for persons affected by ASD. Here we provide a review on original research that used sensor technology for an objective assessment of social behavior, either with the aim to assist the assessment of autism or with the aim to use this technology for intervention and support of people with autism. Considering rapid technological progress, we focus (1) on studies published within the last 10 years (2009–2019), (2) on contact- and irritation-free sensor technology that does not constrain natural movement and interaction, and (3) on sensory input from the face, the voice, or body movements. We conclude that sensor technology has already demonstrated its great potential for improving both behavioral assessment and interventions in autism spectrum disorders. We also discuss selected examples for recent theoretical questions related to the understanding of psychological changes and potentials in autism. In addition to its applied potential, we argue that sensor technology—when implemented by appropriate interdisciplinary teams—may even contribute to such theoretical issues in understanding autism.

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“…Gesture recognition devices such as LeapMotion have been used for games to improve cognitive and motor skills in people with attention deficit and hyperactivity disorder (ADHD) (Garcia-Zapirain et al, 2017), or autism spectrum disorder (ASD) (Cai et al, 2018). Body movement detection has also been used to detect anxiety or depression based on walking posture (Zhao et al, 2019), or stereotypies in ASD (Jazouli et al, 2019). Regarding eye-tracking technology, it has been helpful to observe differences in the patterns of users with or without ASD (Eraslan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Extended reality for mental health: Current trends and future challenges

Pons

Navas-Medrano

Soler-Domínguez

2022

Front. Comput. Sci.

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Virtual and augmented reality have been used to diagnose and treat several mental health disorders for decades. Technological advances in these fields have facilitated the availability of commercial solutions for end customers and practitioners. However, there are still some barriers and limitations that prevent these technologies from being widely used by professionals on a daily basis. In addition, the COVID-19 pandemic has exposed a variety of new scenarios in which these technologies could play an essential role, like providing remote treatment. Disorders that traditionally had received less attention are also getting in the spotlight, such as depression or obsessive-compulsive disorder. Improvements in equipment and hardware, like Mixed Reality Head Mounted Displays, could help open new opportunities in the mental health field. Extended reality (XR) is an umbrella term meant to comprise Virtual reality (VR), mixed reality (MR), and augmented reality (AR). While XR applications are eminently visual, other senses are being explored in literature around multisensory interactions, such as auditory, olfactory, or haptic feedback. Applying such stimuli within XR experiences around mental disorders is still under-explored and could greatly enrich the therapeutic experience. This manuscript reviews recent research regarding the use of XR for mental health scenarios, highlighting trends, and potential applications as well as areas for improvement. It also discusses future challenges and research areas in upcoming topics such as the use of wearables, multisensory, and multimodal interaction. The main goal of this paper is to unpack how these technologies could be applied to XR scenarios for mental health to exploit their full potential and follow the path of other health technologies by promoting personalized medicine.

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Automatic detection of stereotyped movements in autistic children using the Kinect sensor

Cited by 12 publications

References 0 publications

Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural Network

Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural Network

Sensor-Based Technology for Social Information Processing in Autism: A Review

Extended reality for mental health: Current trends and future challenges

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