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Extracting gait and balance pattern features from skeleton data to diagnose attention deficit/hyperactivity disorder in children

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Abstract

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting various aspects of life. Some features of the mental disorders affect people's movement patterns. In the recent decade, researchers have paid attention to the analysis of gait and balance pattern using new technological tools, as well as artificial intelligence algorithms. Therefore, the present study aims to propose an intelligent method to identify ADHD in children using gait and balance pattern features extracted from the person’s movements obtained from the skeleton data. Given that designing and extracting effective motor features for diagnosing the aforementioned disorder is the main objective. In the present applied development experimental study, human movement samples related to the gait and balance were recorded in the standard test of perceptual-motor development, from healthy and ADHD-diagnosed children. After preprocessing the data recorded by the Kinect device, effective features for diagnosis are designed and extracted from the appropriate special movement tests. Comparing the features extracted from gait and balance tests by skeleton data, the results indicated that the data based on other types of methods for differentiation into healthy and ADHD groups are in line with those of the present study. The results of diagnosis and separation of healthy children from those with disorders in the different movement tests, standing on the ground with the superior foot, standing on a balance stick with the superior foot, and walking heel forward on a balance stick, to identify ADHD by SVM classification method are 86.4%, 90.2%, and 88.1%, respectively. The obtained significant results have been achieved relying on machine learning-based methods using the effective features obtained from skeleton gait and balance data of children along with analyzing the descriptive statistics of the features of gait and balance tests.

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Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran

    Faezeh Rohani & Kamrad Khoshhal Roudposhti

  2. Department of Motor Behavior, Ferdowsi University of Mashhad, Mashhad, Iran

    Hamidreza Taheri

  3. Department of Clinical Psychology, Ferdowsi University of Mashhad, Mashhad, Iran

    Ali Mashhadi

  4. Brain and Trauma Foundation Grisons/Switzerland, Chur, Switzerland

    Andreas Mueller

Authors
  1. Faezeh Rohani
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  2. Kamrad Khoshhal Roudposhti
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  3. Hamidreza Taheri
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  4. Ali Mashhadi
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  5. Andreas Mueller
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Contributions

FR participated in all stages of this study such as data collecting, analysis, implementation, and writing the manuscript. KKR designed the model and the computational framework and analyzed the data, and participate in writing the manuscript. HRT designed motor behavior tasks for the modeling process and data collecting process. AM and AM helped in data collecting process (an interview with subjects and labeling subjects). All authors conceived the study and were in charge of overall direction and planning.

Corresponding author

Correspondence to Kamrad Khoshhal Roudposhti.

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Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

The questionnaire and data and methodology for this study were approved by the Human Research Ethics committee of the Islamic Azad University, Lahijan Branch. (Ethics approval number: REC.IAU.LIAU.IR.1398.013). For each subjects in this study, we have consent to participate.

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Rohani, F., Khoshhal Roudposhti, K., Taheri, H. et al. Extracting gait and balance pattern features from skeleton data to diagnose attention deficit/hyperactivity disorder in children. J Supercomput 80, 8330–8356 (2024). https://doi.org/10.1007/s11227-023-05731-0

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