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Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques

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Abstract

Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To support an SIB monitoring system for autism spectrum disorder (ASD), we evaluated machine learning methods for detecting and distinguishing diverse SIB types. SIB episodes were captured with body-worn accelerometers from children with ASD and SIB. The highest detection accuracy was found with k-nearest neighbors and support vector machines (up to 99.1% for individuals and 94.6% for grouped participants), and classification efficiency was quite high (offline processing at ~ 0.1 ms/observation). Our results provide an initial step toward creating a continuous and objective smart SIB monitoring system, which could in turn facilitate the future care of a pervasive concern in ASD.

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Abbreviations

ASD:

Autism spectrum disorder

SIB:

Self-injurious behavior

SMM:

Stereotypical motor movement

SVM:

Support vector machine

DA:

Discriminant analysis

DT:

Decision tree

nB:

Naïve Bayes

kNN:

k-nearest neighbor

NN:

Neural networks

SRC:

Sparse representation classification

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, 24060, USA

    Kristine D. Cantin-Garside, Zhenyu Kong, Sunwook Kim & Maury A. Nussbaum

  2. Department of Psychology, The University of Alabama, Tuscaloosa, AB, USA

    Susan W. White

  3. Department of Psychology, Virginia Tech, Blacksburg, VA, 24060, USA

    Susan W. White & Ligia Antezana

  4. Department of Industrial and System Engineering, Virginia Tech, 250 Durham Hall (0118), Blacksburg, VA, 24061, USA

    Maury A. Nussbaum

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  1. Kristine D. Cantin-Garside
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  4. Ligia Antezana
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Contributions

Dr. KDC-G drove the study design, ran study sessions and led sensor data collection, completed the analyses, informed the interpretation of the results, drafted the manuscript and its revisions, and approved the final manuscript; Dr. ZK informed the study design and selection of analysis methods for sensor analysis, oversaw the model interpretation, and reviewed, revised and approved the final manuscript; Dr. SWW provided guidance on study design for children with ASD, supervision over study sessions and coding SIB, and input for clinical implications of work, and reviewed, revised and approved the final manuscript; LA led ASD evaluations and human data collection, informed SIB coding and interpretation, and reviewed, revised and approved the final manuscript; Dr. SK provided input during the initial study design, sensor selection, setup and usage, and reviewed, revised and approved the final manuscript; Dr. MAN guided the study design, supervised data collection and analysis, informed the interpretation of results, and provided extensive input and guidance for the manuscript draft and its revisions, and approved the final manuscript.

Corresponding author

Correspondence to Maury A. Nussbaum.

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Conflict of interest

All authors declare that they have no conflict of interest and are independent academic researchers with no association with the developers of any technology used in this study. No funding was received for this research.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board (Protocol 17-650) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

All adult participants provided informed consent, and qualifying children provided assent (> 7 years of age and of developmental level), before any data collection. The Virginia Tech Institutional Review Board approved all experimental procedures (#17-650).

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Appendix

Appendix

To balance these data, N data points (observations collected at 60 Hz) from all worn sensors were randomly selected from each label (Bastani et al. 2016b; Großekathöfer et al. 2017; Albinali et al. 2009; Bastani et al. 2016a, Rad, Furlanello, and Kessler 2016). In preliminary work, we examined the accuracy and efficiency of validation results from training with 10 different N values, ranging from 100 to 1000 per label in equal increments (Bastani et al. 2016a; Großekathöfer et al. 2017). From these initial analyses, number of training data points was selected as N = 500 for the first label scheme (0,1) and 400 for the second label scheme (0–23). These training sizes are comparable to previous work (Coronato et al. 2014; Bastani et al. 2016a), and preliminary analyses indicated that additional observations improved classification performance only marginally with larger N values. As discussed in Bastani et al. (2016a), training points were assumed to reflect the entire dataset, since they were randomly selected from across the duration of a given session. The selected numbers of data points for each labeling scheme were considered as representative, yet efficient, training sizes.

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Cantin-Garside, K.D., Kong, Z., White, S.W. et al. Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques. J Autism Dev Disord 50, 4039–4052 (2020). https://doi.org/10.1007/s10803-020-04463-x

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