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

Cantin-Garside, Kristine D.; Kong, Zhenyu; White, Susan W.; Antezana, Ligia; Kim, Sunwook

doi:10.1007/s10803-020-04463-x

Cited by 35 publications

(32 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MLR here had higher accuracy, specificity, precision, and recall compared to several commonly-used machine learning algorithms. These machine learning algorithms also detected SIB with high accuracy in our previous study using featureless data, though accuracy greatly decreased at the group-level (see Cantin-Garside et al for further details) 34 . Multi-level regression with both variable slopes and intercept may be preferred for group data with variable behaviors that could be specific to an individual, and it may also be more accessible to interpretation than other machine learning algorithms.…”

Section: Discussionmentioning

confidence: 67%

“…Multiple researchers annotated and discussed the video data before labeling raw accelerometry files (see Fig. 1 for an overview of the modeling process, and Cantin-Garside et al 34 for details on consensus-building for SIB labels) 34 . Raw sensor data were filtered using a 4th order, low-pass, recursive Butterworth filter, with a cutoff frequency of 20 Hz.…”

Section: Methodsmentioning

confidence: 99%

“…Data used here were obtained in Cantin-Garside et al 34 and are briefly summarized below, with additional information in Supplementary Material. Children with SIB and ASD were recruited through the university-affiliated psychology clinic and through the authors’ networks.…”

Section: Methodsmentioning

confidence: 99%

“…In our previous study, we examined featureless classification methods to detect the presence of SIB and classify the type of SIB among individuals and groups of individuals with ASD 34 . Using data from wearable accelerometers as input, accuracy was up to 99.1% for individual models, and up to 94.6% for models specific to SIB type.…”

mentioning

confidence: 99%

“…However, the detection models that incorporated all participant data had substantially poorer performance (accuracy = 48.8%), likely due to the inter-and intra-individual variability in SIB and activity levels. The current study is a secondary analysis of data described in Cantin-Garside et al 34 , and employed a multi-level modeling technique that includes motor variability features to address this limitation 34 .…”

mentioning

confidence: 99%

See 4 more Smart Citations

Multi-level modeling with nonlinear movement metrics to classify self-injurious behaviors in autism spectrum disorder

Cantin-Garside

Srinivasan

Ranganathan

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Self-injurious behavior (SIB) is among the most dangerous concerns in autism spectrum disorder (ASD), often requiring detailed and tedious management methods. Sensor-based behavioral monitoring could address the limitations of these methods, though the complex problem of classifying variable behavior should be addressed first. We aimed to address this need by developing a group-level model accounting for individual variability and potential nonlinear trends in SIB, as a secondary analysis of existing data. Ten participants with ASD and SIB engaged in free play while wearing accelerometers. Movement data were collected from > 200 episodes and 18 different types of SIB. Frequency domain and linear movement variability measures of acceleration signals were extracted to capture differences in behaviors, and metrics of nonlinear movement variability were used to quantify the complexity of SIB. The multi-level logistic regression model, comprising of 12 principal components, explained > 65% of the variance, and classified SIB with > 75% accuracy. Our findings imply that frequency-domain and movement variability metrics can effectively predict SIB. Our modeling approach yielded superior accuracy than commonly used classifiers (~ 75 vs. ~ 64% accuracy) and had superior performance compared to prior reports (~ 75 vs. ~ 69% accuracy) This work provides an approach to generating an accurate and interpretable group-level model for SIB identification, and further supports the feasibility of developing a real-time SIB monitoring system.

show abstract

Section: Discussionmentioning

confidence: 67%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Multi-level modeling with nonlinear movement metrics to classify self-injurious behaviors in autism spectrum disorder

Cantin-Garside

Srinivasan

Ranganathan

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

Advancing autism technology

deLeyer-Tiarks

Levine‐Schmitt

et al. 2022

Psychology in the Schools

View full text Add to dashboard Cite

The purpose of this review is to broadly identify technological advances in supports and evaluation for individuals on the autism spectrum. Literature review with commentary on the evolving technologies supporting and/or evaluating social‐emotional, behavioral, academic, and cognitive domains. There has been an increasing synthesis of technology within the education domain. A growing body of research has investigated the potentiality and efficacy of technological interventions, specifically in the area of support for individuals on the autism spectrum. This growth must also factor in sustainability and feasibility to be practical within the school setting. Continued research is also necessary to ensure the equity of technology supporting individuals on the autism spectrum.

show abstract

Effective Prediction of Autism Using Ensemble Method

Pujari,

Anusha

2022

Artificial Intelligence for Innovative Healthcare Informatics

View full text Add to dashboard Cite

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

Cited by 35 publications

References 54 publications

Multi-level modeling with nonlinear movement metrics to classify self-injurious behaviors in autism spectrum disorder

Multi-level modeling with nonlinear movement metrics to classify self-injurious behaviors in autism spectrum disorder

Advancing autism technology

Effective Prediction of Autism Using Ensemble Method

Contact Info

Product

Resources

About