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. 2021 Jan 7;21(2):370.
doi: 10.3390/s21020370.

A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC)

Zhaobo K Zheng  1 , John E Staubitz  2 , Amy S Weitlauf  2   3 , Johanna Staubitz  4 , Marney Pollack  4 , Lauren Shibley  2 , Michelle Hopton  2 , William Martin  2 , Amy Swanson  2 , Pablo Juárez  2   3   4 , Zachary E Warren  2   3   4 , Nilanjan Sarkar  1   5
Affiliations

A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC)

Zhaobo K Zheng et al. Sensors (Basel). .

Abstract

Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.

Keywords: ASD; affective computing; functional analysis; machine learning; multimodal data; problem behaviors; signal processing; wearable sensor.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study flow chart.
Figure 2
Figure 2
Platform architecture.
Figure 3
Figure 3
Kinect setup.
Figure 4
Figure 4
(a) Wearable Intelligent Non-invasive Gesture Sensor (WINGS) on a user; (b) Electronics hidden inside WINGS; and (c) WINGS electronic design.
Figure 5
Figure 5
(a) Forward Kinematics of WINGS; (b) WINGS Skeleton Visualization; and (c) User Gesture.
Figure 6
Figure 6
Screenshots of the Behavior Data Collection Integrator (BDCI) application.
Figure 7
Figure 7
Finite state machine (FSM) of BDCI.
Figure 8
Figure 8
(a) Experimental setup; and (b) Experimental procedure.
Figure 9
Figure 9
(a) Prediction Accuracies of Different Data Modality Models; (b) Interval Analysis; and (c) Feature Importance Comparison.
See this image and copyright information in PMC

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