Sensing Technologies for Autism Spectrum Disorder Screening and Intervention
Abstract
:1. Introduction
2. Methodology
- What are the categories of sensing technologies that were intended for autism screening and intervention?
- From the perspective of clinical utility, why are these categories important?
- Were there experiments that showed the effectiveness of the sensors (in terms of accuracy, resolution, etc.) and their corresponding software applications?
- Are the sensors commercially-available or are still proof-of-concepts from research laboratories?
- What are the advantages and limitations of each sensor?
3. A Taxonomy of Sensors for ASD Screening and Intervention
3.1. Eye Trackers
3.1.1. Desktop-Based Eye Trackers
3.1.2. Head-Mounted Eye Trackers
3.1.3. Eye Tracking Glasses
3.2. Movement Trackers
3.3. Electrodermal Activity Monitors
3.4. Touch Sensing
3.5. Prosody and Speech Detection
3.6. Sleep Quality Assessment Detection
3.6.1. Polysomnography
3.6.2. Actigraphy
3.6.3. Video Monitoring
3.6.4. Ballistocardiography
4. Discussion and Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A. Summary of Sensing Technologies for ASD Screening and Intervention
Sensor Category | Purpose | Type | Measured Quantity | Benefits | Limitations |
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Eye trackers | To detect atypical eye gaze patterns for early screening | Desktop-based eye trackers | Timestamp, x and y coordinates of gaze fixations, distance from the display or stimulus |
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Head-mounted eye trackers | Timestamp, x and y coordinates of gaze fixations, distance from the display or stimulus |
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Eye tracking glasses | Timestamp, x and y coordinates of gaze fixations, distance from the display or stimulus |
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Movement trackers | To detect stereotypical movements for timely intervention | Wrist wear, worn on the chest, desktop | Acceleration, velocity or displacement in x, y, and z coordinates |
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Electrodermal activity monitors | To estimate the subject’s internal state through physiological data for timely intervention | Wrist wear | Electrodermal activity, blood volume pulse, heart rate, skin temperature |
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Tactile sensors | To simulate touch and hugs, and to induce controlled pain (for subjects with self-harming tendencies) | Worn on the wrist, chest, or leg | Contact pressure, then provides tactile feedback |
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To provide emotional feedback while playing games and to evaluate the accuracy of the subjects’ responses | Vibrotactile gamepad | Contact pressure, then provides vibrotactile feedback |
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Touch sensors on social robots | Contact pressure, then classifies the contact behavior to provide appropriate feedback |
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Vocal prosody and speech detectors | To detect atypical vocal patterns for early diagnosis | Voice recording and pattern recognition | Detects prohibition, approval, soothing, attentional bids and neutral utterances |
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Voice recording and LENA (Language ENvironment Analysis) device | Counts the number of words spoken by adults to and around the child, adult-child conversational interactions and child vocalizations |
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Sleep quality assessment devices | To get an early indication of ASD since poor sleep quality may serve as a possible indicator | Poly-somnography | Neurophysiological and cardiorespiratory parameters to determine eye-movements, muscle activity and oxygen |
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Actigraphy | Movement data through accelerometer readings |
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Video-monitoring devices | Video data |
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Ballisto-cardiography | Heart rate, respiratory rate, activity detection, bedwetting incidents |
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Cabibihan, J.-J.; Javed, H.; Aldosari, M.; Frazier, T.W.; Elbashir, H. Sensing Technologies for Autism Spectrum Disorder Screening and Intervention. Sensors 2017, 17, 46. https://doi.org/10.3390/s17010046
Cabibihan J-J, Javed H, Aldosari M, Frazier TW, Elbashir H. Sensing Technologies for Autism Spectrum Disorder Screening and Intervention. Sensors. 2017; 17(1):46. https://doi.org/10.3390/s17010046
Chicago/Turabian StyleCabibihan, John-John, Hifza Javed, Mohammed Aldosari, Thomas W. Frazier, and Haitham Elbashir. 2017. "Sensing Technologies for Autism Spectrum Disorder Screening and Intervention" Sensors 17, no. 1: 46. https://doi.org/10.3390/s17010046