Tablet-based gameplay identifies movement patterns related to autism spectrum disorder

Anna Anzulewicz, Krzysztof Sobota, Jonathan Delafield-Butt

Research output: Contribution to conferencePoster

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Abstract

Background: It has been proposed that one of the early markers of autism spectrum disorder (ASD) are abnormalities in the development of intentional movements, which can be observed from early childhood. New evidence suggests that disruption of motor timing and integration may underpin the disorder, providing a new potential marker for its identification.

Objectives: In this study, we used widely available tablet devices (iPads) to identify differences in kinematics between children diagnosed with ASD and their typically developing (TD) peers. We also compared movement patterns of children diagnosed with neurodevelopmental disorders other than autism (OND) with movement patterns exhibited by ASD and TD children. We utilised tablet devices’ inertial sensors (accelerometer, gyroscope, and touchscreen to record the movements children make while playing two educational games on a tablet.

Methods: Ninety-six children (aged 3-6) diagnosed with ASD, 37 diagnosed with OND, and 387 TD children took part in the study. The children were asked to play two educational games on a tablet. Each game consisted of two parts: two-minute long training and five-minute long test session. During the gameplay, we collected data from tablet’s sensors and screen. After the experimental session, 262 variables obtained by simple calculation of the raw sensor data (e.g. acceleration of the movements) were extracted and analysed using machine learning algorithms. To increase generalisation properties of the models, we reduced dimensionality to 49 most significant variables.

Results: To compare movement patterns of children with ASD, OND, and TD children, we used machine learning algorithms. Each algorithm differentiated individuals within the ASD group from the other groups using 49 variables derived from the touch screen and inertial sensors.
ASD - TD comparison: The algorithms classified children diagnosed with ASD from TD children with up to 93% accuracy.
OND - TD comparison: The algorithms classified children diagnosed with OND from TD children with up to 95% accuracy. The results suggest that movement patterns of typically developing children are different than patterns exhibited by children diagnosed with neurodevelopmental disorders other than autism.
ASD - OND comparison
The algorithms classified children diagnosed with ASD from OND children with up to 93% accuracy. This result suggests that ASD is characterised by movement patterns that can be differentiated from patterns related to other neurodevelopment disorders.

Conclusions: These findings support the view that children with ASD can be differentiated
from TD children by movement patterns analysis. We also provide evidence suggesting that patterns characteristic of ASD children are different from patterns exhibited by children with OND. However, the latter result is not particularly strong due to the small sample of OND group. Further research is needed to provide better evidence.
Original languageEnglish
Publication statusPublished - 27 Mar 2018
EventCognitive Neuroscience Society 25th Annual Meeting - Sheraton Hotel, Boston, United States
Duration: 24 Mar 201827 Mar 2018
https://www.cogneurosociety.org/cns-2018-program/

Conference

ConferenceCognitive Neuroscience Society 25th Annual Meeting
CountryUnited States
CityBoston
Period24/03/1827/03/18
Internet address

Fingerprint

Tablets
Autism Spectrum Disorder
Autistic Disorder
Equipment and Supplies
Biomechanical Phenomena

Keywords

  • autism
  • smart technology
  • digital health
  • movement analysis

Cite this

Anzulewicz, A., Sobota, K., & Delafield-Butt, J. (2018). Tablet-based gameplay identifies movement patterns related to autism spectrum disorder. Poster session presented at Cognitive Neuroscience Society 25th Annual Meeting, Boston, United States.
Anzulewicz, Anna ; Sobota, Krzysztof ; Delafield-Butt, Jonathan. / Tablet-based gameplay identifies movement patterns related to autism spectrum disorder. Poster session presented at Cognitive Neuroscience Society 25th Annual Meeting, Boston, United States.
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abstract = "Background: It has been proposed that one of the early markers of autism spectrum disorder (ASD) are abnormalities in the development of intentional movements, which can be observed from early childhood. New evidence suggests that disruption of motor timing and integration may underpin the disorder, providing a new potential marker for its identification.Objectives: In this study, we used widely available tablet devices (iPads) to identify differences in kinematics between children diagnosed with ASD and their typically developing (TD) peers. We also compared movement patterns of children diagnosed with neurodevelopmental disorders other than autism (OND) with movement patterns exhibited by ASD and TD children. We utilised tablet devices’ inertial sensors (accelerometer, gyroscope, and touchscreen to record the movements children make while playing two educational games on a tablet.Methods: Ninety-six children (aged 3-6) diagnosed with ASD, 37 diagnosed with OND, and 387 TD children took part in the study. The children were asked to play two educational games on a tablet. Each game consisted of two parts: two-minute long training and five-minute long test session. During the gameplay, we collected data from tablet’s sensors and screen. After the experimental session, 262 variables obtained by simple calculation of the raw sensor data (e.g. acceleration of the movements) were extracted and analysed using machine learning algorithms. To increase generalisation properties of the models, we reduced dimensionality to 49 most significant variables.Results: To compare movement patterns of children with ASD, OND, and TD children, we used machine learning algorithms. Each algorithm differentiated individuals within the ASD group from the other groups using 49 variables derived from the touch screen and inertial sensors. ASD - TD comparison: The algorithms classified children diagnosed with ASD from TD children with up to 93{\%} accuracy. OND - TD comparison: The algorithms classified children diagnosed with OND from TD children with up to 95{\%} accuracy. The results suggest that movement patterns of typically developing children are different than patterns exhibited by children diagnosed with neurodevelopmental disorders other than autism.ASD - OND comparisonThe algorithms classified children diagnosed with ASD from OND children with up to 93{\%} accuracy. This result suggests that ASD is characterised by movement patterns that can be differentiated from patterns related to other neurodevelopment disorders.Conclusions: These findings support the view that children with ASD can be differentiatedfrom TD children by movement patterns analysis. We also provide evidence suggesting that patterns characteristic of ASD children are different from patterns exhibited by children with OND. However, the latter result is not particularly strong due to the small sample of OND group. Further research is needed to provide better evidence.",
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Anzulewicz, A, Sobota, K & Delafield-Butt, J 2018, 'Tablet-based gameplay identifies movement patterns related to autism spectrum disorder' Cognitive Neuroscience Society 25th Annual Meeting, Boston, United States, 24/03/18 - 27/03/18, .

Tablet-based gameplay identifies movement patterns related to autism spectrum disorder. / Anzulewicz, Anna; Sobota, Krzysztof; Delafield-Butt, Jonathan.

2018. Poster session presented at Cognitive Neuroscience Society 25th Annual Meeting, Boston, United States.

Research output: Contribution to conferencePoster

TY - CONF

T1 - Tablet-based gameplay identifies movement patterns related to autism spectrum disorder

AU - Anzulewicz, Anna

AU - Sobota, Krzysztof

AU - Delafield-Butt, Jonathan

PY - 2018/3/27

Y1 - 2018/3/27

N2 - Background: It has been proposed that one of the early markers of autism spectrum disorder (ASD) are abnormalities in the development of intentional movements, which can be observed from early childhood. New evidence suggests that disruption of motor timing and integration may underpin the disorder, providing a new potential marker for its identification.Objectives: In this study, we used widely available tablet devices (iPads) to identify differences in kinematics between children diagnosed with ASD and their typically developing (TD) peers. We also compared movement patterns of children diagnosed with neurodevelopmental disorders other than autism (OND) with movement patterns exhibited by ASD and TD children. We utilised tablet devices’ inertial sensors (accelerometer, gyroscope, and touchscreen to record the movements children make while playing two educational games on a tablet.Methods: Ninety-six children (aged 3-6) diagnosed with ASD, 37 diagnosed with OND, and 387 TD children took part in the study. The children were asked to play two educational games on a tablet. Each game consisted of two parts: two-minute long training and five-minute long test session. During the gameplay, we collected data from tablet’s sensors and screen. After the experimental session, 262 variables obtained by simple calculation of the raw sensor data (e.g. acceleration of the movements) were extracted and analysed using machine learning algorithms. To increase generalisation properties of the models, we reduced dimensionality to 49 most significant variables.Results: To compare movement patterns of children with ASD, OND, and TD children, we used machine learning algorithms. Each algorithm differentiated individuals within the ASD group from the other groups using 49 variables derived from the touch screen and inertial sensors. ASD - TD comparison: The algorithms classified children diagnosed with ASD from TD children with up to 93% accuracy. OND - TD comparison: The algorithms classified children diagnosed with OND from TD children with up to 95% accuracy. The results suggest that movement patterns of typically developing children are different than patterns exhibited by children diagnosed with neurodevelopmental disorders other than autism.ASD - OND comparisonThe algorithms classified children diagnosed with ASD from OND children with up to 93% accuracy. This result suggests that ASD is characterised by movement patterns that can be differentiated from patterns related to other neurodevelopment disorders.Conclusions: These findings support the view that children with ASD can be differentiatedfrom TD children by movement patterns analysis. We also provide evidence suggesting that patterns characteristic of ASD children are different from patterns exhibited by children with OND. However, the latter result is not particularly strong due to the small sample of OND group. Further research is needed to provide better evidence.

AB - Background: It has been proposed that one of the early markers of autism spectrum disorder (ASD) are abnormalities in the development of intentional movements, which can be observed from early childhood. New evidence suggests that disruption of motor timing and integration may underpin the disorder, providing a new potential marker for its identification.Objectives: In this study, we used widely available tablet devices (iPads) to identify differences in kinematics between children diagnosed with ASD and their typically developing (TD) peers. We also compared movement patterns of children diagnosed with neurodevelopmental disorders other than autism (OND) with movement patterns exhibited by ASD and TD children. We utilised tablet devices’ inertial sensors (accelerometer, gyroscope, and touchscreen to record the movements children make while playing two educational games on a tablet.Methods: Ninety-six children (aged 3-6) diagnosed with ASD, 37 diagnosed with OND, and 387 TD children took part in the study. The children were asked to play two educational games on a tablet. Each game consisted of two parts: two-minute long training and five-minute long test session. During the gameplay, we collected data from tablet’s sensors and screen. After the experimental session, 262 variables obtained by simple calculation of the raw sensor data (e.g. acceleration of the movements) were extracted and analysed using machine learning algorithms. To increase generalisation properties of the models, we reduced dimensionality to 49 most significant variables.Results: To compare movement patterns of children with ASD, OND, and TD children, we used machine learning algorithms. Each algorithm differentiated individuals within the ASD group from the other groups using 49 variables derived from the touch screen and inertial sensors. ASD - TD comparison: The algorithms classified children diagnosed with ASD from TD children with up to 93% accuracy. OND - TD comparison: The algorithms classified children diagnosed with OND from TD children with up to 95% accuracy. The results suggest that movement patterns of typically developing children are different than patterns exhibited by children diagnosed with neurodevelopmental disorders other than autism.ASD - OND comparisonThe algorithms classified children diagnosed with ASD from OND children with up to 93% accuracy. This result suggests that ASD is characterised by movement patterns that can be differentiated from patterns related to other neurodevelopment disorders.Conclusions: These findings support the view that children with ASD can be differentiatedfrom TD children by movement patterns analysis. We also provide evidence suggesting that patterns characteristic of ASD children are different from patterns exhibited by children with OND. However, the latter result is not particularly strong due to the small sample of OND group. Further research is needed to provide better evidence.

KW - autism

KW - smart technology

KW - digital health

KW - movement analysis

M3 - Poster

ER -

Anzulewicz A, Sobota K, Delafield-Butt J. Tablet-based gameplay identifies movement patterns related to autism spectrum disorder. 2018. Poster session presented at Cognitive Neuroscience Society 25th Annual Meeting, Boston, United States.