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Filtering by: Creator Nurnberger Jr, John Remove constraint Creator: Nurnberger Jr, John Creator Jose, Jorge V Remove constraint Creator: Jose, Jorge V Keyword Motor deficits Remove constraint Keyword: Motor deficits

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  • Deep Learning and statistical millisecond motor assessments of neurodevelopmental disorders

    Creator:
    Doctor, Khoshrav, McKeever, Chaundy, Phadnis, Aditya, Wu, Di, Plawecki, Martin, Nurnberger Jr, John, and Jose, Jorge V
    Description:
    The data set consists of movement kinematic data from 92 participants that are neurotypical or have certain neurodevelopmental disorders (Autism, Attention Deficit/Hyperactivity Disorder, and their comorbidity). The data is of roughly 100 trials of a reaching paradigm per participant. The raw data consists of the linear acceleration (a), the roll (R), pitch (P), and yaw (Y), and the rate of change of the roll, pitch, and yaw. Description of the data and file structure. There are three primary folders: Data, Matlab Code, and the Deep Learning Code., Data: Each folder corresponds to one participant. Inside is their motion data (sensor_data.csv) and a file describing their diagnosis and severity (diagnosis.txt). The columns of the csv are: a in x, a in y, a in z, dR/dt, dP/dt, dY/dt, R, P, Y The diagnosis.txt file contains two lines: the first is their diagnosis (ASD, ADHD, A^2, NT), the second is their severity (HF for high functioning, MF for mid functioning, LF for low functioning, and NA for not applicable)., Matlab Code: Information on how to analyze participants can be found in README.txt file within the folder. The Matlab code was written and run on MATLAB_R2023b. The signal processing toolbox is required., and Deep Learning Code: Information on how to perform the deep learning analysis can be found in README.txt file within the folder. The deep learning code is written in Python3 and relies on PyTorch.
    Keyword:
    Neurodivergent, Autism, Neurodevelopmental Disorder, Attention-Deficit/Hyperactivity Disorder, Biometric, Deep Learning, Motor deficits, kinesthetics , and Statistics
    Citation to related publication:
    K. Doctor, C. McKeever, A. Phadnis, D. Wu, J. Nurnberger Jr., M. Plawecki, & J.V. Jose. Deep learning and statistical millisecond motor assessments of neurodevelopmental disorders. Science, In Submission.
    Title:
    Deep Learning and statistical millisecond motor assessments of neurodevelopmental disorders

  • Deep Learning and statistical millisecond motor assessments of neurodevelopmental disorders

    Creator:
    Doctor, Khoshrav, McKeever, Chaundy, Phadnis, Aditya, Wu, Di, Plawecki, Martin, Nurnberger Jr, John, and Jose, Jorge V
    Description:
    The data set consists of movement kinematic data from 92 participants that are neurotypical or have certain neurodevelopmental disorders (Autism, Attention Deficit/Hyperactivity Disorder, and their comorbidity). The data is of roughly 100 trials of a reaching paradigm per participant. The raw data consists of the linear acceleration (a), the roll (R), pitch (P), and yaw (Y), and the rate of change of the roll, pitch, and yaw. Description of the data and file structure. There are three primary folders: Data, Matlab Code, and the Deep Learning Code., Data: Each folder corresponds to one participant. Inside is their motion data (sensor_data.csv) and a file describing their diagnosis and severity (diagnosis.txt). The columns of the csv are: a in x, a in y, a in z, dR/dt, dP/dt, dY/dt, R, P, Y The diagnosis.txt file contains two lines: the first is their diagnosis (ASD, ADHD, A^2, NT), the second is their severity (HF for high functioning, MF for mid functioning, LF for low functioning, and NA for not applicable)., Matlab Code: Information on how to analyze participants can be found in README.txt file within the folder. The Matlab code was written and run on MATLAB_R2023b. The signal processing toolbox is required., and Deep Learning Code: Information on how to perform the deep learning analysis can be found in README.txt file within the folder. The deep learning code is written in Python3 and relies on PyTorch.
    Keyword:
    Neurodivergent, Autism, Neurodevelopmental Disorder, Attention-Deficit/Hyperactivity Disorder, Biometric, Deep Learning, Motor deficits, kinesthetics , and Statistics
    Citation to related publication:
    K. Doctor, C. McKeever, A. Phadnis, D. Wu, J. Nurnberger Jr., M. Plawecki, & J.V. Jose. Deep learning and statistical millisecond motor assessments of neurodevelopmental disorders. Science, In Submission.
    Title:
    Deep Learning and statistical millisecond motor assessments of neurodevelopmental disorders