Multimodal behavior signal analysis and interpretation for young kids with ASD

Presenter: Ming Li

SYSU-CMU Joint Institute of Engineering, Sun Yat-sen University, China

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Identifying Children with Autism Spectrum Disorder with

Machine Learning Framework

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Background

Machine Learning

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Machine Learning

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Background

• Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder.

• The rate of ASD has risen sharply in the past decade. Now 1/68[1] children in the United States has ASD.

• Early interference is so far the most effective method to treat ASD.

• Current ASD diagnose procedure is both time and labor consuming.

• Our main motivation is to analyze children behavior with machine learning, and design a prediction system that can conveniently perform early screening.

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[1] Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network. Morbidity and mortality weekly report.

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Key Symptoms of ASD

http://pages.samsung.com/ca/lookatme/English/

• Restricted and repetitive behaviors[1].

• Social interaction and communication difficulties.

• Avoid eye contact with another person.

• …

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Key Symptoms of ASD

[1] http://www.ninds.nih.gov/disorders/autism/detail_autism.htm#3082_2

• Restricted and repetitive behaviors[1].

• Social interaction and communication difficulties.

• Avoid eye contact with another person.

• Based on the above observation, we have a hypothesis that ASD children may scan faces differently when looking at them.

• Our work focus on finding such abnormality in the face scanning patterns to predict ASD.

• Can we capture the eye movement information? Yes!

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Key Symptoms of ASD

[1] http://www.ninds.nih.gov/disorders/autism/detail_autism.htm#3082_2

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Eye Tracking Device

http://www.tobiipro.com/product-listing/tobii-pro-tx300/

Input: An image on the screen.

Output: A series of eye gaze coordinates indicating where the person is looking at on the showed image.

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Related Work: Do individuals with and without autism spectrum disorder scan faces differently?

Sequence of eye gaze coordinates



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The Face Scanning Pattern Dataset

Li Yi et al., “Do individuals with and without autism spectrum disorder scan faces differently? A new multi-method look at an existing controversy,” Autism Research, 2014.

Eye tracking

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Subject Composition


Subject composition:

• 29 4- to 11-year-old Chinese children with ASD (ASD)

• 29 Chinese typically-developed children matched with the chronological age (TD-Age)

• 29 Chinese typically-developed children matched with IQ (TD-IQ)

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Analysis with Area of Interest (AOI)


Manually partition each face image into different semantic regions.

Count the number of gazes falling into each individual area.

Significant differences between ASD and non-ASD groups are found, especially on right eye and nose.

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Proposed ASD Prediction Framework

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Dataset Description




Eye tracking

Dataset (Sequence of 2d x-y coordinates)

Coordinate Level

ImageLevel

SubjectLevel

87 participants

87*55 = 4785 instances

87*55*180 ≈ 9*10^5 instances

Set of image-wise features (4785 instances)

Trained Prediction

Model

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Dataset Description

ImageLevel

SubjectLevel

87 participants

87*55 = 4785 instances

Feature Representation

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Summary: From Gaze Coordinate to Image-wise Feature

• One feature vector is extracted from the gaze coordinates(180 points) belonging to one viewed image.

• For each subject in the dataset, the number of extracted features is equal to the number of viewed images(55).

• Each feature is associated with a binary label (ASD/non-ASD), decided by the group label of its corresponding subject.

• ASD Prediction model is trained on these labeled features.

Obtain dictionary words with k-means:

• Algorithm: Perform k-means on eye-gaze coordinates.

• Assume N subjects (participants) are assigned for training.

• Input: The complete set of eye gaze coordinates from the N subjects.

• Output: K cluster centroids and their generated cells.

• Each cell is treated as a dictionary word.

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BoW Feature Representation

Illustration of the cells projected onto one example image. Note that such cell partition is the same for all viewed images. From left to right: K = 16, 32, 48, 96.

K-Means vs AOI

• The Area of Interest (AOI) approach needs manual segmentation for every face image.

• K-means is data driven, can automatically group the data based on coordinate locations and densities.

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Dictionary words with k-meansDictionary words with AOI

Traditional hard BoW representation:

• Consider hard membership to dictionary words by assigning each data to the closest one.

Proposed soft BoW representation:

• Consider soft membership to dictionary words.

• Soft membership to each cluster:

• Soft BoW histogram:

xn: The n-th eye gaze coordinate on the image.dk: The centroid of the k-th cluster (dictionary word).K: The number of dictionary words.

N: The number of gaze coordinates on one face image.

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Improved BoW with Soft Membership

Support Vector Machine (SVM) is a linear classifier which learns a decision boundary that maximizes the separation margin.

Data and label

Decision Boundary:

Margin Size:

Objective:

In our problem: The input to SVM are the set of labeled image-level BoW features.

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Image-Level Prediction with SVM

M

Kernel SVM

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• The distribution of extracted feature is not linearly separable.

• We can apply kernel method to map the features into a high dimensional kernel space where they become more linearly separable.

• Our work adopts the radial basis function (RBF) kernel SVM for its good performance.

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Subject level Score ensemble

: Viewed image number of a subject

Subject-Level Prediction:

Image-level Score

Subject-level Score

Framework

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Framework

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Improved Dictionary learning

Improved dictionary learning: Motivation

Different from learn the dictionary words with both classes, we need to treat them differently.

The heat map of ASD Group( Left) and TD Group (Right)

Improved dictionary learning

• Supervised Discriminate Mean-shift• The estimator of density difference computed at point x:

• where cd,1and cd,2 are normalization constants for positive and negative classes. yi and yj equal to 1 when class label is positive, equal to -1 when label is negative.

• We can find the mode of this density by finding the location where gradient equal to zero.

• The mean shift result with different iterations.

Improved dictionary learning: Motivation

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Experiment

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Evaluation Method

Leave-One-Out Cross Validation

Consider the dataset contains K subjects (participants) whose eye gaze data are recorded:

For k = 1 to K

Testing data = Eye gaze data from subject k.

Training data = Eye gaze data from the rest of the subjects.

Train the prediction model with the training data.

Predict subject_score(k) with the testing data.

End

Benchmarks to quantitatively evaluate the prediction performance:

• Receiver operating characteristic ( ROC Curve)

• Area Under the ROC Curve (AUC)

• Accuracy

Parameter Searching

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An example of searching parameters with coarse (left) and fine (right) grid-searching in RBF kernel SVM

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log (gamma)

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Face partitioning: AOI, K-means and Supervised Meanshift

AOI K-means clustering Supervised Meanshift(baseline) (Our proposed method I) (Our proposed method II)

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Baseline 2: BoW with AOI Dictionary

Baseline 2: BoW with AOI Dictionary

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Dictionary Learning Method

AUC(%) Accuracy(%)

AOI 91.20 83.91

K-means 89.63 88.51

Supervised Meanshift 94.53 89.66

AUC and Accuracy with different dictionary learning method

Wenbo Liu, "Identifying Children with Autism Spectrum Disorder Based on Their Face Processing Abnormality: A Machine Learning framework", Autism research, 2016.

Result: Evaluation Summary

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AUC(%) Accuracy(%)

ASD vs. Non-ASD 94.53 89.66

ASD vs. TD-Age 87.51 82.76

ASD vs. TD-IQ 90.25 86.21

AUC and Accuracy with different negative groups

AUC and Accuracy of different Races

Wenbo Liu, "Identifying Children with Autism Spectrum Disorder Based on Their Face Processing Abnormality: A Machine Learning framework", Autism research, 2016.

AUC(%) Accuracy(%)

All Face Images 94.53 89.66

Same-Race Faces 86.92 85.06

Other-Race Faces 96.91 90.80

Ongoing work• We hope to design a more robust system by looking into

other sources of multimodality information, such as emotion, gaze, activity and speech.

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Data Example

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Data CollectionDataset:

• Recording the diagnostic procedures of Autism Diagnostic Observation Schedule (ADOS) exams.

• 230 children are enrolled in the exam.

• M1: 115 participants



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Data collection

Proven technique in our Lab (I)

• Emotion Recognition

Input Images Output Score

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Surprised Sad Disgust Worried Anxious Neutral Happy Angry

5.84e-06 1.14e-04 1.26e-04 1.51e-07 2.27e-03 3.52e-03 4.89e-02 9.44e-01

1.00e-02 2.37e-04 8.66e-02 3.96e-04 3.72e-01 5.05e-02 4.27e-02 4.36e-01

1.32e-08 1.42e-05 3.45e-03 1.59e-08 2.88e-04 4.17e-03 9.90e-01 1.46e-03

Proven technique in our Lab (III)

• Face Detection & Gaze Recognition

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• ADOS Data Speech Recognition Results

doctor: 不仅是什么啊哦好你在哪上学上幼儿园对呀之前的时候是在哪家幼儿园嗯警备区幼儿园是不是哇那你们那幼儿园听起来好像很厉害的样子可以介绍一下你们那个幼儿园吧就是说你那幼儿园是什么样子的嘛嗯嗯嗯嗯真的恩恩恩恩七月份嗯嗯嗯嗯嗯嗯你已经报了名的是吧嗯哼怎么说错了

child: 我不认识我上幼儿园毕业了广州警备区会接受是幼儿园怎么介绍我困扰我的幼儿园我一年之前有一个坡上坡上不能超过五公里我超过五公里就发完后有一个保安网网上他保安一般拿电报他什么拿拿了个手枪呀而且而且有一次有一次我是因为有有一次我上幼儿园

Wearable microphone to record child’s

interaction with patents and therapists

• Description of data

Our audio database is collected from children who havebeen diagnosed as autism and stayed in hospital for a onemonth rehabilitation treatment.

An audio recording wrist-band is worn by each child at thedaytime to record speech data in real environment.

We randomly select 6 audio segments during the child-therapist interactions with a total length of 120 minutes asour diarization evaluation data. They are selected from 3children, two boys (K1, K3) and one girl (K2).

Methods

• Voice activity detection

We simply use an energy-based VAD method due to its efficiency.

• Speaker segmentation

We employ the LSP feature and BIC metric to detect speaker changes.

• Speaker clustering

→ Introducing discriminative features (pitch, energy, phoneme

duration).

→ Revised distance measure.

→ Agglomerative hierarchical clustering (AHC) plus early stop. System overview..

Tianyan Zhou, Weicheng Cai, Huadi Zheng, Luting Wang, Xiaoyan Chen,Xiaobing Zou, Shilei Zhang, Ming Li, "A Pitch, Energy and Phoneme Duration Features based Speaker Diarization System for Autism Kids’ Real-Life Audio Data", ISCSLP 2016

Thank You!

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[email protected]@cmu.eduhttp://jie.sysu.edu.cn/~mli/

mailto:[email protected]

mailto:[email protected]

http://jie.sysu.edu.cn/~mli/

Multimodal behavior signal analysis and interpretation for young kids with ASD

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