Using Behavioral Patterns In Treating Autistic

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Information about Using Behavioral Patterns In Treating Autistic

Published on December 24, 2007

Author: sudarsun

Source: slideshare.net

Description

The development of behavioral therapy
regimens for autistic patients is relatively challenging as these
patients may not be able to express feedback to the applied
treatment. The response to a treatment course is mostly
estimated qualitatively and with little systematic feedback
between therapy and response. Collecting and analyzing data
about a patient’s daily activities could yield patterns linking
these activities, thereby providing therapists with some
foreknowledge of likely possible behavioral outcomes related
to their therapies. We propose a method for anomaly
detection system, which can monitor behavior patterns of the
patient based on the data collected on a daily basis. The
knowledge gathered about the patient could prove suggestive
of the patient’s feedback to the applied therapy. Upon mining
the behavioral patterns, the system could predict the response
of a patient to a stimulus, given a list of recently displayed
behaviors and/or completed activities. The knowledge thus
gathered could also be used to treat other patients of similar
disability.

Sudarsun. S, Member, IEEE Varun Kant Vashishtha Avijit Nayak

Brain development disorder Generally discovered during infancy or childhood Impairments in social interaction and communication Restricted and repetitive behavior Self-injury and aggression No specific cure available

Brain development disorder

Generally discovered during infancy or childhood

Impairments in social interaction and communication

Restricted and repetitive behavior

Self-injury and aggression

No specific cure available

Use of Power Point slides with video segments to teach socio-dramatic play. Train autistic people to point to pictures to aid communication. Simulations use to teach verbal communications or social robots for diagnosis and treatment.

Use of Power Point slides with video segments to

teach socio-dramatic play.

Train autistic people to point to pictures to aid

communication.

Simulations use to teach verbal communications or

social robots for diagnosis and treatment.

Substantive variations among cases Patients socially and intellectually not matured No immediate feedback available Low levels of program adherence

Substantive variations among cases

Patients socially and intellectually not matured

No immediate feedback available

Low levels of program adherence

To improve the life of autistic patients by helping the therapist understand more systematically & more immediately what is the effect of therapy. To predict on the fly when a patient will behave appropriately or not.

To improve the life of autistic patients by helping the

therapist understand more systematically & more

immediately what is the effect of therapy.

To predict on the fly when a patient will behave

appropriately or not.

Collection of patient’s observable behavior Classification of observed behaviors Using the sequence of observed behavior to determine the underlying mental state. If we have a sequence of behavioral pattern (say A=>B=>C=>D) what is the next most probable activity that patient will perform. This will help in preventing any unwanted situation beforehand.

Collection of patient’s observable behavior

Classification of observed behaviors

Using the sequence of observed behavior to

determine the underlying mental state.

If we have a sequence of behavioral pattern (say

A=>B=>C=>D) what is the next most probable

activity that patient will perform. This will help in

preventing any unwanted situation beforehand.

Problems we wish to address: 1. To estimate the effectiveness of the treatment from the observations on the patient. 2. Given a pattern of behavioral symptoms, how to predict the upcoming changes in the patient’s behavior. 3. How to detect anomalies in the patient’s behavior.

Problems we wish to address:

1. To estimate the effectiveness of the treatment from the observations on the patient.

2. Given a pattern of behavioral symptoms, how to predict the upcoming changes in the patient’s behavior.

3. How to detect anomalies in the patient’s behavior.

Estimating the effectiveness of the therapy: Three sequences: The medication event sequence. Patient’s feedback event sequence. Recorded patient’s observed behavior sequence. Medication event sequence is a controller input that is to be manipulated by patient’s feedback sequence. Patient’s feedback sequence it is not readily available (Hidden). But we have the recorded observations on the patient’s behavior which are dependent on the hidden patient feedback sequence.

Estimating the effectiveness of the therapy:

Three sequences:

The medication event sequence.

Patient’s feedback event sequence.

Recorded patient’s observed behavior sequence.

Medication event sequence is a controller input that is to be manipulated by patient’s feedback sequence. Patient’s feedback sequence it is not readily available (Hidden). But we have the recorded observations on the patient’s behavior which are dependent on the hidden patient feedback sequence.

Visualizing the problem as a set of hidden and observed events. Hidden Markov Models (HMM) can be a solution. E.g. deducing weather from a piece of seaweed. If it is “soggy” means wet weather If it is “dry” means sunny weather If it is “intermediate” – can’t say (probably sunny or rainy) Second useful clue can be the known condition of weather on previous day plus present (observed) seaweed state provide a better forecast.

Visualizing the problem as a set of hidden and observed events. Hidden Markov Models (HMM) can be a solution. E.g. deducing weather from a piece of seaweed.

If it is “soggy” means wet weather

If it is “dry” means sunny weather

If it is “intermediate” – can’t say (probably sunny or

rainy)

Second useful clue can be the known condition of

weather on previous day plus present (observed)

seaweed state provide a better forecast.

Three canonical problems associated with HMM: Given the parameters of the model, how can we compute the probability of a particular output sequence? Given the parameters of the model, how can we determine the most likely sequence of hidden states that could have generated a given output sequence? Given an output sequence or a set of such sequences, find the most likely set of state transition and output probabilities. Problem 2 is more related to our case. Viterbi algorithm can be used to solve this problem.

Three canonical problems associated with HMM:

Given the parameters of the model, how can we compute the probability of a particular output sequence?

Given the parameters of the model, how can we determine the most likely sequence of hidden states that could have generated a given output sequence?

Given an output sequence or a set of such sequences, find the most likely set of state transition and output probabilities.

Problem 2 is more related to our case. Viterbi algorithm

can be used to solve this problem.

Prediction of upcoming behavioral change: Any activity dependent on previous activity/activities. Use HMM to predict the next hidden mental state transition (e.g. Observed A=>B=>C what is the probability of next activity to be D, i.e. P(D/A,B,C)). First order HMM may not be appropriate (we have to allow the next state to be dependant on more than one previous state). Use of Conditional random field (CRF) as an alternative to n th order HMM.

Prediction of upcoming behavioral change:

Any activity dependent on previous activity/activities.

Use HMM to predict the next hidden mental state

transition (e.g. Observed A=>B=>C what is the probability of next activity to be D, i.e. P(D/A,B,C)).

First order HMM may not be appropriate (we have to allow the next state to be dependant on more than one previous state).

Use of Conditional random field (CRF) as an

alternative to n th order HMM.

Anomaly detection on behavioral patterns: Simplest way is to manually classify the set of normal and abnormal behaviors. Expert classify the behaviors into set of normal and abnormal behaviors. Fingerprinting is a viable technique for the same. Fingerprinting techniques involve two basic operations: Alignment of the sequence data, where the sequences are aligned to maximum overlap. Computation of similarity scores.

Anomaly detection on behavioral patterns:

Simplest way is to manually classify the set of

normal and abnormal behaviors.

Expert classify the behaviors into set of normal and

abnormal behaviors.

Fingerprinting is a viable technique for the same.

Fingerprinting techniques involve two basic operations:

Alignment of the sequence data, where the sequences

are aligned to maximum overlap.

Computation of similarity scores.

Data mining tools may not be self sufficient Effectiveness depends heavily on how well we collect behavioral data Can help in revealing patterns and relationships but not the significance of these patterns This is a theoretical paper so the accuracy can not be analyzed due to lack of experimental data Validation of these concepts with experimental data is a logical extension of this paper.

Data mining tools may not be self sufficient

Effectiveness depends heavily on how well we collect

behavioral data

Can help in revealing patterns and relationships but

not the significance of these patterns

This is a theoretical paper so the accuracy can not be

analyzed due to lack of experimental data

Validation of these concepts with experimental data

is a logical extension of this paper.

 

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