ASD: ML perspective for Individual Performance Evaluation

D. López De Luise1, M. Fernandez Vuelta2, R. Azor3, M. Agüero4,C. Párraga5, N. López6, P. Bustamante7, M. Marquez8, R. Bielli9,

D. Hisgen10, R. Fairbain11, S. Planes12

Abstract – There are several approaches to interact with ASD patients. Many of them don't relay on vocal abilities because of the wide spectrum of symptoms. But taking data from ML perspective may introduce new sources of information for a better interpretation of patient's behaviour and helps build new parameters to model individual performance. This paper presents some initial findings in that direction based on the automatic evaluation of real cases as a way to build a parameterised description of patients. Preliminary analysis in patients suggest that the individual performance may be compared with the universe of patients, it is possible to elaborate a performance profiling and to model evolution profiles that are present in a predefined universe of patients. The team is currently defining and testing new audio and video protocols to collect additional parameters. From these findings, it is possible to build a parametric reasoning model using MLW.

1 Professor Director of CI2S Lab, Pringles 10 2nd FL, C1183ADB, Buenos Aires, Argentina, +54 11 49586991, mdldl@ci2s.com.ar2 Researcher at Centro CIEL, Calle Ecce Homo,2 Bajo Izqda, 33009, Oviedo, España, +34 934 765265, manuelafv79@hotmail.com3 Researcher at DICYTyV, Eng. Dep. – Universidad de Mendoza, Arístides Villanueva 773, M5502ITH,  Mendoza, Argentina, +54 261 4202017 (150), dicyt@um.edu.ar4 Researcher at CI2S Lab, Pringles 10 2nd FL, C1183ADB, Buenos Aires, Argentina, +54 11 49586991, maguero@ci2s.com.ar5 Researcher at DICYTyV, Eng. Dep. – Universidad de Mendoza, Arístides Villanueva 773, M5502ITH,  Mendoza, Argentina, +54 261 4202017 (150), dicyt@um.edu.ar6  Researcher at  Gab. de Tecnología Médica,  Eng. Dep.  ­  UNSJ, Libertador 1109 oeste, J5400ARL, San Juan, Argentina, +54 264 4211700 (313), nlopez@gateme.unsj.edu.ar7  Researcher at  Gab. de Tecnología Médica,  Eng. Dep.  ­  UNSJ, Libertador 1109 oeste, J5400ARL, San Juan, Argentina, +54 264 4211700 (313), pao.bustamante.011@gmail.com8 Researcher at EAndes foundation, Cnel. Suarez 725, M5602HHO, San Rafael, Mendoza, Argentina, +54 0260 4501412, kukenamar@yahoo.com.ar 9 Researcher at EAndes foundation, Cnel. Suarez 725, M5602HHO, San Rafael, Mendoza, Argentina, +54 0260 4501412, biellibondino80@hotmail.com 10 Researcher at CI2S Lab, Pringles 10 2nd FL, C1183ADB, Buenos Aires, Argentina, +54 11 49586991, dhisgen@ci2s.com.ar11  Researcher at Dep. de Ingeniería Traslacional (FICEyN),   Universidad Favaloro, Solís 453, C1078AAI, Buenos Aires, Argentina, +54 11 43781100, rominafair@hotmail.com12 Researcher at CI2S Lab, Pringles 10 2nd FL, C1183ADB, Buenos Aires, Argentina, +54 11 49586991, splanes@ci2s.com.ar 

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Keywords – Autism Spectrum Disorder; Asperger; Linguistics; Reasoning Model; Computational Linguistic; Morphosyntactic Linguistic Wavelets; Language Processing.

1 Introduction

The Autism Spectrum Disorder (ASD) can be defined as [7] wide continuum of associated cognitive and neurobehavioral disorders, including the core-defining features of impaired socialization, impaired verbal and nonverbal communication, and restricted and repetitive patterns of behavior (p. 470). It is characterized mainly by [18]: 

qualitative impairment in communication and language qualitative impairment in reciprocal social interaction patterns of behaviour, restricted (and stereotyped) interests and activities

Recent works denoted it as closely related to cerebral disorders in one or many brain areas, probably due to a genetic alteration [8] [22]. Regardless the cause, the typical alteration in verbal communication [23] is a real problem for the patient treatment and recovery. It usually affects also its socialization and cognitive devel-opment [24]. This is the reason there is very important to have tools to improve communication and better understanding of the inner thoughts and feelings of the person.

BIOTECH is a proposal to apply Morphosyntactic Linguistic Wavelets (MLW) as an intelligent system able to provide deep information of the reasoning process related to linguistics expressions. It has been applied to interpret WEB contents, automatic summarization, dialog profiling, texts and author profiling, bilingualism effects in young kids, etc. [14] [15]

In order to be able to process with MLW it is mandatory to have linguistics ex -pressions made by the patients. To overcome the lack of language articulations, the project relies in the concept of verbal behaviour [24], sensing verbal activities with specific audio and video devices and generating a meta-language that DAISY will process, compensating the poverty of vocal production1.

Verbal behaviour is becoming widely accepted as a crucial perspective for ASD therapies. Douglas Greer [4] express that there is growing evidence on the onto-genetic sources of language and its development that derives in a renewed interest in the theory that the verbal capability in humans is a result of evolution and onto-genetic development (p. 363).

According to Greer and Longano [11], vocal activities like naming appear to be a or the crucial stage in children’s verbal development. Authors like Lord et.

1  In verbal behaviour theory there is a discrimination between verbal and vocal activities. The  first refers to any expression of the individual (movements, sounds, gesturing, manners,  etc.)  while the other remains to language expressions (written or not)

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Al [25], [19], Greer [9] [10] and Ackerman [1], corroborated this and other close relations between vocal and verbal communication and cognition.

As a consequence, it is reasonable to desire a tool like BIOTECH to auto-matically evaluate verbal activities and provide help to complement those vo-cal activities that the patient can’t produce properly by itself.

Another important aspect to be considered is the particularity of every individu-al. Experts affirm [8] that skill training should be intensive but also customized in order to get better results. The design of MLW within BIOTECH is able to gather such specific differences since it can build a specific model that represents its be-haviour peculiarities.

This paper aims to present the relevance of an automatic processing based on Machine Learning (ML), the main concepts of MLW as an approach based on ML to model linguistic reasoning, and the way it can be applied to ASD patients.

The main contribution of this approach is the application of a novel linguistic processing (MLW) based on a combination of Computational Intelligence (that is, systems inspired in biological intelligence) and Machine Learning (automatic learning software).

The rest of this text is organized as follows: a global presentation of MLW and its applicability to ASD (section 2), some preliminary results of application ML to a simple and much reduced dataset with real data (section 3), conclusions and fu-ture work (section 4).

2 BIOTECH proposal

There are several approaches to treat ASD patients, ranging from diets to stimula-tion. Basically, they are based on a coaching process to develop certain skills in the patients. A new trend is based on technology and its application to help thera-pists. For instance, the Russel [20] and Nao robots [12] [21] provide mimic direc-tives to children. There are also interactive games that use skin temperature and movement sensors to monitor reactions of the patients to certain stimulations gen-erated from the screen [3]. Other systems monitor body symptoms of stress (Ro-bots Reading Autistic Kids' Minds [5]) like cardiac rhythm, skin temperature, and muscle movements and process them in real-time to infer the emotional status of the patients and define how to interact with him [6].

IEEE SIGHT is a Special Interest Group on Humanitarian Technology. In Ar-gentina, it sponsors BIOTECH project, which aims to develop a model for linguis-tic reasoning in individuals with ASD. In order to achieve this, it uses Mor-phosyntactic Linguistic Wavelets (MLW) designed by CIIS Lab with the project DAISY [14], and many specific devices for acoustic and visual interaction with the patient.

Currently, the project is in its early stage, building protocols for data collection, validating them with leading experts (Ciel Argentina, Ciel Spain, TIPNEA foun-dation, and Burgos University), designing suitable user interfaces, and defining the best hardware architecture.

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As part of this first stage, the team performed several preparatory steps de-scribed in the following subsections. The current paper summarizes also initial findings and sketches further work to be done in order to use them.

2.1 Evaluation of video records of sessions with patients

Four sessions with one of the patients were recorded. Let name him  R. Videos have 3.25, 59.3, 22.2, and 29.55 minutes. 

The first evaluation aims to assess the ML information acquirable via sound and video signals. To accomplish this, all the videos are studied from two points of view: sound and images, first isolated (to extract sound and image patterns) and afterwards combined (to relate the previously extracted patters). All this process-ing is manual to guarantee no bugs, but using very well known free Ubuntu® tools as R® and Pitivi®, and Audacity®

All the records have many clues that can be used as a basis for a systematic ex-traction of information relevant to build a complementary communication lan-guage. The following are some of the findings in record 3.

2.1.a Sound findings

Below is a description of the answers given by R upon certain stimulus:

R answers  Si  (Yes) to therapist sentence  se fue papá  (dad has gone) (minute 00:35). That is the only word clearly pronounced but very softly

R pronounces a kind of  yes with the sound (minute 2:54, 4:16, among other. Figure 1 shows a spectrogram (up) and autocorrelation (down) in frequency for yes.

Every motorbike or car's motor sound in the background is replied with an imi­tation sound. In these situations the background sound starts and  then the imi­tation (minutes 00:30, 00:40, 02:20, 02:42, 03:18, 03:50, 04:25, 06:45, 08:10, 09:49, 12:55, 14:30, 14:35). Figure 2 presents the spectrogram (up) and auto­correlation (down) in frequency for motors

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Fig. 1. Spectrogram (up) and Autocorrelation (down) in frequency for yes

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Fig. 2. Spectrogram (up) and Autocorrelation (down) in frequency for motors

A motorbike sound in the background is replied (record 3, minute 40) but this practice stops when R is commanded to stop doing it. But continued at minute 1:10   with   no   background   sound   (as   finishing   response).   Same   happens   in minute 8:40 delayed to minute 9:09

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Every success is celebrated by R with a specific sound pattern  (minutes 3:57, 4:15, 5:00, 5:12, 7:13, 7:45). When R is tired, he repeats the pattern but slowly (11:48, 14:46, 16:41) and once it is truncated (20:54)

It is important to note that most of the times this sound is produced when R is congratulated by his success in the exercise

Another   sound   replies   to   barking   in   the  background   (15.25,   16.00,   among others).  Figure 3 is  the spectrogram (up) and autocorrelation (down) in fre­quency for celebration

Fig. 3. Spectrogram (up) and Autocorrelation (down) in frequency for motors

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The following is the answer to birds chirping (7:24, 12:48, 13:31, 13:48, 14:11, 14:23, among others). Figure 4 has the spectrogram (up) and autocorrelation (down) in frequency for bird chirping

Fig. 4. Spectrogram (up) and Autocorrelation (down) in frequency for bird chirping

In minute 9:50 when R was replying to birds, the therapist ask for silence and R gets annoyed and stops (minute 10:08) but after a while he continues the answer­ing (minute 10:12). That is, there is the ability to snooze a task within a context  and to resume it afterwards. 

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NOTE: It is important to note that audio signals shown in the figures are the re­sult of applying a Hamming Window2  function for segmentation, and processed with autocorrelation. The spectrogram of a signal is useful to show the amount of energy spread over the different frequencies.  The  period­tracking, is performed here by autocorrelation. The picture shows autocorrelation via frequency domain. 

2.1.b Visual findings

There is a physical language also, that is less expressive than the one described  previously. 

A tapping with  three sounds, performed with the right hand (minute 00:01, 00:04, 03:42, 06:32, 09:24, 10:31, and 16:25)

Eyes to the left  at  the head level,  when he puts a small  purple toy into his mouth while he presses it (perhaps to feel its vibration when the toy sounds). Minutes 0:55, 1:03, 5:47, and 8:21

Glance to backwards or lost  look, when she starts  listening (and sometimes when answering) birds3. In these moments he lost the visual contact with the ac­tivity even when he is performing an activity. Minutes 07:24, 8:27 (he starts answering here in 8:36), 12:49, 13:27, 14:20 (in this moment the therapist asks him more attention)

Glance to backwards or lost  look, when she starts  listening (and sometimes when answering) motors**. In these moments he loss the visual contact with the activity even when he is performing an action. Minutes 00:33, 00:48, 02:00, 02:43, 06:46, 08:10, 08:50, 09:15, 09:50 (here the therapist holds his head to make him watch the target and asks for silence), 10:48 (the therapist closes his mouth with her fingers but she does not succeeds in gather her sight. His eyes  keep focused ahead), 12:59 (the therapist holds his head to catch his attention), 13:04 (the therapist holds his left hand and speaks very close to his face trying to get attention), 13:46 (the therapist put her face very close to him to get his attention),    13:56, 14:36 (the therapist  challenges him), 15:12, 15:52, 18:46, 19:26, 20:46 (he is being asked for silence, but his face to the front, not the activity). In 21:03, he keeps listening a motor and doesn't answer to the exer­cise. The therapist takes this gesture as a failed answer.    

2.1.c General findings

At minute 17:28, R is very tired and it is expressed by his body and sound expres­sions. Answers to therapist keep mostly OK but he reduces slightly the answers to 

2 Windows are mathematical functions used to avoid discontinuities in both ends of a piece of  signal. This function results in a signal limited in the time. 3It is important to note that many times the exercise with the therapist coexists with the interac­tion with the background

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the environment. In fact, there is a moment when he keeps absolutely quiet and even lost connection to therapist.

After a very short while, he reconnects with the therapist and to answer to mo­tors. But afterwards he looks tired again (in fact he stretches himself in minute 19:00) and  protest   (minute  19:09)  making a  noise   that   is   repeated   in  minutes 19:15, and 20:37.

Generally  speaking,  the sounds appear to represent  two interactions that are simultaneous:

interaction with the therapist  interaction with the surrounding 

Responses to both interactions are mixed; therefore certain stimulation sounds haven't   a   response.   In   this   record  R seems  to  perform a kind  of  round robin polling of his environment and randomly decide which one to answer regardless the sound intensity and frequency.

2.2 Evaluation of data records of sessions with patients

Therapists in Ciel Argentina keep track of every session with certain forms that collect information like date and time of session, duration, answer timings, retri­als, etc. The team used a reduced dataset with the following information:

• Patient ID: a natural number to preserve biographical information of the patient

• Year/Month/Week• HOURS: total training hours for the declared week• UA2 and UA1: number of tasks and successful tasks • TPM1= UA1/HOURS• TPM2=UA2/HOURS• LANGUAGE TASKS: number of tasks related to language• OTHER TASKS: number of other tasks aside language

All this information is compiled in a database with 420 records from three chil­dren between 9 and 12 years old. All of them suffer ASD with level 3 of severity according to DSM­5 Diagnostic Criteria [2].

The dataset covers the training during several months (see Table 1).

Table 1 Therapy duration for each patient

Patient Starting  Ending

1 Jan 2010 July 2013

2 Nov 2011 Dec 2012

3 Nov 2010 Dec 2012

 

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The dataset  has been evaluated with mining approaches focused in Machine Learning, in order to find out its characteristics and to determine if it makes sense an automatic approach to extract features and model ASD behaviour. In section 3 there is a deeper analysis.

3.3 Evaluation of MLW reasoning model applicability to individu-als with ASD

Several experts in ASD agree that the disorder is originated by a biological alter­ation that makes the brain cells unable to select relevant from irrelevant informa­tion. Therefore a new memory, a new concept can't be built with the right logic and everything seems confusing. Following Scott [22], inter­neuron and pyramidal cells are the filters that avoid environmental distractions to be dismissed: When inhibitory inter­neurons are excited, pyramidal cells fire and relevant stimuli ac­tive hippo­campus and ignore irrelevant information. Both pyramidal and inter­neurons must be in a proper balance in order to allow the brain fine­tune which in­formation it stores in a new memory or not.

It is therefore reasonable to conclude that the reasoning in ASD patients is dis­torted, mainly linguistic and conceptualization processes. As a consequence, many other derived processes are also affected: concentration, linguistic learning, ontol­ogy inner representations, verbal behaviour among others. Wittgenstein said If we  spoke a different language, we would perceive a somewhat different world  and The limits of my language means the limits of my world [26].

 As will be described in section 3, there is a lot of information that is useful and it can be automatically extracted. It reflects many parts of the linguistic process and can be tracked using verbal behaviour.  From the Computational Linguistic perspective, it is possible to build a model that makes sense the verbal activities. It was previously applied to other less disruptive linguistic alterations such in early bilingualism [15], Internet usage [13], dialog conceptualization [16], writer profil­ing [17], etc.

It is worth noting that Morphosyntactic Linguistic Wavelets are not involved in procedures like tagging, translation, semantic/ontology dictionary building, etc. It is a type of leveraging information model, which makes a kind of map for linguis­tic processing. More details about MLW can be found in [14].

    2.4 Definition and implementation of interfaces for MLW

In order to be able to apply MLW, it is mandatory to design and implement a con­venient interface. This will gather data from specific collecting modules and the modeling device. Figure 5 depicts the overall BIOTECH architecture.

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Fig. 5. Global BIOTECH architecture   

As can be seen, from the architecture figure (Figure 5), there are three main blocks: external,  virtual and internal structure.  The two lasts belong to DAISY project and have many internals memories (marked as cylinders) and certain pro­cesses  (marked  as  routines symbols).  The external  structure collects  audio and video streams taken from many intermediate outputs. That data constitute useful information to build tools to evaluate the evolution and status of the patient, the treatment, and BIOTECH as well. Biotech is currently working on the Machine Learning (ML) module that   feeds with relevant   information the Inference  Ma­chine. The meta­language is a model which maps verbal behaviour of the current individual with Spanish structures and vocabulary. It is automatically inferred in the ML, deriving information with techniques similar to the ones described in the next section and some other out of the scope of this paper. 

3 Case study

As mentioned in section 2.1, data corresponding to three patients with ASD were analyzed. The rest of this section presents the analysis of the following findings:

It is possible to derive a performance curve for every individual There is also a global performance curve

This section presents the main statistics to show that TPM follows a Normal distribution for the global dataset. It is tested with T­ Student (see 3.1), due the number of cases. TPM Data for each individual follows the same distribution (see 

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3.2) with different parameters that feature every case. Kolmogorov test (3.3) veri­fies these findings. 

3.1 Performance for each individual

The specific performance for each patient does not follow exactly the global dis­tribution. It is better described by normal distributions using specific media and standard deviation. The following table describes the parameters for each patient and the value of p­statistic (see Table 2).

Table 2 Performance statistics for each patient

Patient Number of records Media  Standard deviation P­value (unilateral)

1 200 3.26 0.89 0.010

2 109 2.27 0.43 0.079

3 111 2.15 0.36 0.45

3.2 Global Behaviour of the Performance

Distribution tested with Student (Infostat , corresponds to Gaussian (see Table 3).

Table 3 Gaussian parameters for the entire dataset

Parameter Value

N (number of records) 420

Media 2.66

Standard Deviation 0.83

Confidence interval (95%):

­Lower limit

­Upper limit 

2.58

2.74

P statistic (bilateral) 0.991

The empirical distribution is inferred using Infostat , and the following figure shows the similarity with Normal distribution (black dots). Figure 6 shows the em­pirical distribution (gray) and Normal (2.66, 0.83) (in black).

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Fig. 6. Empirical distribution (gray) and Normal (2.66, 0.83) (in black)

The following figures compare the specific distribution for ID=1, 2 and 3 with the Normal distribution derived from the global dataset and the Normal distribu­tion for the specific individual. Figure 7, 8 and 9 is the global (up) and specific  (down) curves for ID=1, 2 and 3 respectively.    

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Fig. 7. Global (up) and specific (down) curves for ID=1

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Fig. 8. Global (up) and specific (down) curves for ID=2

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Fig. 9. Global (up) and specific (down) curves for ID=3

It is easy to observe that distributions on the left are less similar than the ones on the right, but all of them behave mainly as normal.

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3.3 Verification with Kolgomorov Test

Distribution validation for global dataset (Infostat ©) confirms the Gaussian distri­bution when the distribution has specific parameters representing each individual. That is, the distribution is a good representation just when media () and standard deviation () are calculated for each patient at a time (see Table 4). 

Table 4 Gaussian parameters for the every subset

Distribution N Value D statistic  p­value

global (,) 420 Normal(2.66, 0.83)

0.12 <0.0001*

ID1 (,) 200 Normal(3.16, 0.78)

0.07 0.3645

ID2 (,) 109 Normal(2.27, 0.18)

0.09 0.3617

ID3 (,) 111 Normal(2.15, 0.13)

0.08 0.4881

*Note: value with not signification

3.4 Variable reduction with PCA

PCA (Principal Component Analysis) is a very well known approach to reduce a set of variables to a minimum independent number of new variables.

TPM1, TMPM2, HOURS, UA1, UA2, LANGUAGE TASKS and OTHER TASKS are not independent. PCA can infer their relationship and reduces them to a minimum number by defining new variables that depends on the original ones.

Analysis with PCA results in only two variables (let say e1 and e2) that can be interpreted as the relation between the effort (e1) and the time to accomplish a task, which is the efficiency (e2). This interpretation is given by the specific com-bination of the original variables.

Using these new variables it is possible to model and evaluate the patient per-formance along the sessions and at a specific one.

When PCA is applied to global dataset (that is, merging data from all the indi-viduals) it gives a general model to asses each person respecting the group. By splitting the dataset according each ID it is shown that every patient has a different evolution profile that is clearly defined.

Table 5 gives specific information of the new variables for all the dataset, and each patient subset.

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Table 5 Eigenvectors for the reduced variables

Patient n Representation

Proportion 

Eigenvector:

(TPM1, TPM2, UA1, UA2,  HS, LAN_TASK, OTH_TASK)

All 420 0.41

0.31e1:(+0.43, +0.44, +0.55, +0.52, +0.52, +0.15, +0.16)

e2:(+0.51, +0.51, ­0.29, ­0.36, ­0.26, ­0.13, ­0.50)

ID1 200 0.41

0.35e1:(­0.09, ­0.09, +0.49, +0.52, +0.52, +0.25, +0.37)

e2:(+0.62, +0.62, +0.32, +0.26, ­0.22, ­0.07, ­0.14)

ID2 109 0.52

0.23e1:(+0.39, +0.37, +0.51, +0.51, +0.33, +0.10, +0.28)

e2:(­0.51, ­0.54, +0.15, +0.15, +0.59, +0.21, +0.13)

ID3 111 0.48

0.27e1:(+0.35, +0.38, +0.52, +0.52, +0.37, +0.11, +0.21)

e2:(+0.50, +0.49, ­0.11, ­0.15, ­0.49, +0.28, ­0.40)

Note that in every case there are only two new variables (that confirms that, thought each patient has distinctive parameters, the specific individual behaviour follows the general distribution). The new variables have homogeneous distribu­tion that aims to represent the main features of the population and individuals.

The plots of both new variables e1 and e2 (see Figure 10 and Figure 11) explain the features of each individual related to the entire dataset. There ID=1 is in black, ID=2 in white and ID=3 in gray.

    

Fig. 10. Plot e1 for ID=1 (black), ID=2 (white) and ID=3 (gray)

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Fig. 11. Plot e2 for ID=1 (black), ID=2 (white) and ID=3 (gray)

It is easy to observe that ID=1 performs over the media in e1 and below for e2. The individual 2 performs average for e1 and over the media for e2. The third pa­tient performs below the media for e1 and average for e2. It can be interpreted as in Table 6, a summary of e1 and e2 interpretation.

Table 6 Summary of e1 and e2 interpretation

ID e1 (effort) e2 (efficiency)

1 HIGH LOW

2 AVERAGE HIGH3 LOW AVERAGE

Note that all the qualifications are relative to the current dataset. More data will make it possible to build a broader criterion. 

Other analysis may give important information when evaluating the individual performance. That can be performed using the empirical distribution for e1 and e2, for each patient. Plotting the new data collected from additional sessions may al­low physicians to evaluate if the current patients performance has improved or not respecting his historical performance. Figures 12, 13 and 14 are the plots of the 

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empirical distribution for ID=1, ID=2 and ID=3. Curves e1 (black), e2 (white) may be used isolated or together.

    

Fig. 12. Empirical distribution for ID=1: e1 (black), e2 (white)

  

Fig. 13. Empirical distribution for ID=2: e1 (black), e2 (white)  

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Fig. 14. Empirical distribution for ID=3: e1 (black), e2 (white)

4 Conclusions and Future work

Although all the findings must be confirmed with further statistics, data prelimi­nary analysis suggest that patients have similar sessions and that training does not bias results. As a consequence, the current therapists and treatment do not make impact on the performance evolution. In other words, therapist and topic of the task make no significant bias from the statistical point of view.

The specific  TPM1 distribution for  ID=1, 2 and 3 compared with the Normal distribution (derived from the global dataset), defines the performance of an indi­vidual   related   to   the  universe  of  patients.  A  study with  more  cases  will   give enough information to build a more accurate performance profiling.

The variables  TPM1,  TPM2,  HOURS,  UA1,  UA2,  LANGUAGE  TASKS  and OTHER TASKS can be reduced to variables e1 and e2 using PCA.  They may be in­terpreted as the relation between the effort (e1) and the time to accomplish a task (e2). 

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The plots of e1 and e2 may model the key features of each individual related to the entire dataset  (here three individuals).  It is useful to automatically evaluate several patients using simple clustering algorithms in order to be able to profile them. The result should be a dot cloud with different features.

These new variables may constitute a patient's performance model along the sessions and a new parameter to evaluate each person respecting a group. It can also model evolution profiles that are present in a predefined universe of patients.

The team is currently defining and testing new audio and video protocols to collect additional parameters. This is important in order to be able to complete a parametric reasoning model using MLW.

From these findings, it may be concluded that it is possible to perform a gen­eral modeling of the skills performance and to build a specific profiling for each individual. 

It remains to future work to better parameterize and evaluate outputs of the BIOTECH processing and to relate them to the status and profile of the patient.

Acknowledgements

     Authors thanks IEEE SIGHT for the initiative and initial sponsorship of the CIIS Lab BIOTECH project, and Universities of Mendoza, San Juan and Favaloro for providing expertise in visual, acoustic signal processing and mathematical knowl-edge. Also thank to CIIS Lab and EAndes foundation, for expert knowledge in Machine Learning, Morphosyntactic Linguistic Wavelets and image processing.

Special thanks to CIEL in Spain and Argentina, and TIPNEA for the permanent collaboration with data, and expert knowledge in ASD.

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