Knowledge Acquisition System based on JSON Schema

Implementation of a HCI for Actuation of Biosignals Acquisition Systems

Nuno Costa

, Tiago Araujo

1,2

, Neuza Nunes

and Hugo Gamboa

1,2

CEFITEC, Departamento de Física, FCT, Universidade Nova de Lisboa, Lisbon, Portugal

PLUX - Wireless Biosignals, S.A., Lisbon, Portugal

Keywords:

Knowledge Acquisition System, Human Computer Interaction, Ontology, Schema Language.

Abstract:

Large amounts of data, increasing every day, are stored and transferred through the internet. These data are

normally weakly structured making information disperse, uncorrelated, non-transparent and difﬁcult to access

and share. Semantic Web, proposed by the World Wide Web Consortium (W3C), addresses this problem by

promoting semantic structured data, like ontologies, enabling machines to perform more work involved in

ﬁnding, combining, and acting upon information on the web. Pursuing this vision, a Knowledge Acquisition

System was created, written in JavaScript using JavaScript Object Notation (JSON) as the data structure and

JSON Schema to deﬁne that structure, enabling new ways of acquiring and storing knowledge semantically

structured. A novel Human Computer Interaction framework was developed with this knowledge system,

enabling the end user to, practically, conﬁgure all kinds of electrical devices and control them. With the

data structured by a Schema, the software becomes robust, error – free and human readable. To show the

potential of this tool, the end user can conﬁgure an electrostimulator, surpassing the speciﬁc use of many

Electrophysiology software. Therefore, we provide a tool for clinical, sports and investigation where the user

has the liberty to produce their own protocols by sequentially compile electrical impulses.

1 INTRODUCTION

The easiness of access, storage, transmission of data

and the exponential proliferation of internet users en-

close new complexities: authentication; scalable con-

ﬁguration management; security; huge masses of high

dimensional and often weakly structured data (the

main problem addressed in this project). Structuring

data pursued by Semantic Web leaves many opportu-

nities open because there is always room for improv-

ing and to develop more adequate languages. Meth-

ods and approaches to solve the problem emerged

from research in Human-Computer Interaction (HCI)

(Prabhu, 1997), Information Retrieval (IR), Knowl-

edge Discovery in Databases and Data Mining (KDD)

(Fayyad et al., 1996). These methods assist end users

to identify, extract, visualize and understand useful in-

formation from data.

The establishment of ontologies is one of the

methods to solve the problem, possible with Semantic

Web, holding much promise in manipulating informa-

tion in ways that are useful and meaningful to the hu-

man user. Ontologies are collections of information

with speciﬁc taxonomies and inference rules to deﬁ-

ne relations between terms. A taxonomy deﬁnes

classes of objects and relations among them, and in-

ference rules provide advanced ways of relating in-

formation by deduction. Classes, subclasses and re-

lations among entities are a very powerful tool for

Web use. We can express a large number of relations

among entities by assigning properties to classes and

allowing subclasses to inherit such properties. The

structure and semantics provided by ontologies make

it easier for an entrepreneur to provide a service, mak-

ing its use completely transparent. Ontologies can en-

hance the functioning of the Web in many ways, like

relating information on a Web page to the associated

knowledge structures and inference rules, thus creat-

ing robust and clean applications (Berners-Lee et al.,

2001).

Mechanisms to specify ontologies have spring

in the late years, the Schema Language is one of

them. Given the fact that Xtensible Markup Lan-

guage (XML) allows users to add arbitrary structure

to their documents, but lacks in describing the struc-

tures, Schema was developed as a notation for deﬁn-

ing a set of XML trees (Thompson et al., 2000). From

this concept, a set of speciﬁcations were established

255

Costa N., Araujo T., Nunes N. and Gamboa H..

Knowledge Acquisition System based on JSON Schema - Implementation of a HCI for Actuation of Biosignals Acquisition Systems.

DOI: 10.5220/0004166702550262

In Proceedings of the International Conference on Signal Processing and Multimedia Applications and Wireless Information Networks and Systems

(WINSYS-2012), pages 255-262

ISBN: 978-989-8565-25-9

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

to create a Schema for JavaScript Object Notation

(JSON), a self descriptive language for data storage

and transmission, enabling the description of the data

structure. A useful Schema notation must have some

speciﬁc properties: identify most of the syntactic re-

quirements that the documents in the user domain fol-

low; allow efﬁcient parsing; be readable to the user;

concede limited transformations corresponding to the

insertion of defaults; be modular and extensible to

support evolving classes (Klarlund et al., 2000). This

language aids in the creation of structured data and

automated tools to present the data in a human read-

able form, making easier the extraction and visualiza-

tion of useful information from data. Therefore, in

this ﬁeld of structuring data, Schema largely super-

sedes Document type Deﬁnitions (DTDs) for markup

language.

One example of this procedure is Protégé, an

open source Ontology Editor and Knowledge Acqui-

sition System. Protégé is a framework written in Java

and uses Swing (Java GUI widget toolkit) to create

complex user interfaces. These interfaces provide

the user with tools to construct domain models and

knowledge-based applications with ontologies. As a

graphical tool for ontology editing and knowledge ac-

quisition, it can adapt to enable conceptual modelling

with new and evolving Semantic Web languages (Noy

et al., 2001). Protégé lets us, like the Schema, cre-

ate domains in a conceptual level without having to

know the syntax of the language ultimately used on

the Web to create interfaces, passing information, be-

tween other features. We can concentrate on the con-

cept types (integers, arrays, strings, ...) and relation-

ships in the domain and the facts about them that we

need to describe.

Using this mechanism in the project for structur-

ing the data, a Human Computer Interaction was cre-

ated with an innovative Knowledge Acquisition sys-

tem for controlling the actuation of a Biosignals Ac-

quisition System. One of the purposes, and as a prac-

tical example, is to control an electrostimulator en-

abling the user to create their own protocols, pursuing

a non-speciﬁc software for Electrostimulation. There-

fore, it allows the user to employ the software in dif-

ferent types of Electrostimulation applications:

• Electrotherapy: Rehabilitation (von Lewinski

et al., 2009); Spinal cord injury, stroke, sen-

sory deﬁcits, and neurological disorders (Co-

gan, 2008); elicit electronarcosis, electrosleep and

electroanalgesia (Lebedev et al., 2002);

• Physical Conditioning: ﬁtness; active recovery;

optimizing physical performance by improvement

of maximum strength of a muscle (muscular

tonus) in less time (Siff, 1990).

This work presents a novel Knowledge Acquisi-

tion System based on JSON Schema with the speciﬁc

focus on creating user interfaces to conﬁgure biosig-

nals acquisition devices, and with this information

control the actuation of that device. Ultimately, the

software will pursue usability and acceptability, be-

cause ease of use affects the users performance and

their satisfaction, while acceptably affects whether

the product is used or not (Holzinger and Leitner,

2005). Our proposal deﬁnes Application Program-

ming Interfaces (APIs) and low-level infrastructures

to create a system for controlling a biosignals acqui-

sition device, where the acquisition and actuation pa-

rameters are acquired from the user. At the core of

this system is a JSON Schema enabling validation

(data integrity), interaction (UI generation - forms and

code) and documentation.

2 DATA STRUCTURE

2.1 JSON

JSON is a simple, lightweight and human readable

text-data structure for information exchange. The

approach for information exchange is simpler than

XML, by the less verbose structure of the notation.

Interpreting JSON is native in some languages with

the existence of several support libraries that make

JSON a platform independent language (Crockford,

D., 2006). JSON structure is composed of name/value

pairs separated by comma, curly brackets holds ob-

jects and square brackets holds arrays. Values can be

numbers, strings, booleans, arrays, objects and null.

In the example below is an object containing infor-

mation of an address and phone number:

{"address":{

"streetAddress": "21 2nd Street",

"city":"New York"

"phoneNumber":

[{

"type":"home",

"number":"212 555-1234"

}]

}

Considering these features, JSON was selected as

the data structure of this work, and is deﬁned by JSON

Schema.

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256

{#}

Source

Basic

Advanced

User

Information

JSON

Schema

JavaScript

Ontology

Configuration Editors

Control the Actuation of a Device

(Raw Code)

(Specialized

GUI)

(Forms)

JSONs

Figure 1: Generic diagram showing the ﬂow of information.

2.2 JSON Schema

A JSON Schema is a Media Type (standard draft of

options) that speciﬁes a JSON-based format to de-

ﬁne the structure of JSON data, providing a con-

tract (set of rules) required in a given application, and

how to interact with the contract. Accordingly, JSON

Schema speciﬁes requirements for JSON properties

and other property attributes with the following inten-

tions:

• Validation (data integrity);

• Documentation;

• Interaction (UI generation - forms and code);

• Hyperlink Navigation.

JSON Schema is also a JSON with a compact im-

plementation and can be used on the client and server.

Speciﬁcations are organized in two parts (Zyp, K.,

2011):

• Core Schema Speciﬁcation: primary concerned

with describing a JSON structure and specifying

valid elements in the structure.

• Hyper Schema Speciﬁcation: deﬁne elements in

a structure that can be interpreted as hyperlinks,

in others JSON documents and elements of in-

teraction (This allows user agents to be able to

successfully navigate JSON documents based on

their Schemas).

Below is an example of a JSON Schema deﬁning

the structure for the JSON example showed before:

{"type":"object",

"required":false,

"properties":{

"address": {

"type":"object",

"required":true,

"properties":{

"city": {

"type":"string",

"required":true

"streetAddress": {

"type":"string",

"required":true

}

"phoneNumber": {

"type":"array",

"required":false,

"items":{

"type":"object",

"required":false,

"properties":{

"number": {

"type":"string",

"required":false

"type": {

"type":"string",

"required":false

}

}}}}

As shown in the diagram, Figure 1, JSON Schema

allows the deﬁnition of ontologies and will be in

the core of the program, enabling: interaction,

Schema serves as blueprint to architect the neces-

sary forms, with or without Graphical User Interfaces

(GUI), and deﬁne the other editors for the Knowl-

edge Acquisition System; documentation, APIs and

low-level software infrastructures in JavaScript en-

able the transformation of the information acquired

from the user in a JSON data structure deﬁned by

the Schemas, then the data (JSONs and Schemas)

is stored in the server and retrieved to the client

(JavaScript) through websockets (full-duplex com-

munications channels over a single TCP connection);

validation, JSON data can be imputed directly in a

raw editor (a knowledge acquisition system) by the

end user. Nevertheless, the data is only stored if the

structure agrees with the Schema.

With these mechanisms the software becomes a

powerful HCI system: robust; error - free; with hu-

man readable data structures, therefore, visualization

and extraction of information is easier.

KnowledgeAcquisitionSystembasedonJSONSchema-ImplementationofaHCIforActuationofBiosignalsAcquisition

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Output

Figure 2: Conﬁguration in Advanced Editor the mode options for an electrostimulator. Initial version of the software.

3 HUMAN COMPUTER

INTERACTION

Based on the method for data structure, described in

the last section, a Human Computer Interaction was

developed with a novel Knowledge Acquisition sys-

tem for controlling the actuation of Biosignals Ac-

quisition Systems. As shown in Figure 1, an ontol-

ogy is deﬁned with the help of JSON Schemas, then

these Schemas are interpreted in JavaScript enabling

the conception of three editors. These editors pro-

vide different means to acquire the information from

the user. Afterwards, this information is saved as

JSONs in the server. In the control, the JSONs are

retrieved from the server, through websockets, and

parsed in JavaScript. Finally, the necessary infor-

mation is sent to the device via APIs. As a practi-

cal example, the HCI provides mechanisms to control

an electrostimulator by enabling the user to conﬁgure

protocols/sessions for actuation of the device.

The HCI is integrated in a software for biosig-

nals acquisition and processing from PLUX - Wire-

less Biosignals, S.A., enabling the control of a generic

device. In this way, actuation, acquisition and pro-

cessing is possible in a closed-loop cycle (Broderick

et al., 2008).

The main purpose of this project is to provide the

end user the liberty to create and sequentially compile

Device Mode Session Control

Figure 3: Diagram representing the direction to conﬁgure

then control. First, conﬁgure a new device, then modes and

ﬁnally the sessions using the modes created.

electrical impulses. This enables the user to create ac-

tuation sessions for electrical devices. In a particular

example, enables the user to conﬁgure an electrostim-

ulator and design sessions to test different types of

Electrostimulation protocols.

The software is divided in conﬁguration (the

knowledge acquisition system based on JSON

Schemas), and control for the actuation of a device.

Basically, as shown in Figure 3, to control we need to

conﬁgure a device, and create temporal sessions, i.e

sequence of impulse modes, for each channel of that

device. Consequently, the conﬁguration is divided in

device, mode and session, each one with the respec-

tive JSON Schema to structure the data, and directly

or indirectly create the editors. In the next sections,

these ideas are explained in more detail accompanied

with images for the speciﬁc case of an electrostimula-

tor.

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Click

Save

Click

Figure 4: Conﬁguration of the mode in Basic Editor and Advanced Editor. Initial versions of the software.

3.1 Conﬁguration

3.1.1 Device

The user can compose a new device by setting the

mode options for that device. There are two types of

options, generic and special. Special stands for spe-

cial modes, in this case the special mode enables the

creation of pulses instead of only periodic waves, un-

like a generic wave generator. For example, the spe-

cial mode is necessary to conﬁgure an electrostimula-

tor. We can see in Figure 2 the conﬁguration with the

special parameters for Electrostimulation: maximum

current, 100 mA, and maximum time on, 500 µs. Af-

ter ﬁll in the form, created directly from the Schema,

a JSON is exported and stored in the server.

3.1.2 Mode

The user can project a new mode for a speciﬁc device.

The mode permits the conﬁguration of pulses (special

mode options activated), and waves (special mode op-

tions deactivated) depending on the mode options. In

Figure 4 is possible to view how a mode can be con-

ﬁgured in two different editors (described in the edi-

tors section). The ﬁgure shows the creation of bifasic

mode for the electrostimulator, a pulse with positive

and negative time on of 250 µs, amplitude up 100 mA

and amplitude down -100 mA, and 10 Hz of pulse fre-

quency.

3.1.3 Session

The user can create a new session for a speciﬁc

device. A session is a sequence of modes, and

each channel of a device can have one session pro-

grammed. A device has a maximum number of pro-

grammable modes, but they can be repeated inﬁnitely.

Figure 5 shows the conﬁguration of a session. Primar-

ily, the deﬁnition of the modes that will be used, four

is the maximum that the hardware can memorize. Af-

terwards, the creation of a session for the channels re-

quired, in this case, only one channel is programmed

with two modes, bifasic, 300 seconds activated and,

no_impulse, during 100 seconds. This sequence is re-

peated during half an hour by activating the loop for

KnowledgeAcquisitionSystembasedonJSONSchema-ImplementationofaHCIforActuationofBiosignalsAcquisition

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Output

Figure 5: Conﬁguration of a session in Advanced Editor. Loop during half an hour with bifasic mode, 300 seconds activated,

and no_impulse 100 seconds activated. Initial version of the software.

that speciﬁc session. The session is named protocol1.

3.1.4 Editors

The core for each conﬁguration stated before is a

Schema, and the end user has the possibility to choose

between three editors (like in Figure 1):

• Basic Editor (Specialized GUI): simple and user-

friendly interface with the minimum required

ﬁelds to ﬁll. The information is saved in a JSON

format deﬁned by the Schema. This editor is in-

tended for the basic users. It can be seen in Fig-

ure 4.

• Advanced Editor: a form generated automatically

from the Schema speciﬁcations, with the possibil-

ity of edition in GUI. Information is also saved

in JSON format deﬁned by the Schema. The most

proﬁtable editor because it shows directly how the

information will be saved in the JSON. This way,

users understand the architecture of the data. Fig-

ures 2, 4 and 5 have the conﬁguration of a de-

vice, a mode and a session, respectively, in the

Advanced Editor.

• Source Editor: an editor to upload or create

JSONs, where the user writes the JSON (like the

JSONs exported in ﬁgures 2 and 5) or copy and

paste the JSON in the editor. JSONs are valid and

can be saved if they agree with JSON Schema, if

not, reports with errors are generated. This Edi-

tor is intended for users with more knowledge of

the data structure, or for the ones that just want to

copy and paste information or upload.

Note that it’s possible to travel between the three

editors, and the information is inherited between

them. Also, in Advanced Editor, GUIs are directly

related with special types from the JSON Schema,

and can be used to ﬁll the necessary ﬁelds. Like

in the example Figure 4, clicking on the speciﬁc

button opens a graphic tool (canvas) for edition of

points by moving them with the mouse, identical

to the canvas from Basic Editor.

3.2 Control

The control is the area for actuation and acquisition

of biosignals. In here, the user ﬁrst needs to set up the

device, for that he must choose the device and the ses-

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Setup

Actuation

protocol_rehab

Figure 6: Actuation of an electrostimulator with the session protocol_rehab. This session has two channels with different

sessions conﬁgured.

sion to program. When the device is ready, start/stop

of the session is feasible. In the course of the session

is possible to acquire real time biosignals and syn-

chronize them with the session. This part is in devel-

opment stage: Figure 6 presents a moke-up of what

could be in the ﬁnal stage. The control area will have

many device sessions templates. Therefore, a quick

approach by the user will enable the simple selection

of a pre-deﬁned session and start the actuation imme-

diately.

4 CONCLUSIONS

This project envisions a software with powerful capa-

bilities. The most important parts are the infrastruc-

tures that conduct the ﬂow of information between the

Schema, the editors, the GUIs and ﬁnally the pro-

cess of saving in JSON data. After the APIs and

low-level infrastructures are programmed is easy and

fast to create information editors with other Schemas,

develop conﬁguration interfaces for that editors and

allow the engineering of new human computer in-

teractions. This explains the impressive usability of

this knowledge acquisition system. For this reason,

is important to refute that the main mechanisms be-

hind this software are JSON, the human-readable data

structure, and JSON Schema that deﬁnes the struc-

ture of JSON, allowing interaction (creation of forms

with specialized GUIs), documentation and valida-

tion, producing an error-free, semantically structured

and human-readable knowledge acquisition system,

contributing to the HCI system robustness.

In Biomedicine, the conﬁguration part should only

be manipulated by clinical professionals, where they

can adapt sessions to each patient. Overly compli-

cated user interfaces and large, bulky designs can de-

ter patients from using the device on a day to day ba-

sis, so, ultimately, the patient will only start or stop

a session prepared for him. Following this idea the

software was separated in conﬁguration, where clini-

cal professionals can conﬁgure a device with sessions

for each channel, and control, where the user just need

to choose the device and session (set up the device)

then start the session. If for some reason, the session

should not stop automatically (before the stop time is

reached), the user can stop the session. This HCI is

being tested in the Electrostimulation area to support

clinical professionals, sportsman and investigators to

control electrostimulators by enabling the creation of

different kind of protocols, empowering the software

usability.

This software, above all, pursues usability, for this

reason, in future stages of the project, the following

tasks will be conducted: user studies, extended unit

tests, usability tests, and usability expert evaluation.

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