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0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.5.4 Database 21 | Iida and N. Campbell at ATR Laboratories constructed a third emotional speech database in Japanese [i.120]. The aim of the project was emotional speech synthesis for disabled people. The database contains emotional speech in three emotion categories, such as joy, anger and sadness. The emotions are simulated but not ex... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.6 Dutch emotion speech databases | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.6.1 Preface | 5. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.6.1 Preface | Two databases focus on Dutch language. It is important to note that the Groningen database is only partially oriented to emotions. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.6.2 Database 22 | Groningen database. It is constructed at the Psychology School at Groningen University in The Netherlands and is distributed by ELRA [i.110]. It contains 20 hours of Dutch speech. It is important to note that the database is only partially oriented to emotion. An electroglottograph and an orthographic transcription are... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.6.3 Database 23 | S. Mozziconacci and his assistant collected an emotional speech database in order to study the relationship between speed in speaking and emotion [i.121] and [i.122]. The database contains emotional speech in seven emotion categories, such as joy, boredom, anger, sadness, fear, indignation and neutral (see clause 5.3 f... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.7 Spanish emotion speech databases | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.7.1 Preface | Two databases consist of Spanish language only (Databases 24 and 25). Additionally the Maribor database covering four languages contains Spanish language, too. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.7.2 Database 24 | Spanish Emotional Speech database (SES). J. M. Montero and his assistants constructed a Spanish emotional speech database in 1998 [i.123]. It contains emotional speech in four emotion categories, such as sadness, happiness, anger and neutral (see clause 5.3 for details). Fifteen subjects identified each emotion with a ... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.7.3 Database 25 | I. Iriondo [i.124] at the University of R. L. of Barcelona also recorded an emotional speech database. It contains emotional speech in seven emotion categories. Each of the eight actors reads two texts in three emotional intensities. The speech was rated-judged by 1 054 students during a perception test. From the 336 d... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.8 Danish emotion speech database | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.8.1 Preface | Four emotions and neutral (see clause 5.3 for details) speech samples are processed in one Danish speech emotion database. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.8.2 Database 26 | I. F. Engberg and A.V. Hansen at the Centre for Person Communication at Aalborg University recorded the Danish Emotional Speech database (DES) [i.125]. T. Brondsted wrote the phonetical transcription. The construction of DES was a part of Voice Attitudes and Emotions in Speech Synthesis (VAESS) project. The database co... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.9 Hebrew emotion speech database | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.9.1 Preface | Three medical parameters are taken into account in one Hebrew database. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.9.2 Database 27 | At the faculty of Holon Academic Institute of Technology at Israel, N. Amir et al. recorded emotional a multi-modal speech database [i.126]. It contains emotional speech in 5 emotion categories. The database consists of emotional speech, electromyogram of the curragator (a muscle of the upper face which assists in expr... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.10 Swedish emotion speech database | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.10.1 Preface | Speakers from four different native speaking languages contributed for the Swedish database. They are Swedish, Spanish, Finnish and English speakers. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.10.2 Database 28 | A. Abelin and his assistants recorded this emotional speech database [i.127]. It contains emotional speech in nine emotion categories, such as joy, surprise, sadness, fear, shyness, anger, dominance, disgust and neutral (see clause 5.3 for details). Different nationality listeners classified the emotional utterances to... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.11 Chinese emotion speech database | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.11.1 Preface | Three emotions and neutral speech segments were created by three Chinese students and by avatars. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.11.2 Database 29 | F. Yu et al. at the Microsoft Research China recorded this emotional speech database [i.128]. It contains speech segments from Chinese teleplays in four emotion categories, such as anger, happiness, sadness and neutral (see clause 5.3 for details). Four persons tagged the 2 000 utterances. Each person tagged all the ut... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.12 Russian emotion speech database | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.12.1 Preface | A big database was collected at the Meikai University in Japan by native Russian students for Russian language. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.12.2 Database 30 | RUSSian LANguage Affective speech (RUSSLANA). V. Makarova and V.A. Petrushin collected this emotional speech database at the Meikai University in Japan [i.129]. In total, 3 660 sentences uttered by 61 native Russian speakers (of which 12 males) from 16 to 28 years old are included in this database. Features of speech l... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.13 Multilingual emotion speech database | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.13.1 Preface | Three Swiss official languages (French, German and Italian) and English language in database 31 and four languages (i.e. English, Slovenian, French and Spanish) in database 32 are used in the following databases. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.13.2 Database 31 | Lost Luggage study. K. Scherer has recorded another emotional speech database [i.117]. The recordings took place inside Geneva International Airport. The subjects are 109 airline passengers waiting in vain for their luggage to arrive on the belt. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.13.3 Database 32 | The database was recorded at the Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia [i.96]. It contains emotional speech in six emotion categories, such as disgust, surprise, joy, fear, anger and sadness. Two neutral emotions were also included: fast loud and low soft. It is seen th... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.14 Four other databases | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.14.1 Preface | Italian, Czech and English languages (different accents and non-native speakers) are used in the following emotion databases. ETSI ETSI TS 103 296 V1.1.1 (2016-08) 51 |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.14.2 Database 33 | EMOVO - Emotional corpus applicable to Italian language. It is a database built from voices of up to six actors who played 14 sentences simulating seven emotional states (disgust, fear, anger, joy, surprise, sadness and neutral (see clause 5.3 for details)).The recordings were made with professional equipment in the Fo... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.14.3 Database 34 | This real-life corpora SAFE Corpus illustrates everyday life contexts in which social emotions frequently occur. The type of emotional manifestations and the degree of intensity of such emotions are determined by politeness habits and cultural behaviours. The corpus focuses on the illustration of extreme fear-type emot... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.14.4 Database 35 | ExamStress is the database of Czech speech under realistic stressed conditions and presents some selected results achieved by analysing stressed speech. The database contains read and conversational speech both in neutral and stressed state of 31 male speakers. The stressed speech was recorded during final oral examina... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.14.5 Database 36 | EMANN - Emotional database for ANN training. The 6 sentences was read by three professional actors (two female and one male). The final database contained 720 patterns (360 patterns for one-word sentences and 360 patterns for multiword sentences).Speech recording was materialized in a recording studio with professional... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.5.15 Summary | A score value of the success rate of emotion identification is introduced in several cases. The total average accuracy is introduced only for 7 of the databases described (out of 36 databases). The scores range between 79 % (Danish database 26) and 85 % (Spanish database 24). The English databases show scores of 79 % a... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.6 Spoken Emotional Speech Pre-processing | 5.6.1 Preface The speech features that efficiently characterize the emotional content of speech are categorized in four classes. They are continuous speech features, qualitative speech features, spectral features and nonlinear TEO-Based features. They are components of the speech pre-processing. ETSI ETSI TS 103 296 V1... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.6.2 Introduction | Various researchers confuse terms features and parameters. The fundamental frequency F0, intensity and duration of speech units are basic parameters for emotion classification or emotion recognition. Also voice colour (timbre) and articulation mode are important for emotional speech. |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.6.3 Features | Typical features are the pitch (fundamental frequency of the phonation F0, pitch frequency), the formants (many algorithms for estimating the pitch signal and formants exist [i.133]), the vocal tract cross-section areas, the mel- frequency cepstral coefficients (MFCC), the Teager energy operator-based features, the int... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.6.4 Parameters | Parameters for emotional speech processing are various. V. A. Petrushin [i.138] employ the following acoustical variables: fundamental frequency F0 (also other authors used this one), energy, speaking rate, first three formants (F1, F2 and F3) and their bandwidths (BW1, BW2 and BW3) and calculated some descriptive stat... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 5.6.5 Methods and materials | Methods which are frequently used in emotion recognition are described hereafter. Mainly they are standard DSP methods. PRAAT is a free scientific computer software package for the analysis of speech in phonetics. The other classification techniques can be divided into several categories, namely those employing prosody... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 6.1 General considerations | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 6.1.1 Introduction | As already described in clause 4.5 of the present document, there are two different classifications that are used to classify emotions in general, namely categorical classification and dimensional classification. The latter is very complex, implying a lower reliability of emotion detection than the categorical classifi... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 6.1.2 Computational Power Requirements | It needs to be mentioned here that a testing phase, which is of most interest here, does not require a large amount of computational resources, e.g. computation time, memory space, etc., as a training phase of the detector. Therefore, the type of machine learning technique deployed by the detector should not also be of... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 6.1.3 Requirements for Operational Modes | Emotion detectors can be classified into two categories when it comes to types of operation. The first category contains the following operation modes: • real-time; • quasi-real-time (noticeable delay caused by the process, which is still acceptable for the rest of the process or other devices involved); • off-line. Th... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 6.2 Requirements for Emotion Detectors for Written Text | First of all, it shall be properly checked whether the application scenario of an emotion detector for written text, which is going to be deployed in the context of emotion detection for telecommunication measurement applications and systems, matches the application scenario required by the prospective user. It shall b... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 6.4.1 General description | This method represents a two-stage or in other terms a cascade approach. At the first stage, emotions carried (if any) by the text are supposed to be detected by an emotion detector for text. This part shall remove a reasonable portion of emotions carried by the text. So, this may also rapidly reduce the time spent or ... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 7.1 Introduction | This clause discusses the aspects of emotion detectors which provide binary results, i.e. the detection result is either that a text or speech sample contains emotions or that it does not contain emotions. It is seen as most likely that such binary detectors will be the first choice if it comes to the use of emotion de... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 7.2 Reference set of samples | It is important to understand that the accuracy of any emotion detector can only be assessed if the proportion of emotions contained in a reference set of text or speech samples is known with a certain interval. In order to assess the emotional content of a reference set of samples, it is mandatory to conduct a series ... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 7.3 Assessment of the accuracy | The accuracy of an emotion detector shall be assessed by comparing the output of the emotion detector with the a-priori determined emotional content of the reference set of samples. In a first step, false negative percentage (fn) and false positive percentage (fp) shall be determined. False negative fn is the percentag... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 7.4 Remaining Percentage of Samples | Having in mind that one of the main purposes of the use of emotion detectors in telecommunication measurement applications is the conductance of auditive (and emotion-free) speech material, it is also very important to consider the remaining number of samples after application of the emotion detector. This can be calcu... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 7.5 Examples of Single Emotion Detectors | Figures 23 to 25 show the principles of single emotion detectors (both for written text and for spoken speech) for different combinations of fn and fp. The original sample set contains 50 samples, by coloured dots, red indicating samples containing emotions and blue indicating emotion-free samples. By applying the emot... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 7.6 Examples of combined emotion detectors | |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 7.6.1 Optimum Recording process with no errors | Figures 26 to 28 show the principles of combined emotion detectors, where the first detector works for written text and the second for spoken speech for a different combination of fn and fp. As a simplification in these figures, the percentage of emotional content does not change when the set of samples is converted fr... |
0b4f01803e8c54dd6b19e65cbd20b96a | 103 296 | 7.6.2 Poor Recording process with errors | Figure 29 shows the principles of combined emotion detectors, where the first detector works for written text and the second for spoken speech for a different combination of fn and fp. As indicated by the black dots another factor of uncertainty is introduced in this case when the set of samples is converted from writt... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 1 Scope | The present document specifies and formalizes SmartBAN unified data representation formats (including in particular sensor/actuator/relay/coordinator/Hub descriptions and sensed/measured data), semantic and open data model and corresponding ontology. The present document addresses semantic interoperability. Therefore, ... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 2 References | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 2.1 Normative references | References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. Referenced documents which a... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 2.2 Informative references | References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. NOTE: While any hyperlinks i... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 3 Definitions of terms, symbols and abbreviations | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 3.1 Terms | For the purposes of the present document, the following terms apply: example 1: text used to clarify abstract rules by applying them literally |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 3.2 Symbols | For the purposes of the present document, the following symbols apply: bpm beats per minute bps bit per second s second ETSI ETSI TS 103 378 V1.2.1 (2026-01) 10 |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 3.3 Abbreviations | For the purposes of the present document, the following abbreviations apply: 3G Third Generation (réseau mobile) AC Alternating Current ANP Alert Notification Profile API Application Programming Interface ASTM American Standards for Testing and Materials BAN Body Area Network BLE Bluetooth Low Energy CCR Continuity of ... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 4 Introduction | The present document gives the high level description of a unified data format providing one of the building blocks for heterogeneity management in Smart BANs. The present document will in particular define a unified description model with an extensible semantic metadata for Smart BAN entities related data as well as f... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 5 Ambit and induced constraints | The scope of clause 5 of the present document is to briefly investigate the initial TC SmartBAN use case requirements in order to point out their impact on the present document specifications and designs. The initial additional requirements induced by the present document scenario will also be listed. Wireless Body Are... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6 SmartBAN open data model and ontology | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.1 SmartBAN information analysis | The objective of clause 6.1 of the present document is first to perform information analysis of the SmartBAN scenarios and to identify: • the relevant related information sources, types, formats and owners; • the information processing operations and functionality towards the SmartBAN related information. Then, based o... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.2 SmartBAN core ontologies | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.2.0 Introduction | One of the most important problems in electronic health is data heterogeneity management and interoperability between different systems. Two types of interoperability can be differentiated: 1) Syntactic and structural interoperability: the structure and provenance of information is understood by any clinical system. 2)... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.2.1 WBAN ontology | Classes: Figure 7: WBAN/SmartBAN Classes hierarchy Object Properties: Object property Domain Range hasContact WBAN Contact hasCommunicationProcess WBAN CommunicationProcess hasWbanLocation WBAN Location isAssociatedTo Role WBAN playsRole Contact Role uses Patient WBAN isUsedBy WBAN Patient hasHeight Patient Quantity (s... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.2.2 Nodes ontology | Classes: The nodes subclasses are depicted in Figure 8 and the nodes classes relationships are depicted in Figure 9. Figure 8: Node's subclasses Figure 9: Nodes Classes relationships ETSI ETSI TS 103 378 V1.2.1 (2026-01) 25 Object Properties: Object property Domain Range Cardinality min max cost NodeType quantity:Quant... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.2.3 Process ontology | Classes: The process classes and relations are depicted in Figure 10. Figure 10: Process classes and relations Object Properties: Object property Domain Range Cardinality min max calibration Process quantity:Quantity 1 acts node:Actuator Action drift Process quantity:Quantity measures node:Sensor Measurement isUsedFor ... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.2.4 Ontofull ontology | Ontofull ontology (see clause B.4 of the present document for the full OWL-DL specification in turtle format) shall include six direct imported ontologies: wban.owl (see annex B of the present document for the full OWL-DL specification in turtle format), nodes.ttl (see clause B.2 of the present document for the full OW... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.2.5 OWL-DL formalization of SmartBAN core ontologies | This clause presents the OWL-DL formalization that shall have TC SmartBAN core ontologies. Since all the ontology classes, objects and attributes were already fully detailed in the previous clauses of the present document, their corresponding text XML format will only be listed in Annex B of the present document. 6.3 S... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4 SmartBAN open service model and ontology | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.0 Introduction | One of the most important problems in electronic health is data interoperability between different systems. Two types of interoperability can be differentiated: 1) Syntactic and structural interoperability: the structure and provenance of information is understood by any clinical system. 2) Semantic Interoperability: h... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.1 SmartBAN ServiceProfile ontology | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.1.0 Introduction | The integration SmartBAN in WoT imposes that the BAN can be discovered by end users/software. At that end, services offered by SmartBAN should be described in a semantic manner. This is the aim of SmartBAN 'ServiceProfile' ontology. It fully describes the non-functional characteristics of a service. This information is... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.1.1 Service Profile Model | First of all, a service profile is described by its name and description (reproduced form OWL-S). Because a WBAN can cover a wide range of application domain having different requirements, constraints and characteristics, the domain experts were left the opportunity to classify the services based on their own criteria.... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.1.2 Service Profile Ontology Description | Clause 6.4.1.2 of the present document details the object properties and data properties of SmartBAN "ServiceProfile" ontology. The full description of the ontology is given in clause B.5. Object Properties: Object property Domain Range Additional description hasCalculationMethod QoS QoI hasServiceCategory ServiceProfi... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.2 SmartBAN ServiceProcess ontology | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.2.0 Introduction | The aim goal of SmartBAN service process sub-ontology is to describe how the service that is executed, as well as how a user (e.g. a caregiver) can interact with the service. As for OWL-S, SmatBAN "ServiceProcess" ontology is based on the IOPE principle. The same service profile can have many service process models. Fo... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.2.1 Service Process Model | Within OWL-S, a process is defined as an atomic process, or as a composite process expanded into one or more simple processes. The same idea is transposed for SmartBAN "ServiceProcess" model and WBAN. Basically in WBAN, a trivial service is a service providing the data collected by a sensor. Within the SmartBAN "Proces... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.2.2 Service Process Ontology Description | The "Atomic" class of the SmartBAN "ServiceProcess" ontology is equivalent to the "Process" class of SmartBAN "Process" sub-ontology (see clause 6.2.3 of the present document). In addition, the "ServiceProcess" class is also an abstract class that can be a composite, simple or atomic class. Object Properties: Object pr... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.3 SmartBAN ServiceGrounding ontology | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.3.0 Introduction | The SmartBAN service grounding ontology models how the service is invoked, as well as how to map semantic data to required data for transmission. This sub-ontology permits to find a service based on the technology enabled on the user (e.g. patient, caregiver, helper) side. a) As defined in OWL-S, each SmartBAN service ... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.3.1 Service Grounding ontology description | Object Properties: Object property Domain Range hasMessageElements ServiceGrounding MessageElements hasMessageOptions ServiceGrounding MessageOption Data Properties: Data Property Domain Range FeildDescription MessageElements or MessageOption string FeildLenght MessageElements or MessageOption integer FeildName Message... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.4 SmartBAN Service ontology | SmartBAN Service ontology is the global service ontology that directly imports the three aforementioned sub-ontologies. It contains the Service class described by the service's name and service description (same as in OWL-S, see clause A.1.3 of the present document). In addition, it also contains the object properties ... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.4.5 OWL-DL formalization of SmartBAN Service ontologies | This clause presents the OWL-DL formalization that shall have TC SmartBAN Service ontology. Since all the ontology classes, objects and attributes were already fully detailed in the previous clauses of the present document, their corresponding text XML format will only be listed in Annex B of the present document. |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.5 SmartBAN ontologies pre-validation | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.5.1 Introduction | The objective of clause 6.5 of the present document is to present the first pre-validation tests of the SmartBAN ontologies that have been conducted within TC SmartBAN. Mainly based on symbolic validation methods, those tests will demonstrate the a priori validity of the proposed SmartBAN ontology. To pre-validate the ... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.5.2 WBAN for runners | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.5.2.1 WBAN Description | For testing purposes, two WBANs Runner_Fadi and Runner_Lina have been created as depicted in Figure 17. Because the WBANID is a functional property, each WBAN can have only one WBANID. That is why the MAC address of the mobile where the application is downloaded was chosen as WBANID, knowing that the application can be... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.5.2.2 SWRL rules | The SWRL rule is added: Patient(?P), MaxHR(?P, ?Q), Age(?P, ?Age), multiply(?x, 0,7, ?Age), subtract(?y, 208, ?x) -> hasValue(?Q, ?y). If the heart beat sensor measures a value greater than the maximum heart rate, a notification will be sent to the user and the measurement will be considered as invalid in order to stop... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 6.5.2.3 SPARQL queries | To retrieve the information, the SPARQL engine implemented in Protégé has been used. Unfortunately, this engine cannot retrieve inferred data. Here is the example of the written query for node discovery: SELECT ?WBAN ?Nodes ?Hub WHERE { ?WBAN full:Contains ?Nodes. ?WBAN full:ElectHub?Hub. } The result of the query is g... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 7.1 Introduction | The objective of clause 7 of the present document is to address the mapping between SmartBAN modular ontologies and others of interest reference ontologies for smart BANs. ETSI ETSI TS 103 378 V1.2.1 (2026-01) 41 |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 7.2 Main reference ontologies to be considered | |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 7.2.1 Semantic Sensor Network (SSN/SOSA) ontology | Semantic Sensor Network (SSN) ontology is the most important ontology that describes sensors and observations, and related concepts [i.13]. However , it does not model domain concepts, time, locations, etc. Developed by W3C Semantic Sensor Network Incubator Group, SSN also includes a core ontology called 'Sensor, Obser... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 7.2.2 oneM2M ontology | oneM2M ontology [i.14] describes key classes, relations and properties that are necessary to enable semantic functionalities and interoperability between applications. This ontology is based on a Thing that has properties through ThingProperty. In oneM2M, a Device is a subclass of Thing that is designed to accomplish a... |
3f9d54f6de9d04e5961572297620cc2e | 103 378 | 7.2.3 Smart Applications REFerence model (SAREF) ontology | SAREF ontology is a reference ontology for IoT created in close interaction with the industry during a study requested by the European Commission in 2015 and subsequently transferred into an ETSI Technical Specification [i.15]. It contains core concepts that are common to several IoT domains, like e.g. a Device made to... |
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