Modelling Weightlifting “Training-Diet-Competition” Cycle

Ontology with Domain and Task Ontologies

Piyaporn Tumnark

1,3

, Miguel Abreu

, Miguel Macedo

, Paulo Cardoso

Jorge Cabral

and Filipe Conceição

Faculty of Sport, University of Porto, Porto, Portugal

Industrial Electronics Department, University of Minho, Braga, Portugal

Faculty of Sports Science, Kasetsart University, Kamphaeng Saen Campus, Nakorn Pathom, Thailand

Keywords: Ontology, Nutrition, Weightlifting, Biomechanics, Semantics, Reasoning.

Abstract: Studies in weightlifting have been characterized by unclear results, and paucity of information. This is due to

the fact that enhancing the understanding of the mechanics of successful lift requires collaborative

contributions of several stakeholders such as coach, nutritionist, biomechanist, and physiologist as well as the

aid of technical advances in motion analysis, data acquisition, and methods of analysis. Currently, there are

still a lack of knowledge sharing between these stakeholders. The knowledge owned by these experts are not

captures, classified or integrated into an information system for decision-making. In this study, we propose

an ontology-driven weightlifting knowledge model as a solution for promoting a better understanding of the

weightlifting domain as a whole. The study aims to build a knowledge framework for Olympic weightlifting,

bringing together related knowledge subdomains such as training methodology, biomechanics, and dietary

while modelling the synergy among them. In so doing, terminology, semantics, and used concepts will be

unified among researchers, coaches, nutritionists, and athletes to partially obviate the recognized limitations

and inconsistencies. The whole weightlifting "training-diet-competition" (TDC) cycle is semantically

modelled by conceiving, designing, and integrating domain and task ontologies with the latter devising

reasoning capability toward an automated and tailored weightlifting TDC cycle.

1 INTRODUCTION

In weightlifting, enhancing the understanding of the

mechanics of successful lift requires collaborative

contributions of several stakeholders such as coach,

nutritionist, biomechanist, and physiologist as well as

the aid of technical advances in motion analysis, data

acquisition, and methods of analysis. Currently, there

are still a lack of knowledge sharing between these

stakeholders. The knowledge owned by these experts

are not captures, classified or integrated into an

information system for decision-making. This

challenge leads to the problem of paucity of

information and inconsistencies of results regarding

an integrated biomechanical analysis, training

methodology, and nutrition analysis. In this study, we

propose an ontology-driven weightlifting knowledge

model as a solution for promoting a better under-

standing of the weightlifting domain as a whole.

Among many techniques, ontology is selected

because it has been wide accepted as a useful method

to simulate human proficiency in narrowly defined

domain during the problem solving stage, by

integrating descriptive, procedural, and reasoning

knowledges. It can unify concepts and terminologies

among weightlifting stakeholders, while partially

helping obviate the paucity and heterogeneity of

existing results. However, the weightlifting

knowledge model should be scalable to easily

integrate further related domain of weight-lifting, and

also used to support the implementation of

weightlifting recommender systems.

Literature about sport ontologies is rare. There are

only few ontologies targeting sport domain. For

example, Muthulakshmi (2015) developed an

ontology for sport training through e-learning which

is based on a query template for a storage and retrieval

of sports information. It has a basic concept of sports

ontology complemented with physiological variable

measured before and after events, as well as with

physical activity. Nwe Ni Aung and Naing (2011)

presented information retrieval from Sports Domain

Tumnark, P., Abreu, M., Macedo, M., Cardoso, P., Cabral, J. and Conceição, F.

Modelling Weightlifting “Training-Diet-Competition” Cycle Ontology with Domain and Task Ontologies.

DOI: 10.5220/0006929402070214

In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 2: KEOD, pages 207-214

ISBN: 978-989-758-330-8

207

Ontology using First-Order Logic rules and they

retrieved relevant semantic relationships between

concepts from it. Contrary to most of existing

ontology-based information retrieval systems which

use concepts mapping, they use semantic

relationships between ontology of concepts to

retrieve more relevant and correct results. Zhai and

Zhou (2010) proposed a sport ontology addressing

fine-grained granularity and wide coverage of

information for semantic retrieval for sports

information in www. They used SPARQL query

language to realize the intelligent retrieval at semantic

level according to the relations of “synonymy of”,

“kind of” and “part of” between sports concepts.

Although ontology-based works regarding to food

and nutrition is not new and some of them already

provided useful artefacts, there are not many studies

using integrated ontology approach to combine

knowledge from various domains to generate diet and

exercise suggestions. Dragoni et al. (2017) presented

PerKApp which aims to provide a full-ﬂedged

platform supporting the monitoring of people

behaviours while persuading them to follow healthy

lifestyles. They used semantic technologies for

modelling all relevant information and for fostering

reasoning activities by combining user-generated

data and domain knowledge. The integrated ontology

supports the creation of the dynamic interfaces used

by domain experts for designing monitoring rules.

Mihnea et al. (2011) proposed recommender system

of workout and nutrition for runners by integrating

web crawling and ontology. The system is a mixture

between experts’ knowledge and a social dimension

in generating the nutrition and workout plan. The

system provides information to users regarding the

workout and treatment recommendations, in case of

injury, alongside diet plan that best suits them, based

on their profile information, food preferences, and

goals.

With respect to works discussed in the literature,

this study aims to conceive and design an ontology-

enriched knowledge model to guide and support the

implementation of “Recommender system of workout

and nutrition for weightlifters”. In doing so, it will

propose: (i) understanding the weightlifting training

system, from both qualitative and quantitative

perspectives, following a modular ontology

modelling, (ii) understanding the weightlifting diet

following a modular ontology modelling, (iii)

semantically integrating weightlifting and nutrition

ontologies to mainly promote nutrition and

weightlifting snatch exercises interoperability, (iv)

extending modular ontology scope by mining rules

while analysing open data from the literature, and (v)

devising reasoning capability toward an automated

weightlifting “training-diet-competition” cycle

supported by previously mined rules. To the best of

our knowledge, this kind of design is innovative with

respect to the other systems due to the collaborative

contributions of several stakeholders such as coach,

nutritionist, and biomechanist for supporting the

monitoring of training and nutriton status of

weightlifter.

This paper is divided into four sections. Section 1

presents an introduction; Section 2 describes the

followed methodology for the ontology development;

Section 3 describes the constructed ontology and

rules derived from the development process. Finally,

some conclusion remarks are mentioned in the

Section 4.

2 METHODS

Based on the guidelines proposed by Chi et al. (2015),

the following approach is proposed to ontologically

model and design of the weightlifting TDC cycle.

2.1 Establishing the Domain Scope and

Analysing Problem Scenarios

Managing training and competition performance of

weightlifters is a very challenging problem due to the

interplay among multiple sources of unobserved

heterogeneity at athletes’ profile, competition,

training model, dietary protocol, research, resource,

or year level. It involves several knowledge sources,

spreading into several information dimensions such

as nutrition, training, and biomechanics (Figure 1).

Nutritional knowledge includes the deﬁnition of

dietary protocol, energy expenditure estimation,

energy balance, as well as food composition in terms

of macro- and micro-nutrients. Dietary protocol as a

concept, includes recommended food intake

according to athletes’ preferences and restrictions at

specific training and competition instants. Coaching

and training knowledge supports a qualitative

analysis technique which includes a controlled

vocabulary. It consists of common terms to alleviate

semantic differences between training methods,

lifting exercises and their phases concepts, as well as

barbell and body kinematics and kinetics. The

training dimension is mostly represented by

descriptive terms or abstract values. They are

regarding lifting exercises’ performance which can be

mapped to ground values measured in real-time by

biomechanics analysis systems or energy expenditure

measurement devices. Biomechanics knowledge

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supports a quantitative analysis approach based on

ground values and it includes a controlled vocabulary

consisting of sub-concepts (e.g., calibration method,

acquisition method, and analysis method,) and

concepts like lifting analysis, resource, and muscle

activity.

To implement the problem scenarios analysis, we

firstly tackle individually each information

dimension of the training-diet-competition (TDC)

cycle and only then formalize the problem solving

according to the following two sets of non-logical

axioms, required to estimate and/or measure

performance, as well as to examine and monitor the

designed and prescribed training to each individual

athlete.

Figure 1: The problem solving for improving weightlifting

ability.

2.1.1 Assessment and Monitoring of

Nutrition Dietary Features

Several prediction equations and method of analyses

will be required to estimate and measure the energy

expenditure which depends on muscular activation

and muscle contraction. Both anthropometric and

metabolic measurements having been carried out

because physical activities are usually classified in

terms of their mutually dependent biomechanical and

metabolic aspects, as well as their intensities and

durations. Therefore, not only body composition

should be assessed but also other potential sources of

change in energy expenditure during lifting activities,

through activation levels of major muscles groups. By

assessing the energy expenditures while lifting, the

prescribed energy intake will be examined to

determine the energy balance status and then

accordingly adjusted (i.e., in terms of macro-and

micro-nutrients) for a more suitable dietary intake.

For accurate measurement of energy expenditure and

outcome assessment, accelerometer-, heart rate-,

electromyography-, or calorimetric-based devices

have been used to collected data related to diverse

energy expenditure components.

2.1.2 Assessment and Monitoring of

Biomechanics Features

To maintain consistence in each lift while enhancing

performance, weightlifting biomechanics have been

analysed following qualitative, quantitative, and

predictive approaches, as well as combinations of

them (Ho et al., 2014). Quantitative approaches have

been toward kinetics and kinematics of barbell and

weightlifter body, mainly trying to classify barbell

trajectory, identify optimal lifting technique, quantify

barbell parameters, joint angle, and applied force. In

so doing, it required several devices such as motion

capture systems, force plates, and EMG, as well as

several and different method of analysis.

Having already defined the motivation for

addressing issues related to the weightlifting TDC

cycle, the following general competence questions

were formulated to be answered by the ontology and

so, limiting the ontology scope:

1) Did the athlete properly lift the barbell?

2) Did the athlete’s body move accordingly

during exercises phases?

3) Was the athlete well-served in terms of

macronutrients and micronutrients according

to the training protocol specificity?

4) Did the rhythmic execution reflect an efﬁcient

snatch technique?

The rhythmic execution, should be understood as the

definition presented by Ho et al. (2014) i.e., the

coordination movement of the weightlifter-barbell

system for an efﬁcient and effective lift.

Figure 2: Weightlifting TDC-cycle OWL- and Rule-

Knowledge-based System.

In Figure 2, each actor plays a fundamental role in

the assessment task. The Reasoning and Knowledge

Base layer encompasses three non-overlapping

sublayers. The four perspectives are defined as

follows. (i) Task Fact Base (FB) encloses task related

instances. The Athlete creates its profile by inserting

relevant personal data whereas the Training Manager

and Lab Technician are in charge of updating the

Modelling Weightlifting “Training-Diet-Competition” Cycle Ontology with Domain and Task Ontologies

209

knowledge base with training data, respectively,

providing qualitative and quantitative assessments.

(ii) Reasoning and Knowledge Base (KB) is

composed of all available knowledge over which the

reasoning is performed. The Task FB input is used as

a trigger to start the inference process, which is based

on SWRL rules whereas the output of that process is

given as a series of axioms, representing detailed

results with practical, human readable data. (iii) Task

Rules comprises all SWRL rules created to infer

knowledge from training related instances. These

rules may be created or updated by several experts

from different domains. (iv) Domain Knowledge Base

refers to the application-independent axioms, which

can be updated to better cope with improvements in

the understanding of applicable fields. Knowledge

bases are implemented as ontologies, which were

divided into assertion axioms (i.e., Fact base; FB)

and terminological axioms (i.e., Concepts and

Attributes; CA) to illustrate the interaction of both

areas in the global architecture. Each KB and

respective rules were created using Protégé and its

plug-ins.

2.2 Modelling and Design of the

Weightlifting Domain Ontology

To obtain a deep understanding of aspects and concrete

entities comprising the weightlifting TDC cycle, repe-

titive collaboration meetings were organized between

athletes, coaches, biomechanist and nutritionist along

with electronics and software engineers. The following

design artefacts express ontologies in the weightlifting

TDC-cycle knowledge -based system i.e., TDC-

Ontology = (CA, CV, FB, R, A):

2.2.1 Concepts and Attributes (CA)

Different concepts in the TDC-Ontology have been

divided into four main knowledge sets: training,

biomechanics, nutrition, and problem solving,

complemented with an athlete profile concept as

nearly all observation and measurement are around

athlete's activities. The first three sets correspond to

domain ontology which identifies general concepts

and their relations in the field of weightlifting, while

the fourth one is part of the task ontology.

Training-or coaching-related ontology subset

refers to classes modelling exercises performed by

athlete, with each exercise consisting of several

phases. Basically, these concepts are used to promote

a qualitative weightlifting analysis and are mainly

represented by abstract values regarding observable

lifting performance by a coach.

Biomechanics-related ontology subset is used to

leverage a quantitative weightlifting analysis and are

represented by the ExerciseProperty concept. The

main purpose is complementing qualitative lifting

performance values with biomechanics ground values

measured during a lifting exercise phase, using

biomechanics equipment.

Nutrition-related ontology subset is also used to

leverage a quantitative weightlifting analysis and it is

modelled by the following subclasses. The Dietary

Protocol related to each workout period, the

respective NutrientPortions, and the Consumable

having nutrients. Nutritional ground values are

measured for a lifting exercise phase, using a

combination of energy expenditure measurement

equipment, prediction equations, and methods of

analysis. The DietaryProtocol concept prescribes the

receipt of nutrient portions for a specified workout

phase, the NutrientPortions concept identifies a

specific nutrient and its amount in terms of macro-

and micro-nutrients and the Consumable concept

represents the food and drink that are sources of

nutrients. In this prototype, Consumable concept are

adopted from our previous work (Tumnark et al.,

2013). However, in the future, we may consider

adopting the food concept from other available

literature in order to cover all available menus items.

2.2.2 The Controlled Vocabulary (CV)

Horizontal to concepts defined in CA, there is a list of

authorized keywords, used across both domain and

task ontology. The list contains nine subclasses and

under each of them, authorized keywords are used to

provide reference and indexing for communication

with other concepts and instances. Subclasses are the

WorkoutPhase concept defining periods for which a

dietary protocol is prescribed, which is instantiated as

authorized keywords Preworkout, Duringworkout,

and Postworkout. The DayPart concept represents

day time prescribed for weightlifting training and

dietary intake which is instantiated as authorized

keywords Morning, Afternoon, and Evening. The

Acquisition Method concept establishes methods used

to collect quantitative ground values, e.g., heart rate

monitor, motion analysis, electromyography (EMG),

or force measurement; Muscle concept defines

muscles where activity should be measured, e.g.,

VastusLateralis, Biceps Femoris, PectineusGracilis.

The AnalysisMethod concept establishes analysis

methods used for the assessment of energy

expenditure and biomechanics features from several

kinds of collected data, such as kinetics, kinematics,

and physiological. The Calibration Method concept

KEOD 2018 - 10th International Conference on Knowledge Engineering and Ontology Development

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establishes some known methods for proper

calibration of biomechanics equipment which is

instantiated as authorized keywords OnePointCal,

TwoPointCal, and Curve FittingCal. The Resource

Type concept defines resource types used for

quantitative measurement of barbell/body kinematics

and kinetics (e.g., video camera, infrared cameras,

force plates), body composition, as well as training

resource (e.g., barbell and weight plates). The

Nutrient concept includes groups of macro- and

micro-nutrients, as standard vocabulary used in

energy expenditure assessment and dietary intake to

promote optimal health and performance across

different scenarios of weightlifting training. The

ExerciseMethod concept classifies weightlifting

training methods under Bulgarian or Russian

frameworks and principles.

2.2.3 The Fact Base as a Set of Individual

(FB)

Concepts in the domain ontology are further

elaborated and terminal concepts are described in

terms of instances. These individuals belonging to the

ontology will act as the foundations of the knowledge

base supporting the problem solving activity. The fact

base is populated by a collection of facts generated

through the elaboration of domain ontology concepts,

i.e., terminal concepts are described in terms of

instances. These instances contain measured

nutritional and biomechanics ground values as well as

observable training-related abstract values collected

by coaches which are mapped to corresponding

ground values.

2.2.4 Relationship between Classes (R)

Excluding the data properties presented in Table 1,

the remaining relationships (i.e., among classes) are

constructed as object properties. Figure 3 displays

some individuals that represent the analysis of a phase

of the Snatch exercise. The Snatch exercise individual

is related to five phases/six positions by the object

property hasExercisePhase and, for the Firstpull

phase (Liftoffposition), there are some Exercise

Property individuals where each is related to a Result

individual that belongs to an individual of the

PhaseAnalysis concept, called LiftoffAnalysis.

2.3 Engineering the Task Ontology

To solve specific weightlifting TDC-cycle problems

as previously formulated through competency

questions, the task ontology will use the conceptual

structure of the domain ontology expressing the

semantic knowledge of biomechanics, nutrition, and

training dimensions of the TDC-cycle, while defining

other concepts’ constituent properties to describe the

problem solving structure. Basically, (i) property

values of known facts or unknown knowledge are

defined to separate asserted properties from inferred

ones, (ii) the corresponding domain and range of

properties are asserted, and then, (iii) Semantic Web

Rule Language (SWRL) rules supported by Sematic

Query-Enhanced Web Rule Language (SQWRL) are

created for reasoning about individuals on FB and so,

addressing the insufficient expressivity of ontologies

in properties association and operation required by

the formulated competency questions.

Table 1: Data properties of each concept presented on the

domain ontology.

Figure 3: Some individuals and their associated object

properties.

Generically, the problem solving structure

consists of two main groups: nutrition analysis and

training analysis (i.e., addressed both in terms of

qualitative and quantitative analysis, being the latter

achieved through biomechanics analysis) according

to Figure 1 and also the aforementioned competency

questions. Therefore, some concepts that constitute

the problem solving structure are:

Modelling Weightlifting “Training-Diet-Competition” Cycle Ontology with Domain and Task Ontologies

211

The AthleteProfileAnalysis concept contains 9

properties, being 8 asserted properties and 1 inferred

from rule EEE (Exercise Energy Expenditure).

The PhaseAnalysis concept contains 8 properties.

6 are asserted properties and 2 are inferred properties,

which are used for the evaluation of an exercise's

phase. (see rule analyse).

The ResourceAnalysis concept contains 5

asserted properties and 1 inferred property that

represents the accuracy of the resource. It is inferred

using rule topResources.

The ExercisePropertyAnalysis concept contains 2

asserted properties and another property that is either

asserted or inferred, to represent the evaluation of the

result. When inferred, this evaluation maps to rules

evaluateMax, evaluateMin, and evaluateMinMax.

The TrainingDayAnalysis concept contains 9

properties, where 3 are asserted and 6 are inferred.

The exercise energy expenditure (EEE) is inferred by

the rule EEE. The total energy needed (TEN) and the

resting metabolic rate (RMR) are inferred by rules

TENmale or TENfemale. The energy intake is inferred

by the rule EI while the difference between consumed

and energy needed is mapped to the Rule balance.

One property was used to report dietary problems of

the training day, which is inferred from rules

evaluateNutrientsMax and evaluateNutrientsMin.

Three of these concepts are combined to form a

complete biomechanics and nutrition analysis chain,

being the core of the problem solving structure.

Starting with the ExercisePropertyAnalysis, this

concept analyses the individual biomechanics

characteristics of an exercise which are mapped to the

ExerciseProperty concept. Then, PhaseAnalysis

focuses on several phases of each exercise and

provides a broader analysis of the biomechanics of an

exercise. Lastly, TrainingDayAnalysis encompasses

the analysis of nutrition for a full training day of

multiple exercises.

3 RESULTS

All the 11 inferred properties of the Task Ontology

require semantic rules that relate facts and, thus, are

able to infer new knowledge. In order to answer all

the competency questions, SWRL-based rules and

SQWRL queries have been used. Although SWRL is

built on the same description logic foundation as

OWL-DL, it provides strong formal guarantees when

performing inference. It has considerably more

expressive power than OWL alone, particularly when

dealing with complex interrelationships between

OWL individuals or when reasoning with data values

(Dhingra and Bhatia, 2015). In this study, SWRL

rules operate over the instances of the ontology and

are expressed as a chain of atoms that, if all hold true,

a consequence is produced. SQWRL queries work

similarly to the SWRL rules but they are used for

retrieving knowledge from the ontology instead of

creating it. Also, query's result needs to be manually

added to the ontology. Overall, 9 rules and 3 queries

were created and these can be separated into three

broad categories: Biomechanics, Nutrition, and

Resource reliability.

3.1 Developing Semantic Rules

Due to the space limitations, only some of the drafted

SWRL rules are described in detail.

Biomechanics/Coaching Rules

1) evaluateMinMax used for the evaluation of an

exercise and it starts by evaluating if each of its

properties are within a considered favorable range. It

verifies whether the value of an exercise property's

result is within the specified range, and in case of

being true, it causes the result to receive a positive

evaluation denoted by the word "OK". Breaking

down the rule, it starts by obtaining an Exercise

PropertyAnalysis individual called r (1) and its value

(2) using the r's hasValue data property. Then it

obtains, through the hasExerciseProperty object

property, the ExerciseProperty individual p (3) and,

like before, its min and max values (4-5) are retrieved

using the hasMin and hasMax data properties,

respectively. After obtaining all the necessary values,

the rule then checks if the result's value is within the

exercise property's range (6-7) and it asserts r's

evaluation as "OK" (8).

Rule: evaluateMinMax

ExercisePropertyAnalysis(?r)

(1)

^ hasValue(?r, ?v)

(2)

^ hasExerciseProperty(?r, ?p)

(3)

^ hasMin(?p, ?min)

(4)

^ hasMax(?p,?max)

(5)

^swrlb:greaterThanOrEqual(?v,

?min)

(6)

^swrlb:lessThanOrEqual(?v, ?max)

(7)

-> hasEvaluation(?r, "OK")

(8)

2) evaluateMin/evaluateMax are used to evaluate if

the value of the result is below the minimum or above

the maximum. It uses the ExerciseProperty's name to

be easily identifiable, as this evaluation will be later

used for the overall examination of the exercise.

3) analyse examines if the exercise was not properly

executed by checking if there are any unsuccessful

results and so, reporting all associated problems.

KEOD 2018 - 10th International Conference on Knowledge Engineering and Ontology Development

212

Nutrition Rules

4) EEE calculates the Exercise Energy Expenditure

based on the formula EEE = METs * 0.0175 * Weight

* Duration.

Rule: EEE

TrainingDayAnalysis(?tda)

^ hasPhaseAnalysis(?tda, ?pa)

^ hasResult(?pa, ?r)

^ hasExercisePhase(?pa, ?ep)

^ EPDuration(?p)

^ hasExerciseProperty(?r, ?p)

^ hasValue(?r, ?d)

^ hasTrainingDay(?tda, ?td)

^ hasAthlete(?td, ?a)

^ hasWeight(?a, ?w)

^ hasExerciseRoutine(?td, ?er)

^ hasExercise(?er, ?e)

^ hasExercisePhase(?e, ?ep)

^ hasMET(?e, ?m)

^ swrlb:multiply(?v0,"0.0175"^^

xsd:float, ?m)

^ swrlb:multiply(?v1, ?v0, ?d)

^ swrlb:multiply(?v2, ?v1, ?w)

˚ sqwrl:makeBag(?b, ?v2)

^ sqwrl:groupBy(?b, ?tda)

˚ sqwrl:sum(?s, ?b)

-> sqwrl:select(?tda, ?s)

5) femaleTEN calculates the RMR and the amount of

energy needed (TEN) by an athlete.

6) balance compares the energy intake with the

amount of energy needed to calculate the energy

difference.

7) EI is used to calculate the necessary energy intake

for a training day.

8) evaluateNutrinetsMin evaluates the athlete's

nutrients intake for each workout phase. In this case,

it evaluates if the intake is below the recommended

level and reports a problem.

Resource Reliability Rule

9) topResources retrieves all the resources for each

type in descending order of accuracy.

Rule: topResources

ResourceAnalysis(?res)

^ hasResourceType(?res, ?rt)

^ hasMethod(?res, ?m)

^ hasAccuracy(?m, ?ac)

˚ sqwrl:makeBag(?b, ?ac)

^ sqwrl:groupBy(?b, ?rt,?res)

˚ sqwrl:max(?max, ?b)

-> sqwrl:select(?rt,?res,?max)

3.2 Evaluation of the Knowledge

Representation

To illustrate the reasoning process, a simple test case

was inserted in Protégé. The athlete had to perform a

full Snatch lift, while monitoring numerous

biomechanical variables. To accomplish that, six

instances of PhaseAnalysis were created along with

several phase related sensor results. These values,

which are ExecisePropertyAnalysis instances (Figure

4), were linked to the analysis instance via the

hasResult object property.

Figure 4: Snatch, its six phases and all associated exercise

property analysis instances.

Figure 5: Rule based evaluation of Transition phase and

Turnover phase of Snatch.

Upon comparison of the results with exercise

ranges (domain knowledge), Pellet, which was the

chosen reasoner, inferred the existence of 2 values out

of bounds in the third phase of the exercise as

presented in Figure 5. The value of thigh angle and

knee joint angle were above maximum value and the

exercise was not declared as compensated by The

training manager. So, the evaluation was reported as

"failed". It means that there were errors in lifting's

technique of this athlete regarding the movement of

thigh and knee and it was not approved by an expert.

On the right side of the same figure, is presented a

different case, i.e., the analysis of the fifth phase. The

system generated no problems because it was

manually reported as being compensated by the

Training Manager. In this case, even there was an

error in lifting's technique of an athlete, it was

approved quantitatively by an expert.

Modelling Weightlifting “Training-Diet-Competition” Cycle Ontology with Domain and Task Ontologies

213

4 CONCLUSIONS

This study demonstrated the use of Ontology Web

Language (OWL) and SWRL to semantically model

the whole weightlifting TDC-cycle, bringing together

related knowledge subdomains, while modeling the

synergy among them. Nutritional, biomechanics, and

coaching/training facts were combined with SWRL

rules representing rhythmic execution and energy

balance to infer athlete’ lifting performance.

Moreover, these rules can be used to trigger and

classify any qualitative-quantitative lifting mismatch

as corner cases which will deserve deeper and future

quantitative analysis, both regarding nutritional and

biomechanics perspectives. Each KB and respective

rules in TDC Competency Questions Engine

Architecture were created using only Protégé and its

plug-ins, resulting into: 43 classes, 57 properties, and

29 relationships. Overall, 9 SWRL rules, and 3

SQWRL queries were created and these can be

separated into three broad categories: Biomechanics,

Nutrition, and Resource reliability.

Beside the advantages that was mentioned

earlier, coaches and athletes can be benefited from

this system in several ways such as it can help coaches

to identify errors in the technique during the lifting.

This is due to the fact that errors can be overcome by

compensatory movement and successful lift can still

be achieved. This point causes a gap between the

weightlifter’s actual performance and what the

weightlifter could potentially lift. This system could

narrow this gap and help to identify which factors

lead to efficient technique and which ones limit the

performance. In case that the FB is large enough, the

novel factors/relationships might be discovered.

In spite of the mentioned applicability of the

proposed weightlifting TDC-cycle OWL knowledge-

based system, few drawbacks have been identified to

be later tackled in the next iterated TDC-ontology:

1) Re-design the TDC-Ontology to address domain-

level modularity, as well as being more scalable.

2) Devise the integration of new concepts and

properties which will ease the modeling of corner

cases (i.e., qualitative-quantitative lifting mismatch).

3) Iteratively tune rhythmic execution SWRL rules

according to identified corner cases, biomechanics

analysis, and optimization approaches, as well as to

reference top performance athletes, both in terms of

rhythm and anthropometric features.

Furthermore, more tests should be made based not

only on open data presented and discussed in the

existing literature but also lively collected during

weightlifting training.

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