Gesture Recognition Ontology

FLOW ALGEBRA
Gesture Recognition Ontology
Kireth Semantic Research Archive

PAGE:
https://kirethresearch.org/booksai-core/ontologies/computing-ontologies/human-computer-interaction/gesture-recognition-ontology.html

AUTHORS:
Mark Anthony Higgins — Sky (Recursive AI)

FLOW "GestureRecognitionOntology" v4.0

# ---------- DOMAIN ----------
DOMAIN := ComputingOntology
SUBDOMAIN := HumanComputerInteraction
ONTOLOGY := GestureRecognitionOntology

# ---------- PRIMARY ENTITY ----------
ENTITY GestureRecognition {
    definition:
        "The computational process of sensing, representing, analysing and
         classifying human body, hand, face or motion patterns as meaningful
         gestures that can be interpreted as commands, expressions or
         interaction signals.";

    objectives: [
        GestureDetection,
        GestureClassification,
        HumanComputerInteraction,
        MotionInterpretation,
        IntentRecognition,
        NaturalInteraction
    ];
}

# ---------- CORE ENTITIES ----------
GestureEntity := {
    User,
    Gesture,
    BodyPart,
    Sensor,
    Observation,
    Feature,
    GestureModel,
    InteractionCommand
};

GestureType := {
    StaticGesture,
    DynamicGesture,
    HandGesture,
    ArmGesture,
    BodyGesture,
    FacialGesture,
    TouchGesture
};

SensorType := {
    Camera,
    DepthSensor,
    MotionSensor,
    InertialSensor,
    TouchSensor,
    WearableSensor
};

RecognitionState := {
    Idle,
    Observing,
    GestureDetected,
    Classifying,
    Recognised,
    Rejected,
    Ambiguous
};

# ---------- FUNDAMENTAL RELATIONSHIPS ----------
User PERFORMS Gesture;
Gesture INVOLVES BodyPart;
Sensor OBSERVES Gesture;
Sensor PRODUCES Observation;
Observation CONTAINS GestureEvidence;
Feature EXTRACTED_FROM Observation;
GestureModel CLASSIFIES Feature;
RecognisedGesture MAY_MAP_TO InteractionCommand;
InteractionCommand AFFECTS InteractiveSystem;

# ---------- RECOGNITION FLOW ----------
UserMovement ->
    SensorCapture
    -> PreprocessObservation
    -> DetectCandidateGesture
    -> SegmentGesture
    -> ExtractFeatures
    -> ApplyGestureModel
    -> ScoreCandidates
    -> SelectGesture
    -> MapToMeaningOrCommand;

# ---------- GESTURE ----------
Gesture :=
    DeliberateOrMeaningfulPhysicalAction;

Gesture MAY_HAVE {
    StartTime,
    EndTime,
    Duration,
    Position,
    Orientation,
    Trajectory,
    Velocity
};

# ---------- STATIC GESTURE ----------
StaticGesture :=
    GestureRepresentedPrimarilyByPoseAtMomentOrInterval;

StaticGestureRecognition MAY_USE {
    Shape,
    Position,
    Orientation,
    FingerConfiguration
};

# ---------- DYNAMIC GESTURE ----------
DynamicGesture :=
    GestureDefinedByChangeAcrossTime;

DynamicGesture MAY_HAVE {
    Trajectory,
    VelocityProfile,
    TemporalSequence,
    Direction
};

DynamicGestureRecognition REQUIRES TemporalInformation;

# ---------- HAND GESTURE ----------
HandGesture :=
    GesturePerformedPrimarilyWithHandOrFingers;

HandGesture MAY_ENCODE {
    Selection,
    Direction,
    Quantity,
    Symbol,
    Command
};

# ---------- BODY GESTURE ----------
BodyGesture :=
    GestureUsingBodyPostureOrMovement;

BodyGesture MAY_USE {
    Head,
    Torso,
    Arms,
    Legs
};

# ---------- FACIAL GESTURE ----------
FacialGesture :=
    FacialMovementOrConfigurationUsedAsInteractionSignal;

FacialGesture MAY_INCLUDE {
    Blink,
    GazeShift,
    MouthMovement,
    EyebrowMovement
};

# ---------- SENSOR CAPTURE ----------
SensorCapture :=
    Sensor
    -> MeasurePhysicalSignal
    -> ProduceRawObservation;

RawObservation MAY_BE {
    ImageFrame,
    DepthFrame,
    MotionVector,
    Acceleration,
    Rotation,
    TouchCoordinates
};

# ---------- PREPROCESSING ----------
Preprocessing MAY_INCLUDE {
    NoiseReduction,
    Normalisation,
    BackgroundRemoval,
    CoordinateTransformation,
    TemporalSmoothing
};

Preprocessing SEEKS ImproveSignalQuality;

# ---------- GESTURE DETECTION ----------
GestureDetection :=
    ObservationStream
    -> IdentifyPotentialGestureActivity;

IF MovementOrPoseMatchesDetectionCondition
THEN CandidateGesture := true;

Detection PRECEDES ClassificationInCommonPipeline;

# ---------- SEGMENTATION ----------
GestureSegmentation :=
    ContinuousObservationStream
    -> IdentifyGestureStart
    -> IdentifyGestureEnd
    -> ExtractGestureSegment;

SegmentationError MAY_AFFECT RecognitionAccuracy;

# ---------- FEATURE EXTRACTION ----------
GestureFeature MAY_INCLUDE {
    Position,
    RelativePosition,
    Angle,
    Distance,
    Shape,
    Velocity,
    Acceleration,
    Trajectory,
    TemporalPattern
};

FeatureExtraction :=
    GestureObservation
    -> FeatureVectorOrSequence;

# ---------- COORDINATE NORMALISATION ----------
CoordinateNormalisation :=
    TransformObservedCoordinates
    TO CommonReferenceFrame;

Normalisation MAY_REDUCE VariationFrom {
    UserPosition,
    Scale,
    SensorPlacement
};

# ---------- GESTURE MODEL ----------
GestureModel :=
    ModelMappingFeaturesToGestureClass;

GestureModel MAY_BE {
    RuleBasedModel,
    TemplateModel,
    StatisticalModel,
    MachineLearningModel,
    NeuralModel
};

# ---------- TEMPLATE MATCHING ----------
TemplateMatching :=
    ObservedGesture
    -> CompareWithGestureTemplates
    -> SimilarityScores
    -> BestMatch;

IF SimilarityScore >= Threshold
THEN Gesture MAY_BE Recognised;

# ---------- CLASSIFICATION ----------
GestureClassification :=
    FeatureRepresentation
    -> GestureModel
    -> ClassScores
    -> PredictedGesture;

PredictedGesture :=
    argmax(ClassScore);

# ---------- CONFIDENCE ----------
RecognitionConfidence :=
    f(
        ModelScore,
        ObservationQuality,
        GestureCompleteness,
        ClassSeparation,
        Context
    );

IF RecognitionConfidence < AcceptanceThreshold
THEN RecognitionState := AmbiguousOrRejected;

# ---------- CONTEXT ----------
InteractionContext MAY_INCLUDE {
    CurrentApplicationState,
    UserTask,
    PreviousGesture,
    DeviceState,
    Location
};

Context MAY_DISAMBIGUATE GestureMeaning;

SameGesture MAY_HAVE DifferentMeaningInDifferentContext;

# ---------- COMMAND MAPPING ----------
GestureCommandMapping :=
    RecognisedGesture
    + InteractionContext
    -> InteractionCommand;

InteractionCommand MAY_BE {
    Select,
    Navigate,
    Manipulate,
    Confirm,
    Cancel,
    Scroll,
    Zoom,
    Rotate
};

# ---------- CONTINUOUS GESTURE ----------
ContinuousGesture :=
    GestureWhoseParametersContinuouslyControlSystem;

Example:
    HandMovement
    -> PointerMovement;

ContinuousGestureMapping MAY_USE PositionVelocityOrOrientation;

# ---------- DISCRETE GESTURE ----------
DiscreteGesture :=
    GestureRecognisedAsDistinctEvent;

DiscreteGesture MAY_TRIGGER SingleCommand;

# ---------- MULTI-GESTURE SEQUENCE ----------
GestureSequence :=
    ordered_set(Gesture);

SequenceMeaning MAY_DIFFER_FROM IndividualGestureMeaning;

TemporalModel MAY_CLASSIFY GestureSequence;

# ---------- MULTIMODAL INTERACTION ----------
Gesture MAY_BE_COMBINED_WITH {
    Speech,
    Gaze,
    Touch,
    KeyboardInput
};

MultimodalFusion :=
    CombineMultipleInputModalities
    -> InterpretUserIntent;

# ---------- FEEDBACK ----------
GestureInterface SHOULD_PROVIDE Feedback;

Feedback MAY_INDICATE {
    GestureDetected,
    GestureRecognised,
    CommandExecuted,
    GestureRejected
};

IF RecognitionUncertain
THEN Interface MAY_REQUEST Confirmation;

# ---------- FALSE POSITIVE ----------
FalsePositive :=
    SystemRecognisesGesture
    WHEN NoIntendedGestureOccurred;

# ---------- FALSE NEGATIVE ----------
FalseNegative :=
    IntendedGestureOccurs
    BUT SystemFailsToRecognise;

RecognitionQuality SHOULD_CONSIDER {
    Accuracy,
    FalsePositiveRate,
    FalseNegativeRate,
    Latency
};

# ---------- LATENCY ----------
RecognitionLatency :=
    TimeFromGestureEvidenceToRecognitionResult;

InteractiveGestureSystem SHOULD_KEEP LatencyAppropriateForTask;

HighLatency MAY_REDUCE PerceivedResponsiveness;

# ---------- ROBUSTNESS ----------
GestureRecognitionRobustness :=
    AbilityToMaintainRecognitionAcrossVariation;

Variation MAY_INCLUDE {
    User,
    Speed,
    Scale,
    Lighting,
    Background,
    SensorNoise,
    Viewpoint
};

# ---------- PERSONALISATION ----------
GestureModel MAY_BE PersonalisedToUser;

Calibration MAY_CAPTURE UserSpecificGestureCharacteristics;

Personalisation MAY_IMPROVE RecognitionForIndividualUser;

# ---------- ACCESSIBILITY ----------
GestureInteraction SHOULD_NOT AssumeAllUsersCanPerformSameMovements;

AlternativeInput SHOULD_BE ConsideredForRequiredFunctions;

GestureDesign SHOULD_CONSIDER {
    MotorCapability,
    Fatigue,
    RangeOfMotion,
    CulturalMeaning
};

# ---------- FATIGUE ----------
RepeatedOrLargeGesture MAY_CAUSE PhysicalFatigue;

InteractionDesign SHOULD_AVOID UnnecessaryHighEffortGestures;

# ---------- PRIVACY ----------
GestureSensors MAY_CAPTURE SensitiveVisualOrBehaviouralData;

PrivacyControl MAY_INCLUDE {
    LocalProcessing,
    DataMinimisation,
    AccessRestriction,
    RetentionControl
};

# ---------- SECURITY ----------
Gesture MAY_BE UsedAsAuthenticationEvidenceInSomeSystems;

GestureRecognition != IdentityAuthenticationByDefault;

SpoofingRisk SHOULD_BE_EVALUATED_IF GestureUsedForSecurity;

# ---------- TRAINING DATA ----------
MachineLearningGestureModel MAY_REQUIRE TrainingDataset;

TrainingDataset SHOULD_REPRESENT RelevantVariation;

DatasetBias MAY_REDUCE RecognitionQualityForUnderrepresentedUsersOrContexts;

# ---------- EVALUATION ----------
GestureRecognitionEvaluation MAY_MEASURE {
    Accuracy,
    Precision,
    Recall,
    ConfusionMatrix,
    RecognitionLatency,
    UserTaskSuccess
};

Evaluation SHOULD_USE RepresentativeUsersAndConditions;

# ---------- SEMANTIC REASONING ----------
IF SensorObservesHumanMovement
AND SystemMapsMovementPatternToGestureClass
THEN GestureRecognitionClassification := supported;

IF GestureChangesAcrossTime
THEN GestureType := DynamicGesture;

IF GestureRepresentedPrimarilyByPose
THEN GestureType := StaticGesture;

IF RecognitionConfidenceLow
THEN CommandExecution SHOULD_BE DelayedOrConfirmedWhereRiskRelevant;

IF FalsePositiveRateHigh
THEN GestureDetectionOrThreshold SHOULD_BE Reviewed;

IF RequiredGestureCannotBePerformedByUser
THEN AccessibilityBarrier := true;

IF SensorConditionsDegrade
THEN RecognitionConfidence MAY_DECREASE;

# ---------- SEMANTIC EQUIVALENCE ----------
SEMANTIC_EQUIVALENCE {
    "gesture recognition" ~= "gesture detection and classification";
    "hand gesture recognition" ~= "hand movement recognition";
    "static gesture" ~= "pose gesture";
    "dynamic gesture" ~= "motion gesture";
    "gesture classifier" ~= "gesture classification model";
    "gesture segmentation" ~= "gesture boundary detection";
    "gesture interface" ~= "gesture-based interface";
    "motion recognition" ~= "gesture recognition" WHEN context == intentional_human_interaction;
}

# ---------- QUERY FLOW ----------
QUERY(q) ->
    NORMALISE(q) ->
    IDENTIFY_USER_AND_BODY_PART(q) ->
    IDENTIFY_SENSOR(q) ->
    IDENTIFY_GESTURE_TYPE(q) ->
    IDENTIFY_OBSERVATIONS(q) ->
    IDENTIFY_FEATURES(q) ->
    IDENTIFY_GESTURE_MODEL(q) ->
    IDENTIFY_CONTEXT_AND_COMMAND(q) ->
    MAP_TO_ONTOLOGY(q) ->
    APPLY_REASONING(q) ->
    SCORE_CONFIDENCE(q) ->
    RETURN_RELEVANT_KNOWLEDGE;

# ---------- CONFIDENCE ----------
CONFIDENCE(assertion) :=
    weighted_mean(
        source_reliability,
        observation_quality,
        model_confidence,
        evaluation_evidence,
        semantic_consistency,
        contextual_relevance
    );

CONFIDENCE >= 0.90 => HIGH;
CONFIDENCE >= 0.70 AND < 0.90 => MODERATE;
CONFIDENCE < 0.70 => REVIEW;

# ---------- AI CONSUMPTION ----------
AI_CONSUMPTION {
    primary_entity: GestureRecognition;
    ontology: GestureRecognitionOntology;
    parse_entities: true;
    parse_relationships: true;
    preserve_relationship_direction: true;
    resolve_semantic_equivalence: true;
    execute_reasoning_rules: true;
    apply_confidence_weighting: true;
    preserve_provenance: true;
}

# ---------- UPDATE MODEL ----------
UPDATE_MODEL {
    new_gesture:
        validate
        -> classify_static_or_dynamic
        -> identify_body_part
        -> identify_features
        -> map_meaning
        -> integrate;

    new_sensor:
        validate
        -> classify
        -> identify_observation_type
        -> identify_quality_properties
        -> integrate;

    new_model_evidence:
        identify_model
        -> record_evaluation
        -> update_class_confidence
        -> update_error_rates
        -> integrate;

    recognition_revision:
        update_features
        -> update_model
        -> update_thresholds
        -> recompute_confidence
        -> regenerate_flow_hash;

    conflict:
        preserve_provenance
        -> compare_recognition_evidence
        -> adjust_confidence
        -> flag_review;
}

# ---------- FLOW HASH ----------
FLOW_HASH_INPUT :=
    DOMAIN
    + SUBDOMAIN
    + ONTOLOGY
    + GestureEntity
    + GestureType
    + SensorType
    + RecognitionState
    + GestureFeature
    + GestureModel
    + RELATIONSHIPS
    + SEMANTIC_REASONING
    + SEMANTIC_EQUIVALENCE
    + UPDATE_MODEL;

FLOW_HASH :=
    HASH(FLOW_HASH_INPUT);

# ---------- TERMINAL ----------
GestureRecognitionOntology
    -> ObserveUserMovement
    -> CaptureSensorData
    -> PreprocessObservation
    -> DetectCandidateGesture
    -> SegmentGesture
    -> ExtractGestureFeatures
    -> ClassifyGesture
    -> EvaluateRecognitionConfidence
    -> ApplyInteractionContext
    -> MapGestureToMeaningOrCommand
    -> ProvideUserFeedback
    -> EvaluateRecognitionQuality
    -> MaintainAccessibleGestureInteraction;

END FLOW