An Ontology for Dynamic Sensor Selection in Wearable Activity Recognition

Oresti Banos
Oresti BanosAssociate Professor

A strong effort has been made during the last years in the autonomous and automatic recognition of human activities by using wearable sensor systems. However, the vast majority of proposed solutions are designed for ideal scenarios, where the sensors are pre-defined, well-known and steady. Such systems are of little application in real-world settings, in which the sensors are subject to changes that may lead to a partial or total malfunctioning of the recognition system. This work presents an innovative use of ontologies in activity recognition to support the intelligent and dynamic selection of the best replacement for a given shifted or anomalous wearable sensor. Concretely, an upper ontology describing wearable sensors and their main properties, such as measured magnitude, location and internal characteristics is presented. Moreover, a domain ontology particularly defined to neatly and unequivocally represent the exact placement of the sensor on the human body is presented. These ontological models are particularly aimed at making possible the use of standard wearable activity recognition in data-driven approaches.

An Ontology for Dynamic Sensor Selection
in Wearable Activity Recognition
Claudia Villalonga, Héctor Pomares, Ignacio Rojas
Research Center for Information and Communications Technologies
of the University of Granada (CTIC-UGR)
cvillalonga@correo.ugr.es, {hpomares,irojas}@atc.ugr.es
Oresti Baños
Department of Computer Engineering
Kyung Hee University, Korea
oresti@oslab.khu.ac.kr
A strong effort has been made during the last years in the autonomous and automatic recognition of human activities by using wearable sensor
systems. However, the vast majority of proposed solutions are designed for ideal scenarios, where the sensors are pre-defined, wellknown and
steady. Such systems are of little application in real-world settings, in which the sensors are subject to changes that may lead to a partial or total
malfunctioning of the recognition system. This work presents an innovative use of ontologies in activity recognition to support the intelligent and
dynamic selection of the best replacement for a given shifted or anomalous wearable sensor. Concretely, an upper ontology describing wearable
sensors and their main properties, such as measured magnitude, location and internal characteristics is presented. Moreover, a domain ontology
particularly defined to neatly and unequivocally represent the exact placement of the sensor on the human body is presented. These ontological
models are particularly aimed at making possible the use of standard wearable activity recognition in data-driven approaches.
Keywords: Ontologies, Activity Recognition, Wearable sensors, Sensor selection, Sensor placement, Human anatomy
Abstract
• Wearable activity recognition systems work in closed
environments with pre-defined, well-known and steady sensors.
• In real-world scenarios, sensors might suffer from anomalies such
as failures or deployment changes. Thus, realistic dynamic sensor
setups pose important challenges to the practical use of wearable
activity recognition systems.
• To ensure seamless recognition capabilities, an important
requirement of activity recognition systems is the support of
anomalous sensor replacement. Therefore, mechanisms to
abstract the selection of the most adequate sensors are needed.
• Comprehensive and interoperable descriptions of available sensors
are required so that the best ones could be selected to replace the
anomalous ones.
Human physical self-quantification
for health and wellness
• Semantic description to define the wearable sensor capabilities:
the information the sensor measures, its intrinsic characteristics,
and its location on the human body.
• Ontologies provide full potential for machines to acquire and
interpret the emerging semantics from data. Thus, the selection
and replacement of anomalous sensors can be automatically
executed by a machine.
• Ontologies enable interoperability. Therefore, sensor descriptions
provided for sensors of different vendors are sufficiently rich to be
automatically interpreted by the activity recognition system to
apply methods to select a replacing sensor.
Ontologies for Sensor Selection
SS4RWWAR Upper Ontology
SS4RWWAR Human Body Ontology
The Sensor Selection for Real-World
Wearable Activity Recognition
Ontology (SS4RWWAR)• Personal self-tracking and evaluation
of people’s wellbeing is flourishing as
a key business.
• Self-quantification systems build on
mobile and portable sensor devices
carried on or worn by the users.
• Wearables are capable of measuring
physical and physiological human
characteristics, such as body motion
or vital signs, and used to quantify
physical activity patterns and to
determine abnormal vital conditions.

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An Ontology for Dynamic Sensor Selection in Wearable Activity Recognition

  • 1. An Ontology for Dynamic Sensor Selection in Wearable Activity Recognition Claudia Villalonga, Héctor Pomares, Ignacio Rojas Research Center for Information and Communications Technologies of the University of Granada (CTIC-UGR) cvillalonga@correo.ugr.es, {hpomares,irojas}@atc.ugr.es Oresti Baños Department of Computer Engineering Kyung Hee University, Korea oresti@oslab.khu.ac.kr A strong effort has been made during the last years in the autonomous and automatic recognition of human activities by using wearable sensor systems. However, the vast majority of proposed solutions are designed for ideal scenarios, where the sensors are pre-defined, wellknown and steady. Such systems are of little application in real-world settings, in which the sensors are subject to changes that may lead to a partial or total malfunctioning of the recognition system. This work presents an innovative use of ontologies in activity recognition to support the intelligent and dynamic selection of the best replacement for a given shifted or anomalous wearable sensor. Concretely, an upper ontology describing wearable sensors and their main properties, such as measured magnitude, location and internal characteristics is presented. Moreover, a domain ontology particularly defined to neatly and unequivocally represent the exact placement of the sensor on the human body is presented. These ontological models are particularly aimed at making possible the use of standard wearable activity recognition in data-driven approaches. Keywords: Ontologies, Activity Recognition, Wearable sensors, Sensor selection, Sensor placement, Human anatomy Abstract • Wearable activity recognition systems work in closed environments with pre-defined, well-known and steady sensors. • In real-world scenarios, sensors might suffer from anomalies such as failures or deployment changes. Thus, realistic dynamic sensor setups pose important challenges to the practical use of wearable activity recognition systems. • To ensure seamless recognition capabilities, an important requirement of activity recognition systems is the support of anomalous sensor replacement. Therefore, mechanisms to abstract the selection of the most adequate sensors are needed. • Comprehensive and interoperable descriptions of available sensors are required so that the best ones could be selected to replace the anomalous ones. Human physical self-quantification for health and wellness • Semantic description to define the wearable sensor capabilities: the information the sensor measures, its intrinsic characteristics, and its location on the human body. • Ontologies provide full potential for machines to acquire and interpret the emerging semantics from data. Thus, the selection and replacement of anomalous sensors can be automatically executed by a machine. • Ontologies enable interoperability. Therefore, sensor descriptions provided for sensors of different vendors are sufficiently rich to be automatically interpreted by the activity recognition system to apply methods to select a replacing sensor. Ontologies for Sensor Selection SS4RWWAR Upper Ontology SS4RWWAR Human Body Ontology The Sensor Selection for Real-World Wearable Activity Recognition Ontology (SS4RWWAR)• Personal self-tracking and evaluation of people’s wellbeing is flourishing as a key business. • Self-quantification systems build on mobile and portable sensor devices carried on or worn by the users. • Wearables are capable of measuring physical and physiological human characteristics, such as body motion or vital signs, and used to quantify physical activity patterns and to determine abnormal vital conditions.