Abstract
Original language | English |
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Pages (from-to) | 1-25 |
Number of pages | 25 |
Journal | Neurocomputing |
DOIs | |
Publication status | Published - 2017 |
Fingerprint
Keywords
- Sensor replacement
- Sensor selection
- Sensor description
- Wearable sensor platform
- Ontology
- EWI-27262
- IR-104356
- Activity Recognition
- Magnetic and inertial measurement unit
Cite this
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MIMU-Wear: ontology-based sensor selection for real-world wearable activity recognition. / Villalonga, Claudia; Pomares, Hector; Rojas, Ignacio; Banos Legran, Oresti .
In: Neurocomputing, 2017, p. 1-25.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - MIMU-Wear: ontology-based sensor selection for real-world wearable activity recognition
AU - Villalonga, Claudia
AU - Pomares, Hector
AU - Rojas, Ignacio
AU - Banos Legran, Oresti
PY - 2017
Y1 - 2017
N2 - An enormous effort has been made during the recent years towards the recognition of human activity based on wearable sensors. Despite the wide variety of proposed systems, most existing solutions have in common to solely operate on predefined settings and constrained sensor setups. Real-world activity recognition applications and users rather demand more flexible sensor configurations dealing with potential adverse situations such as defective or missing sensors. In order to provide interoperability and reconfigurability, heterogeneous sensors used in wearable activity recognition systems must be fairly abstracted from the actual underlying network infrastructure. This work presents MIMU-Wear, an extensible ontology that comprehensively describes wearable sensor platforms consisting of mainstream magnetic and inertial measurement units (MIMUs). MIMU-Wear describes the capabilities of MIMUs such as their measurement properties and the characteristics of wearable sensor platforms including their on-body location. A novel method to select an adequate replacement for a given anomalous or nonrecoverable sensor is also presented in this work. The proposed sensor selection method is based on the MIMU-Wear Ontology and builds on a set of heuristic rules to infer the candidate replacement sensors in different conditions. Then, queries are iteratively posed to select the most appropriate MIMU sensor for the replacement of the defective one. An exemplary application scenario is used to illustrate some of the potential of MIMU-Wear for supporting seamless operation of wearable activity recognition systems.
AB - An enormous effort has been made during the recent years towards the recognition of human activity based on wearable sensors. Despite the wide variety of proposed systems, most existing solutions have in common to solely operate on predefined settings and constrained sensor setups. Real-world activity recognition applications and users rather demand more flexible sensor configurations dealing with potential adverse situations such as defective or missing sensors. In order to provide interoperability and reconfigurability, heterogeneous sensors used in wearable activity recognition systems must be fairly abstracted from the actual underlying network infrastructure. This work presents MIMU-Wear, an extensible ontology that comprehensively describes wearable sensor platforms consisting of mainstream magnetic and inertial measurement units (MIMUs). MIMU-Wear describes the capabilities of MIMUs such as their measurement properties and the characteristics of wearable sensor platforms including their on-body location. A novel method to select an adequate replacement for a given anomalous or nonrecoverable sensor is also presented in this work. The proposed sensor selection method is based on the MIMU-Wear Ontology and builds on a set of heuristic rules to infer the candidate replacement sensors in different conditions. Then, queries are iteratively posed to select the most appropriate MIMU sensor for the replacement of the defective one. An exemplary application scenario is used to illustrate some of the potential of MIMU-Wear for supporting seamless operation of wearable activity recognition systems.
KW - Sensor replacement
KW - Sensor selection
KW - Sensor description
KW - Wearable sensor platform
KW - Ontology
KW - EWI-27262
KW - IR-104356
KW - Activity Recognition
KW - Magnetic and inertial measurement unit
U2 - 10.1016/j.neucom.2016.09.125
DO - 10.1016/j.neucom.2016.09.125
M3 - Article
SP - 1
EP - 25
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -