Abstract
Multi-modal context-aware systems can provide user-adaptive services, but it requires complicated recognition models with larger resources. The limitations to build optimal models and infer the context efficiently make it difficult to develop practical context-aware systems. We developed a multi-modal context-aware system with various wearable sensors including accelerometers, gyroscopes, physiological sensors, and data gloves. The system used probabilistic models to handle the uncertain and noisy time-series sensor data. In order to construct the efficient probabilistic models, this paper uses an evolutionary algorithm to model structure and EM algorithm to determine parameters. The trained models are selectively inferred based on a semantic network which describes the semantic relations of the contexts and sensors. Experiments with the real data collected show the usefulness of the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 695-707 |
| Number of pages | 13 |
| Journal | Pattern Analysis and Applications |
| Volume | 17 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2014 Oct 16 |
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence
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Recognizing multi-modal sensor signals using evolutionary learning of dynamic Bayesian networks. / Lee, Young Seol; Cho, Sung-Bae.
In: Pattern Analysis and Applications, Vol. 17, No. 4, 16.10.2014, p. 695-707.Research output: Contribution to journal › Article
TY - JOUR
T1 - Recognizing multi-modal sensor signals using evolutionary learning of dynamic Bayesian networks
AU - Lee, Young Seol
AU - Cho, Sung-Bae
PY - 2014/10/16
Y1 - 2014/10/16
N2 - Multi-modal context-aware systems can provide user-adaptive services, but it requires complicated recognition models with larger resources. The limitations to build optimal models and infer the context efficiently make it difficult to develop practical context-aware systems. We developed a multi-modal context-aware system with various wearable sensors including accelerometers, gyroscopes, physiological sensors, and data gloves. The system used probabilistic models to handle the uncertain and noisy time-series sensor data. In order to construct the efficient probabilistic models, this paper uses an evolutionary algorithm to model structure and EM algorithm to determine parameters. The trained models are selectively inferred based on a semantic network which describes the semantic relations of the contexts and sensors. Experiments with the real data collected show the usefulness of the proposed method.
AB - Multi-modal context-aware systems can provide user-adaptive services, but it requires complicated recognition models with larger resources. The limitations to build optimal models and infer the context efficiently make it difficult to develop practical context-aware systems. We developed a multi-modal context-aware system with various wearable sensors including accelerometers, gyroscopes, physiological sensors, and data gloves. The system used probabilistic models to handle the uncertain and noisy time-series sensor data. In order to construct the efficient probabilistic models, this paper uses an evolutionary algorithm to model structure and EM algorithm to determine parameters. The trained models are selectively inferred based on a semantic network which describes the semantic relations of the contexts and sensors. Experiments with the real data collected show the usefulness of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=84919464484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84919464484&partnerID=8YFLogxK
U2 - 10.1007/s10044-012-0300-z
DO - 10.1007/s10044-012-0300-z
M3 - Article
AN - SCOPUS:84919464484
VL - 17
SP - 695
EP - 707
JO - Pattern Analysis and Applications
JF - Pattern Analysis and Applications
SN - 1433-7541
IS - 4
ER -