This paper proposes a novel deep learning method for extraction of the conjunctive information that describes the relationship between signals in multi-sensor systems to enhance the performance of the given classification task. The signals obtained from different sensors included in the multi-sensor systems are closely related. Handcrafted metrics have been used to extract the relationship between the signals in some work, which is hardly optimal for the given task. Our proposed method learns the pair-wise relationship from data to maximize the performance of the given task, which is fully data-driven, multi-aspect, and target-oriented. We demonstrate the effectiveness of the proposed method on a toy example and two real-world problems, i.e., activity recognition using accelerometer signals and emotional video classification using brain signals.
|Title of host publication||ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings|
|Editors||Giuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senen Barro, Alberto Bugarin, Jerome Lang|
|Publisher||IOS Press BV|
|Number of pages||8|
|Publication status||Published - 2020 Aug 24|
|Event||24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Santiago de Compostela, Online, Spain|
Duration: 2020 Aug 29 → 2020 Sept 8
|Name||Frontiers in Artificial Intelligence and Applications|
|Conference||24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020|
|City||Santiago de Compostela, Online|
|Period||20/8/29 → 20/9/8|
Bibliographical noteFunding Information:
This work was supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Ministry of Science and ICT (MSIT) (R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding).
© 2020 The authors and IOS Press.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence