Special Issue "Multisensor and Multimodal Datasets in Intelligent Home for Context-Awareness, Human Home Interaction and Dialogue"
Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 3799
Interests: Multisource Context-Aware Activity and Situation Recognition; Knowledge-based Decision Support System; Natural Language Generation; Multisource Automatic Speech Recognition
Interests: activity and intention recognition; human behavior models; knowledge elicitation; natural language processing; automatic extraction of behavior models from textual sources
Special Issues, Collections and Topics in MDPI journals
Special Issue in Informatics: Sensor-Based Activity Recognition and Interaction
Special Issue in Sensors: Sensor-Based Activity Recognition and Interaction
Special Issue in Sensors: Selected papers from the 9th EAI International Conference on Wireless Mobile Communication and Healthcare (MobiHealth 2020)
Topics: Methods for Data Labelling for Intelligent Systems
Interests: Ubiquitous computing system; Social information system
Interests: activity recognition; data visualisation; social and physical sensors
Special Issues, Collections and Topics in MDPI journals
Intelligent home systems rely on sensor information and models of the underlying user behaviour to reason about the needs of the users and assist them in their everyday activities. With the continued shift from knowledge-based approaches to methods relying on machine learning techniques, the need of large-scale datasets of high quality increases. Datasets are essential both in designing and training the system to recognise and reason about the situation, either through the definition of a suitable situation model in knowledge-driven applications or through the preparation of training data for learning tasks in data-driven models. Hence, the quality of datasets can have a significant impact on the performance of the derived systems. Datasets are also vital for validating and quantifying the performance of applications. Furthermore, well documented datasets with reliable annotation ensure the reproducibility of research results. While datasets and the process of collecting and annotating them are highly developed in some fields, such as natural language processing (NLP) or computer vision, in smart homes, this data collection process is less systematic. This is due to several reasons:
- There is a large diversity of sensors and applications (highly variable input/output);
- The smart home domain is still a developing research field as opposed to some mature tasks such as in NLP;
- It is difficult to acquire ground truth in smart homes without jeopardizing the ecological aspect of the data being collected;
- There are no established roadmaps and standards for annotating data for smart homes, which results in datasets with different annotation granularity and structure, making them difficult to re-use in other applications/environments.
This call encourages submissions reporting methodological or experimental studies about ground truth labelled multisensor corpus collections. In particular, ecological corpus including several modalities (home automation sensors, wearable sensors, microphones, video cameras) and several dwellers are very welcome. All submissions should contain a section about the ethical positioning of the study/project. Further, when relevant, authors reporting a corpus collection are encouraged to make it available to the community and register it through a DOI. We also look forward to submissions which provide guidelines for sensor data collection and annotation or which empirically analyse existing or novel sensor datasets especially in the context of transfer learning.
The topics of interest include but are not limited to:
- Methods and intelligent tools for ecological corpus collection in intelligent or the general houses;
- Multimodal corpora (microphones, video cameras and any other sensors) made publicly available to the community;
- Processes of and best practices in collecting ecological user data;
- Improving and evaluating the quality of annotations;
- Ethical and privacy issues in data collection in home;
- Overview of currently available corpora in smart home;
- Synthetic data generation for improving models learning;
- Machine learning techniques to re-use mismatched data.
Dr. François Portet
Dr. Kristina Yordanova
Dr. Hirohiko Suwa
Dr. Emma Tonkin
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