Active learning with uncertainty sampling for large scale activity recognition in smart homes

Download

index.pdf

Date

2017-01-01

Author

Alemdar, Hande
Ersoy, Cem

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

140
views

149
downloads

One of the major problems faced by automated human activity recognition systems is the scalability. Since the probabilistic models employed in activity recognition require labeled data sets for adapting themselves to different users and environments, redeploying these systems in different settings becomes a bottleneck. In order to handle this problem in a cost effective and user friendly way, uncertainty sampling based active learning method is proposed. With active learning, it is possible to reduce the annotation effort by selecting only the most informative data points for annotation. In this paper, three different measures of uncertainty have been used for selecting the most informative data points and their performance have been evaluated by using real world data sets. It has been shown that the annotation effort can be reduced by a factor of two to four, depending on the house and resident settings in an active learning setup.

Subject Keywords

Activity recognition, Active learning, Uncertainty sampling

URI

https://hdl.handle.net/11511/35837

Journal

JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS

DOI

https://doi.org/10.3233/ais-170427

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Lifelong learning and personalization in long-term human-robot interaction (LEAP-HRI) Irfan, Bahar; Ramachandran, Aditi; Spaulding, Samuel; Kalkan, Sinan; Parisi, German I.; Gunes, Hatice (2021-03-08) While most of the research in Human-Robot Interaction (HRI) focuses on short-term interactions, long-term interactions require bolder developments and a substantial amount of resources, especially if the robots are deployed in the wild. Robots need to incrementally learn new concepts or abilities in a lifelong fashion to adapt their behaviors within new situations and personalize their interactions with users to maintain their interest and engagement. The "Lifelong Learning and Personalization in Long-Term ...
Flexible querying in an intelligent object-oriented database environment Koyuncu, M; Yazıcı, Adnan; George, R (2000-10-28) Many new-generation database applications demand intelligent information management necessitating efficient interactions between database gr. knowledge bases and the users. In this study we discuss evaluation of imprecise queries in an intelligent object-oriented database environment, IFOOD. A flexible query evaluation mechanism, capable of handling different data types including complex and imprecise data and knowledge is presented and key language issues are addressed.
Multiple human trajectory prediction and cooperative navigation modeling in crowded scenes Hacinecipoglu, Akif; Konukseven, Erhan İlhan; Koku, Ahmet Buğra (Springer Science and Business Media LLC, 2020-07-01) As mobile robots start operating in environments crowded with humans, human-aware navigation is required to make these robots navigate safely, efficiently and in socially compliant manner. People navigate in an interactive and cooperative fashion so that, they are able to find their path to a destination even if there is no clear route leading to it. There are significant efforts to solve this problem for mobile robots; however, they are not scalable to high human density and learning based approaches depen...
Efficient computation of strong partial transitive-closures Toroslu, İsmail Hakkı (null; 1993-01-01) The development of efficient algorithms to process the different forms of the transitive-closure (TC) queries within the context of large database systems has recently attracted a large volume of research efforts. In this paper, we present a new algorithm suitable for processing one of these forms, the so called strong partially-instantiated, in which one of the query's argument is instantiated to a set of constants and the processing of which yields a set of tuples that draw their values form both of the q...
Evaluation and selection of case tools: a methodology and a case study Okşar, Koray; Okşar, Koray; Department of Information Systems (2010) Today’s Computer Aided Software Engineering (CASE) technology covers nearly all activities in software development ranging from requirement analysis to deployment.Organizations are evaluating CASE tool solutions to automate or ease their processes. While reducing human errors, these tools also increase control, visibility and auditability of the processes. However, to achieve these benefits, the right tool or tools should be selected for usage in the intended processes. This is not an easy task when the vas...

Citation Formats

H. Alemdar and C. Ersoy, “Active learning with uncertainty sampling for large scale activity recognition in smart homes,” JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, pp. 209–223, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35837.