Human activity recognition using neural networks
Abstract
This paper presents research made for independent daily life assistance of elderly or persons with disabilities using IoT technologies. Our scope is to develop a system that allows living for as long as possible in familiar environment. This will be possible by wider spread of assistive technologies and the internet of things (IoT). We aim to bring together latest achievements in domain of Internet of things and assistive technologies in order to develop a complex assistive system with adaptive capability and learning behavior. We can use IoT technologies to monitor in real time the state of a patient or to get sensitive data in order to be subsequently analyzed for a medical diagnosis. We present the state of our work related to the development of an assistive assembly consisting of a smart and assistive environment, a human activity and health monitoring system, an assistive and telepresence robot, together with the related components and cloud services.
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Existing HAR solutions often rely on fully supervised techniques.
“Existing HAR solutions often rely on fully supervised techniques [15, 25].”
From Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion (2021) · cites this paper as [15]
Fully supervised HAR approaches are generally robust and effective in terms of performance.
“While these approaches are generally robust and effective in terms of performance [15], they face a number of challenges and limitations.”
From Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion (2021) · cites this paper as [15]
Cites
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Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion
Rahimi Taghanaki, S., Rainbow, M. J., & Etemad, A. (2021) · 2021 International Symposium on Wearable Computers · doi:10.1145/3460421.3480417 · Cited by 22 · see citing works
“While these approaches are generally robust and effective in terms of performance [15], they face a number of challenges and limitations.”
Background
- Authors
- Oniga, S., & Suto, J.
- Affiliations
- University of Debrecen (HU)
- ORCID
- Oniga, Stefan
- Venue
- Proceedings of the 2014 15th International Carpathian Control Conference (ICCC)
- Publisher
- IEEE
- Published
- 2014-05
- Pages
- 403-406
- OpenAlex
- W2025823130
- Cited by
- 74
- References
- 22
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 6 · 2023: 9 · 2022: 7 · 2021: 9 · 2020: 8 · 2019: 9 · 2018: 8 · 2017: 7 · 2016: 4 · 2015: 5
- FWCI
- 2.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Carpathian Control Conference (publishedVersion)
- University of Debrecen Electronic Archive (University of Debrecen) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Social Sciences › Demography › Technology Use by Older Adults
- Keywords
- human activity recognition, assistive technology, elderly care, people with disabilities, Internet of Things, health monitoring, telepresence robots, smart environments, cloud services, independent living
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