Multi-Resident human behaviour identification in ambient assisted living environments

Date

2014-11-12

Author

Alemdar, Hande

Metadata

Show full item record

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

Item Usage Stats

178
views

0
downloads

Multimodal interactions in ambient assisted living environments require human behaviour to be recognized and monitored automatically. The complex nature of human behaviour makes it extremely difficult to infer and adapt to, especially in multi-resident environments. This proposed research aims to contribute to the multimodal interaction community by (i) providing publicly available, naturalistic, rich and annotated datasets for human behaviour modeling, (ii) introducing evaluation methods of several inference methods from a behaviour monitoring perspective, (iii) developing novel methods for recognizing individual behaviour in multi-resident smart environments without assuming any person identification, (iv) proposing methods for mitigating the scalability issues by using transfer, active, and semi-supervised learning techniques. The proposed studies will address both practical and methodological aspects of human behaviour recognition in smart interactive environments.

URI

https://hdl.handle.net/11511/35850

DOI

https://doi.org/10.1145/2663204.2666286

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Multimodal Wireless Sensor Network-Based Ambient Assisted Living in Real Homes with Multiple Residents Tunca, Can; Alemdar, Hande; Ertan, Halil; Incel, Ozlem Durmaz; Ersoy, Cem (MDPI AG, 2014-06-01) Human activity recognition and behavior monitoring in a home setting using wireless sensor networks (WSNs) provide a great potential for ambient assisted living (AAL) applications, ranging from health and wellbeing monitoring to resource consumption monitoring. However, due to the limitations of the sensor devices, challenges in wireless communication and the challenges in processing large amounts of sensor data in order to recognize complex human activities, WSN-based AAL systems are not effectively integr...
Single and Multiple-Access Channel Capacity in Molecular Nanonetworks Atakan, Baris; Akan, Ozgur B. (2009-10-20) Molecular communication is a new nano-scale communication paradigm that enables nanomachines to communicate with each other by emitting molecules to their surrounding environment. Nanonetworks are also envisioned to be composed of a number of nanomachines with molecular communication capability that are deployed in an environment to share specific molecular information such as odor, flavour, light, or any chemical state. In this paper, using the principles of natural ligand-receptor binding mechanisms in bi...
Online mining of human deep intention by proactive environment changes using deep neural networks Er, Nur Baki; Erkmen, Aydan Müşerref; Department of Electrical and Electronics Engineering (2015) This thesis focuses on surfacing human deep intention, which is known or assumed, in a smart environment that consists of autonomous robotic systems which can interact with the human. Deep intentions are defined as kind of actions that humans would like to behave but pushed deeper in the stack of the intentions in a daily life. The purpose of the designed system is to observe the human in the smart room for a while and to analyze human’s behaviors to offer the optimal set of system behavior to surface a des...
SPEED PERCEPTION IN MULTISENSORY PROFILES: WHAT IS THE EFFECT OF ATTENTION? Kavaklıoğlu, Efsun; Özge, Umut; Kafalıgönül, Hulusi; Department of Bioinformatics (2022-2-7) Multisensory processing and crossmodal interactions in the temporal domain are crucial for survival in a dynamic environment. Temporal ventriloquism illusion demonstrates the importance of the crossmodal interactions in the temporal domain and the influences of the auditory signals (e.g., auditory time intervals) on visual perception. Attention is another mechanism playing a critical role in sensory processing, and it allows us to prioritize relevant information in the visual field. Previous studies have sh...
Continual Learning for Affective Robotics: Why, What and How? Churamani, Nikhil; Kalkan, Sinan; Gunes, Hatice (2020-01-01) Creating and sustaining closed-loop dynamic and social interactions with humans require robots to continually adapt towards their users' behaviours, their affective states and moods while keeping them engaged in the task they are performing. Analysing, understanding and appropriately responding to human nonverbal behaviour and affective states are the central objectives of affective robotics research. Conventional machine learning approaches do not scale well to the dynamic nature of such real-world interac...

Citation Formats

H. Alemdar, “Multi-Resident human behaviour identification in ambient assisted living environments,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35850.