Computational state space models for activity and intention recognition. A feasibility study.
BACKGROUND: Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algor...
Main Authors: | Frank Krüger, Martin Nyolt, Kristina Yordanova, Albert Hein, Thomas Kirste |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2014-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC4220990?pdf=render |
Similar Items
-
Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors
by: Florian Grützmacher, et al.
Published: (2018-09-01) -
A Decentralized Partially Observable Decision Model for Recognizing the Multiagent Goal in Simulation Systems
by: Shiguang Yue, et al.
Published: (2016-01-01) -
Creating and Exploring Semantic Annotation for Behaviour Analysis
by: Kristina Yordanova, et al.
Published: (2018-08-01) -
Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring
by: Kristina Yordanova, et al.
Published: (2019-02-01) -
Computational models for intent recognition in robotic systems
by: Persiani, Michele
Published: (2020)