Online Guest Detection in a Smart Home Using Pervasive Sensors and Probabilistic Reasoning

@article{Renoux2018OnlineGD,
  title={Online Guest Detection in a Smart Home Using Pervasive Sensors and Probabilistic Reasoning},
  author={Jennifer Renoux and Uwe K{\"o}ckemann and Amy Loutfi},
  journal={ArXiv},
  year={2018},
  volume={abs/2003.06347}
}
Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system’s ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at… 

On the people counting problem in smart homes: undirected graphs and theoretical lower-bounds

  • A. GiarettaA. Loutfi
  • Computer Science, Mathematics
    Journal of Ambient Intelligence and Humanized Computing
  • 2021
A graph-based technique is proposed that allows to map a smart home to an undirected graph G and discover the lower-bound of certainly countable people, also defined as certain count, and it is proved that every independent set of n vertices of an und directed graph G represents a minimum count of n people.

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References

SHOWING 1-10 OF 18 REFERENCES

Recognizing multi-user activities using wearable sensors in a smart home

Multioccupant Activity Recognition in Pervasive Smart Home Environments

An overview of existing approaches and current practices for activity recognition in multioccupant smart homes is provided, which presents the latest developments and highlights the open issues in this field.

ARAS human activity datasets in multiple homes with multiple residents

The details of ARAS (Activity Recognition with Ambient Sensing) human activity recognition datasets that are collected from two real houses with multiple residents during two months, which contain the ground truth labels for 27 different activities are presented.

An Ontology-based Context-aware System for Smart Homes: E-care@home

This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations.

Ontology-based sensor fusion activity recognition

This paper investigates the fusion of wearable and ambient sensors for recognizing activities of daily living in a smart home setting using ontology. The proposed approach exploits the advantages of

Context Recognition in Multiple Occupants Situations: Detecting the Number of Agents in a Smart Home Environment with Simple Sensors

Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count ...

Pedestrian counting with grid-based binary sensors based on Monte Carlo method

Evaluation results show that in the field whose width is 8 [m] the relative error in the proposed method is the smallest by using 2×8 binary sensors.

A Unified Framework for Multi-target Tracking and Collective Activity Recognition

This work presents a coherent, discriminative framework for simultaneously tracking multiple people and estimating their collective activities and proposes an algorithm for solving this otherwise intractable joint inference problem by combining belief propagation with a version of the branch and bound algorithm equipped with integer programming.

Literature survey for people counting and human detection

This literature survey discusses some of the existing methods for people counting and their performance and proposes Foreground Extraction and Expectation Maximization based methods, which provides a better accurate solution.

The Application of Hidden Markov Models in Speech Recognition

The aim of this review is first to present the core architecture of a HMM-based LVCSR system and then to describe the various refinements which are needed to achieve state-of-the-art performance.