Motion detector placement optimization in smart homes for inhabitant location tracking

Fanti, Maria Pia; Roccotelli, Michele; Lesage, Jean-Jacques; Faraut, Grégory

doi:10.1109/etfa.2016.7733558

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…In particular, the grid scales are defined in a 2-D space ( , ) and it is possible to choose two different values of and for the two dimensions that are ( , ) and ( , ), respectively: and are the scales for the dimension, and are scales for the dimension, with , , , ∈ ℕ + . It is remarked that the discretization of zones can be performed according to standard techniques [2].…”

Section: Smart Placement Methodologymentioning

confidence: 99%

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Smart placement of motion sensors in a home environment

Fanti

Roccotelli

Faraut

et al. 2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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This paper deals with the smart placement of motion sensors in smart homes for Ambient Assisted Living, by considering the sensor technology and cost and respecting specific coverage requirements. The core of the proposed methodology is a decision module that can optimize the sensors placement according to different objectives. More precisely, the main objective is the minimization of costs of the deployed sensors. Moreover, the second objective can be the maximization of the overlapping in order to find a robust solution or the minimization of the overlapping of the detection areas in order to improve the inhabitant localization. A case study demonstrates the effectiveness of the proposed strategy on sensors placement in a domestic environment.

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Section: Smart Placement Methodologymentioning

confidence: 99%

“…With respect to [1] and [2] the authors improve the model of the decision-making system by providing an updated version of the database module that includes new sensors technological aspects. In addition, the optimization phase has also been improved.…”

Section: A Paper Contributionmentioning

confidence: 99%

Smart placement of motion sensors in a home environment

Fanti

Roccotelli

Faraut

et al. 2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Self Cite

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“…A decision-making tool to help experts to do the best choice for sensors deployment is under development to minimize costs and the overlapping of motion sensor detections in a home environment [21], [22]. Their method represents the motion sensors in a space as binary grids and then integer linear programming techniques are applied to optimize their placement, as applied in [9].…”

Section: Related Workmentioning

confidence: 99%

Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

2023

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This work presents a novel method for motion sensor placement within smart homes. Using recordings from 3D depth cameras within six real homes, clusters are created with the resident's tracked location. The resulting clusters identify the possible position of a sensor and its field of view. By using a sequence of clusters as input to a Recurrent Neural Network, we evaluate our method on the task of activity recognition and prediction. These results are compared to using sensor events as input sequence, from motion sensors that were installed empirically in the same homes. Different clustering methods are investigated and all outperform the installed motion sensors, achieving a significant increase of prediction accuracy and F1-score.

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“…Another factor to be considered when selecting the sensors is the room layout and size and the sensor's placement. Finding the optimal placement for the sensors is a significant factor to consider when implementing and adopting the ambient sensors in the room of PWAD [39].…”

Section: Proposed Frameworkmentioning

confidence: 99%

Assessing Alzheimer’s Therapeutic Environment Digitally through a People with Alzheimer’s’ Disease Perspective: A Computation-Based Approach Framework

Elnimr

2023

Buildings

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People with Alzheimer’s disease (PWAD) are impacted by their surroundings, and their performance improves in therapeutic environments designed to meet their specific individual needs, are adjustable in terms of their health status, and are created to accommodate their abilities. A literature review of the field revealed scarce knowledge in using a combination of building information modeling (BIM) and the Internet of Things (IoT) for the purpose of understanding the daily needs and self-orientation ability of PWAD, as well as the architectural barriers they face in their rooms in long-term healthcare centers. In this context, this paper proposes a framework based on computational design approaches to assess the existing therapeutic environment for PWAD using BIM–IoT sensors-based monitoring. The proposed framework used the user experience design concept (UX) and the design thinking framework to evaluate the resident rooms of PWAD. The UX design concept and the design thinking framework core allow for the adoption of user-centered methods to provide a comprehensive image of the issues that affect PWAD in their therapeutic environment. The proposed framework-structured approach will enable healthcare architects/designers to (1) digitalize old building architecture plans using BIM; (2) strategize IoT sensor selection; (3) recognize the activities performed by PWAD and detect any anomaly; and (4) integrate IoT real-time data into the BIM system. The proposed framework supports three types of professionals: (1) architects in decision-making processes, (2) researchers in collecting/analyzing accurate data for shadow observations, and (3) neurologists in following up the health statuses of PWAD.

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Motion detector placement optimization in smart homes for inhabitant location tracking

Cited by 5 publications

References 15 publications

Smart placement of motion sensors in a home environment

Smart placement of motion sensors in a home environment

Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

Assessing Alzheimer’s Therapeutic Environment Digitally through a People with Alzheimer’s’ Disease Perspective: A Computation-Based Approach Framework

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