sMRT: Multi-Resident Tracking in Smart Homes With Sensor Vectorization

Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sens...

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Published inIEEE transactions on pattern analysis and machine intelligence Vol. 43; no. 8; pp. 2809 - 2821
Main Authors Wang, Tinghui, Cook, Diane J.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial Intelligence
Building automation
Data models
Environment Design
Equipment Design
Floorplans
Hidden Markov models
Humans
Layout
Layouts
Monitoring
Monitoring, Ambulatory
multi-resident tracking
multi-target Bayes filter
Pattern Recognition, Automated
sensor networks
Sensors
Smart buildings
Smart home
Smart homes
Smart houses
Structural health monitoring
time series
Tracking
Online AccessGet full text

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Abstract Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.
AbstractList Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications such as building automation, health monitoring, behavioral intervention and home security. However, when there are multiple residents living in the smart home, the data association between sensor events and residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layout, floor plan and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.
Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.
Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data.
Author Wang, Tinghui
Cook, Diane J.
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Snippet Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation,...
Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications such as building automation,...
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SubjectTerms Algorithms
Artificial Intelligence
Building automation
Data models
Environment Design
Equipment Design
Floorplans
Hidden Markov models
Humans
Layout
Layouts
Monitoring
Monitoring, Ambulatory
multi-resident tracking
multi-target Bayes filter
Pattern Recognition, Automated
sensor networks
Sensors
Smart buildings
Smart home
Smart homes
Smart houses
Structural health monitoring
time series
Tracking
Title sMRT: Multi-Resident Tracking in Smart Homes With Sensor Vectorization
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