A Context Driven Indoor Localization Framework for Assisted Living in Smart Homes

2020 ◽  
pp. 387-400
Author(s):  
Nirmalya Thakur ◽  
Chia Y. Han
Keyword(s):  
Assisted Living ◽  
Indoor Localization ◽  
Smart Homes

scholarly journals Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 114
Author(s):  
Nirmalya Thakur ◽  
Chia Y. Han
Keyword(s):  
Assisted Living ◽  
Indoor Localization ◽  
Mean Squared Error ◽  
Specific Activity ◽  
Ambient Assisted Living ◽  
Smart Homes ◽  
Support Vector ◽  
User Interactions ◽  
Indoor Location ◽  
Spatial Coordinates

This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user’s indoor location in a specific ‘activity-based zone’ during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the ‘zone-based’ indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user’s indoor position that outperforms all similar works in this field, as per the associated root mean squared error—one of the performance evaluation metrics in ISO/IEC18305:2016—an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.

scholarly journals From Activity Recognition to Intention Recognition for Assisted Living Within Smart Homes

2017 ◽  
Vol 47 (3) ◽  
pp. 368-379 ◽  
Author(s):  
Joseph Rafferty ◽  
Chris D. Nugent ◽  
Jun Liu ◽  
Liming Chen
Keyword(s):  
Activity Recognition ◽  
Assisted Living ◽  
Smart Homes ◽  
Intention Recognition

scholarly journals Older Adults’ Loneliness, Social Isolation, and Physical Information and Communication Technology in the Era of Ambient Assisted Living: A Systematic Literature Review (Preprint)

2021 ◽  
Author(s):  
Rita Latikka ◽  
Rosana Rubio-Hernández ◽  
Elena Simona Lohan ◽  
Juho Rantala ◽  
Fernando Nieto Fernández ◽  
...  
Keyword(s):  
Older Adults ◽  
Literature Review ◽  
Social Isolation ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Systematic Literature Review ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Smart Homes ◽  
Information And Communication

BACKGROUND Loneliness and social isolation can have severe effects on human health and well-being. Partial solutions to combat these circumstances in demographically aging societies have been sought from the field of information and communication technology (ICT). OBJECTIVE This systematic literature review investigates the research conducted on older adults’ loneliness and social isolation, and physical ICTs, namely robots, wearables, and smart homes, in the era of ambient assisted living (AAL). The aim is to gain insight into how technology can help overcome loneliness and social isolation other than by fostering social communication with people and what the main open-ended challenges according to the reviewed studies are. METHODS The data were collected from 7 bibliographic databases. A preliminary search resulted in 1271 entries that were screened based on predefined inclusion criteria. The characteristics of the selected studies were coded, and the results were summarized to answer our research questions. RESULTS The final data set consisted of 23 empirical studies. We found out that ICT solutions such as smart homes can help detect and predict loneliness and social isolation, and technologies such as robotic pets and some other social robots can help alleviate loneliness to some extent. The main open-ended challenges across studies relate to the need for more robust study samples and study designs. Further, the reviewed studies report technology- and topic-specific open-ended challenges. CONCLUSIONS Technology can help assess older adults’ loneliness and social isolation, and alleviate loneliness without direct interaction with other people. The results are highly relevant in the COVID-19 era, where various social restrictions have been introduced all over the world, and the amount of research literature in this regard has increased recently.

scholarly journals Semantic Smart Homes: Towards Knowledge Rich Assisted Living Environments

2009 ◽  
pp. 279-296 ◽  
Author(s):  
Liming Chen ◽  
Chris Nugent ◽  
Maurice Mulvenna ◽  
Dewar Finlay ◽  
Xin Hong
Keyword(s):  
Assisted Living ◽  
Smart Homes ◽  
Living Environments

scholarly journals Pervasive Activity Logging for Indoor Localization in Smart Homes

2021 ◽  
Author(s):  
Nirmalya Thakur ◽  
Chia Y. Han
Keyword(s):  
Indoor Localization ◽  
Smart Homes

scholarly journals Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1401
Author(s):  
Haq Nawaz ◽  
Ahsen Tahir ◽  
Nauman Ahmed ◽  
Ubaid U. Fayyaz ◽  
Tayyeb Mahmood ◽  
...  
Keyword(s):  
Low Power ◽  
Indoor Localization ◽  
Low Cost ◽  
Smart Homes ◽  
Indoor Positioning ◽  
High Accuracy ◽  
Positioning System ◽  
Telemetry Data ◽  
Ultra Low Power ◽  
Indoor Positioning System

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

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