scholarly journals Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments

2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Nirmalya Thakur ◽  
Chia Y. Han
Keyword(s):  
Assisted Living ◽  
Probabilistic Reasoning ◽  
Indoor Localization ◽  
Traditional Approach ◽  
Ambient Assisted Living ◽  
Training Data ◽  
Gradient Boosting ◽  
User Interactions ◽  
Adaboost Algorithm ◽  
Multiple Users

This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living (AAL), with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-based mathematical approach to model all possible forms of user interactions for any activity arising from user diversity of multiple users in such environments. Second, it presents a system that uses this approach with a machine learning method to model individual user-profiles and user-specific user interactions for detecting the dynamic indoor location of each specific user. Third, to address the need to develop highly accurate indoor localization systems for increased trust, reliance, and seamless user acceptance, the framework introduces a novel methodology where two boosting approaches—Gradient Boosting and the AdaBoost algorithm are integrated and used on a decision tree-based learning model to perform indoor localization. Fourth, the framework introduces two novel functionalities to provide semantic context to indoor localization in terms of detecting each user’s floor-specific location as well as tracking whether a specific user was located inside or outside a given spatial region in a multi-floor-based indoor setting. These novel functionalities of the proposed framework were tested on a dataset of localization-related Big Data collected from 18 different users who navigated in 3 buildings consisting of 5 floors and 254 indoor spatial regions, with an to address the limitation in prior works in this field centered around the lack of training data from diverse users. The results show that this approach of indoor localization for personalized AAL that models each specific user always achieves higher accuracy as compared to the traditional approach of modeling an average user. The results further demonstrate that the proposed framework outperforms all prior works in this field in terms of functionalities, performance characteristics, and operational features.

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 Localino T-shirt: The Real-time Indoor Localization in Ambient Assisted Living Applications

2019 ◽  
Vol 2 (4) ◽  
pp. 196-204
Author(s):  
Stefania Dhamo ◽  
Savvas Vassiliadis ◽  
Ilias Skouras ◽  
Panagiotis Papageorgas ◽  
Ilda Kazani ◽  
...  
Keyword(s):  
Real Time ◽  
Assisted Living ◽  
Indoor Localization ◽  
Ambient Assisted Living ◽  
Mobile User ◽  
Solar Panel ◽  
Home Healthcare ◽  
Sensor Nodes ◽  
Smart Textiles ◽  
Localization Accuracy

In the last decade, smart textiles have become very popular as a concept and have found use in many applications, such as military, electronics, automotive, and medical ones. In the medical area, smart textiles research is focused more on biomonitoring, telemedicine, rehabilitation, sport medicine or home healthcare systems.In this research, the development and localization accuracy measurements of a smart T-shirt are presented, which will be used by elderly people for indoor localization in ambient assisted living applications. The proposed smart T-shirt and the work presented is considered to be applicable in cases of elderly, toddlers or even adults in indoor environments where their continuous real-time localization is critical. This smart T-shirt integrates a localization sensor, namely the Localino sensor, together with a solar panel for energy harvesting when the user is moving outdoors, as well as a battery/power bank that is both connected to the solar panel and the Localino sensor for charging and power supply respectively. Moreover, a mock-up house was deployed, where the Localino platform anchors were deployed at strategic points within the house area. Localino sensor nodes were installed in all the house rooms, from which we obtained the localization accuracy measurements. Furthermore, the localization accuracy was also measured for a selected number of mobile user scenarios, in order to assess the platform accuracy in both static and mobile user cases.Details about the implementation of the T-shirt, the selection and integration of the electronics parts, and the mock-up house, as well as about the localization accuracy measurements results are presented in the paper.

scholarly journals Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Emilio Sansano-Sansano ◽  
Óscar Belmonte-Fernández ◽  
Raúl Montoliu ◽  
Arturo Gascó-Compte ◽  
Antonio Caballer-Miedes
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Indoor Positioning ◽  
Training Data ◽  
Sensor Data ◽  
Smart Devices ◽  
Positioning System ◽  
Positioning Systems ◽  
Future Performance ◽  
Data Scaling

A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies.

A Fuzzy Logic-Based Method for Evaluating AAL Systems

2019 ◽  
Vol 10 (4) ◽  
pp. 59-77 ◽  
Author(s):  
Kara Madjid ◽  
Olfa Lamouchi ◽  
Manolo Dulva Hina ◽  
Amar Ramdane-Cherif
Keyword(s):  
Fuzzy Logic ◽  
Data Quality ◽  
Assisted Living ◽  
Indoor Localization ◽  
Ambient Assisted Living ◽  
Quality Criteria ◽  
Evaluation Procedure ◽  
Quality Model ◽  
Daily Life Activities ◽  
Evaluation Approach

The Ambient Assisted Living (AAL) domain aims to support the daily life activities of elders, patients with chronic conditions, and disabled people. Several AAL platforms have been developed over the last two decades. Hence, there is a need to identify Quality Criteria (QC) and make it well defined in order to achieve the AAL system purposes. To be able to convince all stakeholders including both technologies and end users of AAL systems, high quality must be guaranteed. The goal of this article is to obtain a set of data quality characteristics that would be applicable to AAL system, and have its performance evaluated using sensors' data. To this end, this work uses the ISO/IEC 25012 and ISO/IEC 25010 standards to extract the most relevant criteria that are apt for AAL systems. As a result, an evaluation approach on an indoor localization platform was made, and an evaluation procedure has been established. This is done by first generating a hierarchical data quality model, and have it evaluated using the metrics, based on the sensors data and the concept of fuzzy logic.

Ambient Assisted Living Systems: The Scope of Research and Development

2017 ◽  
Vol 3 (2) ◽  
pp. 21
Author(s):  
Ashish D Patel ◽  
Jigarkumar H. Shah
Keyword(s):  
Assisted Living ◽  
Independent Living ◽  
Intelligent Systems ◽  
New Technologies ◽  
Ambient Assisted Living ◽  
Smart Devices ◽  
Economic Changes ◽  
Aged Population ◽  
The People ◽  
Living Person

The aged population of the world is increasing by a large factor due to the availability of medical and other facilities. As the number grows rapidly, requirements of this segment of age (65+) are increasing rapidly as well as the percentage of aged persons living alone is also increasing with the same rate due to the inevitable socio-economic changes. This situation demands the solution of many problems like loneliness, chronic conditions, social interaction, transportation, day-to-day life and many more for independent living person. A large part of aged population may not be able to interact directly with new technologies. This sought some serious development towards the use of intelligent systems i.e. smart devices which helps the people with their inability to use the available as well future solutions. Ambient Assisted Living (AAL) is the answer to these problems. In this paper, issues related to AAL systems are studied. Study of challenges and limitations of this comparatively new field will help the designers to remove the barriers of AAL systems.

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