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 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 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 Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 60 ◽  
Author(s):  
Irvin Hussein Lopez-Nava ◽  
Matias Garcia-Constantino ◽  
Jesus Favela
Keyword(s):  
Assisted Living ◽  
Inertial Sensor ◽  
Ambient Assisted Living ◽  
Human Gait ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Acceleration Data ◽  
Vector Machines ◽  
Young Subjects ◽  
Physical Spaces

Activity recognition is an important task in many fields, such as ambient intelligence, pervasive healthcare, and surveillance. In particular, the recognition of human gait can be useful to identify the characteristics of the places or physical spaces, such as whether the person is walking on level ground or walking down stairs in which people move. For example, ascending or descending stairs can be a risky activity for older adults because of a possible fall, which can have more severe consequences than if it occurred on a flat surface. While portable and wearable devices have been widely used to detect Activities of Daily Living (ADLs), few research works in the literature have focused on characterizing only actions of human gait. In the present study, a method for recognizing gait activities using acceleration data obtained from a smartphone and a wearable inertial sensor placed on the ankle of people is introduced. The acceleration signals were segmented based on the automatic detection of strides, also called gait cycles. Subsequently, a feature vector of the segmented signals was extracted, which was used to train four classifiers using the Naive Bayes, C4.5, Support Vector Machines, and K-Nearest Neighbors algorithms. Data was collected from seven young subjects who performed five gait activities: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The results demonstrate the viability of using the proposed method and technologies in ambient assisted living contexts.

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