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Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct Activities of Daily Living (ADLs), which are crucial for one's sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with ADLs to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADLs while being able to track the indoor location of the elderly in the real world. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an Ambient Assisted Living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real world. Proof of concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparison studies with prior works in this field are also presented to uphold the novelty of this work. The first comparison study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparison study shows that the cost for the development of this system is the least as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a neutral emotion. The other emotions - bad, good, terrible, and great were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish, French, Italian, and other languages. Third, the findings from source tracking showed that Twitter for Android was associated with 35.2% of tweets. It was followed by Twitter Web App, Twitter for iPhone, Twitter for iPad, and other sources. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, and other domains. Finally, to support similar research in this field, we have developed a Twitter dataset that comprises more than 500,000 tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living, 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 the 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. 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.

Over the last decade, exoskeletons have had an extensive impact on different disciplines and application domains such as assisted living, military, healthcare, firefighting, and industries, on account of their diverse and dynamic functionalities to augment human abilities, stamina, potential, and performance in a multitude of ways. In view of this wide-scale applicability and use-cases of exoskeletons, it is crucial to investigate and analyze the public opinion, views, and perspectives towards exoskeletons which would help to interpret the effectiveness of the underlining human-robot, human-machine, and human-technology interactions. The Internet of Everything era of today's living, characterized by people spending more time on the internet than ever before, holds the potential for the investigation of the same by mining and analyzing relevant web behavior, specifically from social media, that can be interpreted to understand public opinion, views, and perspectives towards a topic or set of topics. Therefore, this paper aims to address this research challenge related to exoskeletons by utilizing the potential of web behavior-based Big Data mining in the modern-day Internet of Everything era. As Twitter is one of the most popular social media platforms on a global scale - characterized by both the number of users and the amount of time spent by its users on the platform - this work focused on investigating web behavior on Twitter to interpret the public opinion, views, and perspectives towards exoskeleton technology. A total of approximately 20,000 tweets related to exoskeletons were used to evaluate the effectiveness of the proposed approach. The results presented and discussed uphold the efficacy of the proposed approach to interpret and analyze the public opinion, views, and perspectives towards exoskeletons from the associated tweets., Comment: Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022

COVID-19, a pandemic that the world has not seen in decades, has resulted in presenting a multitude of unprecedented challenges for student learning across the globe. The global surge in COVID-19 cases resulted in several schools, colleges, and universities closing in 2020 in almost all parts of the world and switching to online or remote learning, which has impacted student learning in different ways. This has resulted in both educators and students spending more time on the internet than ever before, which may be broadly summarized as both these groups investigating, learning, and familiarizing themselves with information, tools, applications, and frameworks to adapt to online learning. This paper takes an explorative approach to further investigate and analyze the impact of COVID-19 on such web behavior data related to online learning to interpret the associated interests, challenges, and needs. The study specifically focused on investigating Google Search-based web behavior data as Google is the most popular search engine globally. The impact of COVID-19 related to online learning-based web behavior on Google was studied for the top 20 worst affected countries in terms of the total number of COVID-19 cases, and the findings have been published as an open-access dataset. Furthermore, to interpret the trends in web behavior data related to online learning, the paper discusses a case study in terms of the impact of COVID-19 on the education system of one of these countries., Comment: Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022

The United States of America has been the worst affected country in terms of the number of cases and deaths on account of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or COVID-19, a highly transmissible and pathogenic coronavirus that started spreading globally in late 2019. On account of the surge of infections, accompanied by hospitalizations and deaths due to COVID-19, and lack of a definitive cure at that point, a national emergency was declared in the United States on March 13, 2020. To prevent the rapid spread of the virus, several states declared stay at home and remote work guidelines shortly after this declaration of an emergency. Such guidelines caused schools, colleges, and universities, both private and public, in all the 50-United States to switch to remote or online forms of teaching for a significant period of time. As a result, Google, the most widely used search engine in the United States, experienced a surge in online shopping of remote learning-based software, systems, applications, and gadgets by both educators and students from all the 50-United States, due to both these groups responding to the associated needs and demands related to switching to remote teaching and learning. This paper aims to investigate, analyze, and interpret these trends of Google Shopping related to remote learning that emerged since March 13, 2020, on account of COVID-19 and the subsequent remote learning adoption in almost all schools, colleges, and universities, from all the 50-United States. The study was performed using Google Trends, which helps to track and study Google Shopping-based online activity emerging from different geolocations. The results and discussions show that the highest interest related to Remote Learning-based Google Shopping was recorded from Oregon, which was followed by Illinois, Florida, Texas, California, and the other states., Comment: Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022

The rise of the Internet of Everything lifestyle in the last decade has had a significant impact on the increased emergence and adoption of online learning in almost all countries across the world. E-learning 3.0 is expected to become the norm of learning globally in almost all sectors in the next few years. The pervasiveness of the Semantic Web powered by the Internet of Everything lifestyle is expected to play a huge role towards seamless and faster adoption of the emerging paradigms of E-learning 3.0. Therefore, this paper presents an exploratory study to analyze multimodal components of Semantic Web behavior data to investigate the emergence of online learning in different countries across the world. The work specifically involved investigating relevant web behavior data to interpret the 5 W's and 1 H - Who, What, When Where, Why, and How related to online learning. Based on studying the E-learning Index of 2021, the study was performed for all the countries that are member states of the Organization for Economic Cooperation and Development. The results presented and discussed help to interpret the emergence of online learning in each of these countries in terms of the associated public perceptions, queries, opinions, behaviors, and perspectives. Furthermore, to support research and development in this field, we have published the web behavior-based Big Data related to online learning that was mined for all these 38 countries, in the form of a dataset, which is avail-able at https://dx.doi.org/10.21227/xbvs-0198., Comment: Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022

This framework for human behavior monitoring aims to take a holistic approach to study, track, monitor, and analyze human behavior during activities of daily living (ADLs). The framework consists of two novel functionalities. First, it can perform the semantic analysis of user interactions on the diverse contextual parameters during ADLs to identify a list of distinct behavioral patterns associated with different complex activities. Second, it consists of an intelligent decision-making algorithm that can analyze these behavioral patterns and their relationships with the dynamic contextual and spatial features of the environment to detect any anomalies in user behavior that could constitute an emergency. These functionalities of this interdisciplinary framework were developed by integrating the latest advancements and technologies in human-computer interaction, machine learning, Internet of Things, pattern recognition, and ubiquitous computing. The framework was evaluated on a dataset of ADLs, and the performance accuracies of these two functionalities were found to be 76.71% and 83.87%, respectively. The presented and discussed results uphold the relevance and immense potential of this framework to contribute towards improving the quality of life and assisted living of the aging population in the future of Internet of Things (IoT)-based ubiquitous living environments, e.g., smart homes.

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.

The population of elderly people has been increasing at a rapid rate over the last few decades and their population is expected to further increase in the upcoming future. Their increasing population is associated with their increasing needs due to problems like physical disabilities, cognitive issues, weakened memory and disorganized behavior, that elderly people face with increasing age. To reduce their financial burden on the world economy and to enhance their quality of life, it is essential to develop technology-based solutions that are adaptive, assistive and intelligent in nature. Intelligent Affect Aware Systems that can not only analyze but also predict the behavior of elderly people in the context of their day to day interactions with technology in an IoT-based environment, holds immense potential for serving as a long-term solution for improving the user experience of elderly in smart homes. This work therefore proposes the framework for an Intelligent Affect Aware environment for elderly people that can not only analyze the affective components of their interactions but also predict their likely user experience even before they start engaging in any activity in the given smart home environment. This forecasting of user experience would provide scope for enhancing the same, thereby increasing the assistive and adaptive nature of such intelligent systems. To uphold the efficacy of this proposed framework for improving the quality of life of elderly people in smart homes, it has been tested on three datasets and the results are presented and discussed.

The increasing population of elderly people is associated with the need to meet their increasing requirements and to provide solutions that can improve their quality of life in a smart home. In addition to fear and anxiety towards interfacing with systems; cognitive disabilities, weakened memory, disorganized behavior and even physical limitations are some of the problems that elderly people tend to face with increasing age. The essence of providing technology-based solutions to address these needs of elderly people and to create smart and assisted living spaces for the elderly; lies in developing systems that can adapt by addressing their diversity and can augment their performances in the context of their day to day goals. Therefore, this work proposes a framework for development of a Personalized Intelligent Assistant to help elderly people perform Activities of Daily Living (ADLs) in a smart and connected Internet of Things (IoT) based environment. This Personalized Intelligent Assistant can analyze different tasks performed by the user and recommend activities by considering their daily routine, current affective state and the underlining user experience. To uphold the efficacy of this proposed framework, it has been tested on a couple of datasets for modelling an average user and a specific user respectively. The results presented show that the model achieves a performance accuracy of 73.12% when modelling a specific user, which is considerably higher than its performance while modelling an average user, this upholds the relevance for development and implementation of this proposed framework., Comment: arXiv admin note: text overlap with arXiv:2106.15599

One of the distinct features of this century has been the population of older adults which has been on a constant rise. Elderly people have several needs and requirements due to physical disabilities, cognitive issues, weakened memory and disorganized behavior, that they face with increasing age. The extent of these limitations also differs according to the varying diversities in elderly, which include age, gender, background, experience, skills, knowledge and so on. These varying needs and challenges with increasing age, limits abilities of older adults to perform Activities of Daily Living (ADLs) in an independent manner. To add to it, the shortage of caregivers creates a looming need for technology-based services for elderly people, to assist them in performing their daily routine tasks to sustain their independent living and active aging. To address these needs, this work consists of making three major contributions in this field. First, it provides a rather comprehensive review of assisted living technologies aimed at helping elderly people to perform ADLs. Second, the work discusses the challenges identified through this review, that currently exist in the context of implementation of assisted living services for elderly care in Smart Homes and Smart Cities. Finally, the work also outlines an approach for implementation, extension and integration of the existing works in this field for development of a much-needed framework that can provide personalized assistance and user-centered behavior interventions to elderly as per their varying and ever-changing needs.

This paper makes two scientific contributions to the field of exoskeleton-based action and movement recognition. First, it presents a novel machine learning and pattern recognition-based framework that can detect a wide range of actions and movements - walking, walking upstairs, walking downstairs, sitting, standing, lying, stand to sit, sit to stand, sit to lie, lie to sit, stand to lie, and lie to stand, with an overall accuracy of 82.63%. Second, it presents a comprehensive comparative study of different learning approaches - Random Forest, Artificial Neural Network, Decision Tree, Multiway Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Decision Stump, AutoMLP, Linear Regression, Vector Linear Regression, Random Tree, Na\"ive Bayes, Na\"ive Bayes (Kernel), Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Deep Learning applied to this framework. The performance of each of these learning approaches was boosted by using the AdaBoost algorithm, and the Cross Validation approach was used for training and testing. The results show that in boosted form, the k-NN classifier outperforms all the other boosted learning approaches and is, therefore, the optimal learning method for this purpose. The results presented and discussed uphold the importance of this work to contribute towards augmenting the abilities of exoskeleton-based assisted and independent living of the elderly in the future of Internet of Things-based living environments, such as Smart Homes. As a specific use case, we also discuss how the findings of our work are relevant for augmenting the capabilities of the Hybrid Assistive Limb exoskeleton, a highly functional lower limb exoskeleton.

Multimedia classroom technology provides a unique opportunity to augment the classroom experience. Coordinating instructional streams with student feedback and assessment will enable instructors to know when and if their intended message was communicated to their students. A system where both instructional and presentational aspects of a lecture can be evaluated promptly will permit instructors and students to react quickly when there is a gap between intent and understanding, improving instruction. Presented in this paper is a multimedia, e-classroom framework where information can be gathered to measure student, faculty and organizational achievement and to assist in improvement. Specifically, this paper focuses on using this e-classroom framework to improve instruction. (Contains 18 references.) (Author)