scholarly journals Music, Computing, and Health: A roadmap for the current and future roles of music technology for health care and well-being

2021 ◽  
Author(s):  
Kat Rose Agres ◽  
Rebecca Schaefer ◽  
Anja Volk ◽  
Susan van Hooren ◽  
André Holzapfel ◽  
...  
Keyword(s):  
Health Care ◽  
Large Scale ◽  
State Of The Art ◽  
Well Being ◽  
Music Technology ◽  
The State ◽  
Future Research ◽  
Challenges And Opportunities ◽  
Evidence Based Treatments ◽  
Music Information

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop ‘Music, Computing, and Health’ was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech) and robotics. Following the discussions at the workshop, this paper provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the paper summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that 1) supports person-centered care and evidence-based treatments, and 2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The paper provides a resource for those seeking toengage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.

scholarly journals Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music Technology for Health Care and Well-Being

2021 ◽  
Vol 4 ◽  
pp. 205920432199770
Author(s):  
Kat R. Agres ◽  
Rebecca S. Schaefer ◽  
Anja Volk ◽  
Susan van Hooren ◽  
Andre Holzapfel ◽  
...  
Keyword(s):  
Health Care ◽  
Large Scale ◽  
State Of The Art ◽  
Well Being ◽  
Music Technology ◽  
The State ◽  
Future Research ◽  
Challenges And Opportunities ◽  
Evidence Based Treatments ◽  
Music Information

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.

scholarly journals Beyond the “Third Wave of Positive Psychology”: Challenges and Opportunities for Future Research

2022 ◽  
Vol 12 ◽  
Author(s):  
Marié P. Wissing
Keyword(s):  
Positive Psychology ◽  
State Of The Art ◽  
Well Being ◽  
The State ◽  
Future Research ◽  
Complex Nature ◽  
Third Wave ◽  
Health And Wellness ◽  
The Third ◽  
Challenges And Opportunities

The positive psychology (PP) landscape is changing, and its initial identity is being challenged. Moving beyond the “third wave of PP,” two roads for future research and practice in well-being studies are discerned: The first is the state of the art PP trajectory that will (for the near future) continue as a scientific (sub)discipline in/next to psychology (because of its popular brand name). The second trajectory (main focus of this manuscript) links to pointers described as part of the so-called third wave of PP, which will be argued as actually being the beginning of a new domain of inter- or transdisciplinary well-being studies in its own right. It has a broader scope than the state of the art in PP, but is more delineated than in planetary well-being studies. It is in particular suitable to understand the complex nature of bio-psycho-social-ecological well-being, and to promote health and wellness in times of enormous challenges and changes. A unique cohering focus for this post-disciplinary well-being research domain is proposed. In both trajectories, future research will have to increase cognizance of metatheoretical assumptions, develop more encompassing theories to bridge the conceptual fragmentation in the field, and implement methodological reforms, while keeping context and the interwovenness of the various levels of the scientific text in mind. Opportunities are indicated to contribute to the discourse on the identity and development of scientific knowledge in mainstream positive psychology and the evolving post-disciplinary domain of well-being studies.

A Comprehensive Taxonomy of Dynamic Texture Representation

2023 ◽  
Vol 55 (1) ◽  
pp. 1-39
Author(s):  
Thanh Tuan Nguyen ◽  
Thanh Phuong Nguyen
Keyword(s):  
Large Scale ◽  
Environmental Changes ◽  
State Of The Art ◽  
The State ◽  
Future Research ◽  
Research Activities ◽  
Potential Applications ◽  
Benchmark Datasets ◽  
Negative Impacts ◽  
Made In

Representing dynamic textures (DTs) plays an important role in many real implementations in the computer vision community. Due to the turbulent and non-directional motions of DTs along with the negative impacts of different factors (e.g., environmental changes, noise, illumination, etc.), efficiently analyzing DTs has raised considerable challenges for the state-of-the-art approaches. For 20 years, many different techniques have been introduced to handle the above well-known issues for enhancing the performance. Those methods have shown valuable contributions, but the problems have been incompletely dealt with, particularly recognizing DTs on large-scale datasets. In this article, we present a comprehensive taxonomy of DT representation in order to purposefully give a thorough overview of the existing methods along with overall evaluations of their obtained performances. Accordingly, we arrange the methods into six canonical categories. Each of them is then taken in a brief presentation of its principal methodology stream and various related variants. The effectiveness levels of the state-of-the-art methods are then investigated and thoroughly discussed with respect to quantitative and qualitative evaluations in classifying DTs on benchmark datasets. Finally, we point out several potential applications and the remaining challenges that should be addressed in further directions. In comparison with two existing shallow DT surveys (i.e., the first one is out of date as it was made in 2005, while the newer one (published in 2016) is an inadequate overview), we believe that our proposed comprehensive taxonomy not only provides a better view of DT representation for the target readers but also stimulates future research activities.

The power of summarization in graph mining and learning

2021 ◽  
Vol 14 (13) ◽  
pp. 3416-3416
Author(s):  
Danai Koutra
Keyword(s):  
Graph Mining ◽  
Large Scale ◽  
Well Being ◽  
Future Research ◽  
Natural Processes ◽  
Large Scale Data ◽  
Challenges And Opportunities ◽  
Graph Streams ◽  
Computationally Intensive ◽  
Graph Neural Networks

Our ability to generate, collect, and archive data related to everyday activities, such as interacting on social media, browsing the web, and monitoring well-being, is rapidly increasing. Getting the most benefit from this large-scale data requires analysis of patterns it contains, which is computationally intensive or even intractable. Summarization techniques produce compact data representations (summaries) that enable faster processing by complex algorithms and queries. This talk will cover summarization of interconnected data (graphs) [3], which can represent a variety of natural processes (e.g., friendships, communication). I will present an overview of my group's work on bridging the gap between research on summarized network representations and real-world problems. Examples include summarization of massive knowledge graphs for refinement [2] and on-device querying [4], summarization of graph streams for persistent activity detection [1], and summarization within graph neural networks for fast, interpretable classification [5]. I will conclude with open challenges and opportunities for future research.

How Do Self-tracking and Quantified Self Promote Health and Well-being: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Shan Feng ◽  
Matti Mäntymäki ◽  
Amandeep Dhir ◽  
Hannu Salmela
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Longitudinal Research ◽  
State Of The Art ◽  
Well Being ◽  
The State ◽  
Future Research ◽  
Quantified Self ◽  
Research Activities ◽  
Health And Well Being

BACKGROUND Self-tracking technologies are widely used in people’s daily lives and healthcare. Academic research on self-tracking and quantified self has also accumulated rapidly in recent years. Surprisingly, there is a paucity of research that reviews, classifies, and synthesizes the state of the art with respect to self-tracking and quantified self. OBJECTIVE Our objective was to identify the state of the art in self-tracking and quantified self in health and well-being. METHODS We have undertaken a systematic literature review on self-tracking and quantified self in promoting health and well-being. We reviewed altogether 81 empirical research papers. RESULTS Our results show that prior research has focused on three perspectives with respect to self-tracking and quantified self, namely individual user, healthcare professional, and market. We further describe the research themes under each of the three perspectives. Moreover, we classified the future research suggestions given in the literature into five directions: 1) employment of longitudinal research designs, 2) users’ modalities in the use of self-tracking technologies, 3) issues related to data sharing, 4) psychological and behavioral aspects of self-tracking, and 5) self-tracking in clinical use. We further described the specific research areas for each research direction. CONCLUSIONS This systematic literature review contributes to research and practice by assisting future research activities and providing practitioners with a concise view of the state of the art in self-tracking research.

scholarly journals Sentiment Analysis in Health and Well-Being: Systematic Review (Preprint)

2019 ◽  
Author(s):  
Anastazia Zunic ◽  
Padraig Corcoran ◽  
Irena Spasic
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Well Being ◽  
The State ◽  
Support Vector ◽  
Domain Specific ◽  
Wide Range ◽  
Health And Well Being

BACKGROUND Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” OBJECTIVE This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals. METHODS Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation. RESULTS The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. CONCLUSIONS SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

scholarly journals Sentiment Analysis in Health and Well-Being: Systematic Review

10.2196/16023 ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. e16023 ◽  
Author(s):  
Anastazia Zunic ◽  
Padraig Corcoran ◽  
Irena Spasic
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Well Being ◽  
The State ◽  
Support Vector ◽  
Domain Specific ◽  
Wide Range ◽  
Health And Well Being

Background Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” Objective This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals. Methods Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation. Results The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. Conclusions SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

Towards Interconnected Blockchains

2021 ◽  
Vol 54 (7) ◽  
pp. 1-39
Author(s):  
Ankur Lohachab ◽  
Saurabh Garg ◽  
Byeong Kang ◽  
Muhammad Bilal Amin ◽  
Junmin Lee ◽  
...  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Future Research ◽  
Missing Link ◽  
Layered Architecture ◽  
Blockchain Technology ◽  
Wide Scale ◽  
Effective Development ◽  
Insight Into ◽  
Game Changer

Unprecedented attention towards blockchain technology is serving as a game-changer in fostering the development of blockchain-enabled distinctive frameworks. However, fragmentation unleashed by its underlying concepts hinders different stakeholders from effectively utilizing blockchain-supported services, resulting in the obstruction of its wide-scale adoption. To explore synergies among the isolated frameworks requires comprehensively studying inter-blockchain communication approaches. These approaches broadly come under the umbrella of Blockchain Interoperability (BI) notion, as it can facilitate a novel paradigm of an integrated blockchain ecosystem that connects state-of-the-art disparate blockchains. Currently, there is a lack of studies that comprehensively review BI, which works as a stumbling block in its development. Therefore, this article aims to articulate potential of BI by reviewing it from diverse perspectives. Beginning with a glance of blockchain architecture fundamentals, this article discusses its associated platforms, taxonomy, and consensus mechanisms. Subsequently, it argues about BI’s requirement by exemplifying its potential opportunities and application areas. Concerning BI, an architecture seems to be a missing link. Hence, this article introduces a layered architecture for the effective development of protocols and methods for interoperable blockchains. Furthermore, this article proposes an in-depth BI research taxonomy and provides an insight into the state-of-the-art projects. Finally, it determines possible open challenges and future research in the domain.

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