AI Hears Your Health: Computer Audition for Health Monitoring

Amiriparian, Shahin; Schuller, Björn W.

doi:10.1007/978-3-030-94209-0_20

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Furthermore, if we organize the process of charisma generation into levels of abstraction, methods such as hierarchical RL (Saleh et al, 2020;Rothenpieler and Amiriparian, 2023) can offer a valuable approach for modeling charismatic behaviors at various levels of complexity. It can begin from overarching attributes like empathy and humor (Christ et al, 2022a;Kathan et al, 2022) (highlevel) to specific behaviors such as affective storytelling (Christ et al, 2022b) and body language (Wu et al, 2022) (mid-level), down to fine-grained details like speech patterns (Niebuhr et al, 2016;Amiriparian and Schuller, 2021) and linguistics (Rosenberg and Hirschberg, 2009;Fraser et al, 2022) (low-level). This multilevel approach could enhance the comprehensiveness of charisma generation and capture its multifaceted nature.…”

Section: Learning-based Generation Of Charismamentioning

confidence: 99%

Computational charisma—A brick by brick blueprint for building charismatic artificial intelligence

Schuller,

Amiriparian,

Batliner

et al. 2023

Front. Comput. Sci.

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Charisma is considered as one's ability to attract and potentially influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of charisma, mediating human-to-human conversation, or identifying charismatic individuals in big social data. While charisma is a subject of research in its own right, a number of models exist that base it on various “pillars,” that is, dimensions, often following the idea that charisma is given if someone could and would help others. Examples of such pillars, therefore, include influence (could help) and affability (would help) in scientific studies, or power (could help), presence, and warmth (both would help) as a popular concept. Modeling high levels in these dimensions, i. e., high influence and high affability, or high power, presence, and warmth for charismatic AI of the future, e. g., for humanoid robots or virtual agents, seems accomplishable. Beyond, also automatic measurement appears quite feasible with the recent advances in the related fields of Affective Computing and Social Signal Processing. Here, we therefore present a brick by brick blueprint for building machines that can appear charismatic, but also analyse the charisma of others. We first approach the topic very broadly and discuss how the foundation of charisma is defined from a psychological perspective. Throughout the manuscript, the building blocks (bricks) then become more specific and provide concrete groundwork for capturing charisma through artificial intelligence (AI). Following the introduction of the concept of charisma, we switch to charisma in spoken language as an exemplary modality that is essential for human-human and human-computer conversations. The computational perspective then deals with the recognition and generation of charismatic behavior by AI. This includes an overview of the state of play in the field and the aforementioned blueprint. We then list exemplary use cases of computational charismatic skills. The building blocks of application domains and ethics conclude the article.

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Section: Learning-based Generation Of Charismamentioning

confidence: 99%

Computational charisma—A brick by brick blueprint for building charismatic artificial intelligence

Schuller,

Amiriparian,

Batliner

et al. 2023

Front. Comput. Sci.

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“…Machine learning (ML) has been used to prevent and manage COPD by collecting and integrating large-scale medical data for precision medicine [16]. The COVID-19 pandemic has further driven research on artificial intelligence (AI)-based solutions, such as automatic detection of SARS-CoV-2 [17][18][19][20], mainly on smartphones [21]. These systems enable remote disease monitoring and reduce the need for patients to visit medical centers [22][23][24].…”

Section: Voice-based Estimation Of Respiratory Distressmentioning

confidence: 99%

Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios

Alvarado,

Grágeda,

Luzanto

et al. 2023

Sensors

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A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition, the original telephone training data are modified using an environmental model that incorporates natural robot-generated and external noise sources and reverberant effects using room impulse responses (RIRs). The results indicate that the average accuracy and AUC are just 0.4% less than those obtained with matched training/testing conditions with simulated data. Quite surprisingly, there is not much difference in accuracy and AUC between static and dynamic HRI conditions. Moreover, the beamforming methods delay-and-sum and MVDR lead to average improvement in accuracy and AUC equal to 8% and 2%, respectively, when applied to training and testing data. Regarding the complementarity of time-dependent and time-independent features, the combination of both types of classifiers provides the best joint accuracy and AUC score.

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“…Herein, AI-powered auscultation via respiratory sounds collected by electronic stethoscopes and smartphones has received massive attention for its high flexibility and scalability. 10 Traditional auscultation is usually done by respiratory physicians while training those experts to be qualified is costly in both time and money. Moreover, to be diagnosed, individuals need to go to the hospital or clinical venues, which increases clinical expenses and the risk of virus exposure.…”

Section: Introductionmentioning

confidence: 99%

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues

Tong

Mascolo

2022

Exp Biol Med (Maywood)

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Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we describe publicly available audio databases that can be used for experiments, illustrate the developed ML methods proposed to date, and flag some under-considered issues which still need attention. Compared to existing surveys on the topic, we cover the latest literature, especially those audio-based COVID-19 detection studies which have gained extensive attention in the last two years. This work can help to facilitate the application of artificial intelligence in the respiratory auscultation field.

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AI Hears Your Health: Computer Audition for Health Monitoring

Cited by 9 publications

References 20 publications

Computational charisma—A brick by brick blueprint for building charismatic artificial intelligence

Computational charisma—A brick by brick blueprint for building charismatic artificial intelligence

Automatic Detection of Dyspnea in Real Human–Robot Interaction Scenarios

Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues

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