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Multimodal Technologies for Remote Assessment of Neurological and Mental Health.

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Title: Multimodal Technologies for Remote Assessment of Neurological and Mental Health.
Authors: Ramanarayanan, Vikram1,2 vikram.ramanarayanan@modality.ai
Source: Journal of Speech, Language & Hearing Research. Nov2024, Vol. 67 Issue 11, p4233-4245. 13p.
Subject Terms: *Conversation, *Brain, *Information resources, *Communication, *Case studies, Competency assessment (Law), Cardiopulmonary system physiology, Speech, Body mass index, Health, Natural language processing, Group dynamics, Telemedicine, Neurological disorders, Physiological aspects of speech, Combined modality therapy, Body movement
Abstract: Purpose: Automated remote assessment and monitoring of patients' neurological and mental health is increasingly becoming an essential component of the digital clinic and telehealth ecosystem, especially after the COVID-19 pandemic. This review article reviews various modalities of health information that are useful for developing such remote clinical assessments in the real world at scale. Approach: We first present an overview of the various modalities of health information--speech acoustics, natural language, conversational dynamics, orofacial or full body movement, eye gaze, respiration, cardiopulmonary, and neural --which can each be extracted from various signal sources--audio, video, text, or sensors. We further motivate their clinical utility with examples of how information from each modality can help us characterize how different disorders affect different aspects of patients' spoken communication. We then elucidate the advantages of combining one or more of these modalities toward a more holistic, informative, and robust assessment. Findings: We find that combining multiple modalities of health information allows for improved scientific interpretability, improved performance on downstream health applications such as early detection and progress monitoring, improved technological robustness, and improved user experience. We illustrate how these principles can be leveraged for remote clinical assessment at scale using a real-world case study of the Modality assessment platform. Conclusion: This review article motivates the combination of human-centric information from multiple modalities to measure various aspects of patients' health, arguing that remote clinical assessment that integrates this complementary information can be more effective and lead to better clinical outcomes than using any one data stream in isolation. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Speech, Language & Hearing Research is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Multimodal Technologies for Remote Assessment of Neurological and Mental Health.
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  Data: <searchLink fieldCode="AR" term="%22Ramanarayanan%2C+Vikram%22">Ramanarayanan, Vikram</searchLink><relatesTo>1,2</relatesTo><i> vikram.ramanarayanan@modality.ai</i>
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  Data: *<searchLink fieldCode="DE" term="%22Conversation%22">Conversation</searchLink><br />*<searchLink fieldCode="DE" term="%22Brain%22">Brain</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+resources%22">Information resources</searchLink><br />*<searchLink fieldCode="DE" term="%22Communication%22">Communication</searchLink><br />*<searchLink fieldCode="DE" term="%22Case+studies%22">Case studies</searchLink><br /><searchLink fieldCode="DE" term="%22Competency+assessment+%28Law%29%22">Competency assessment (Law)</searchLink><br /><searchLink fieldCode="DE" term="%22Cardiopulmonary+system+physiology%22">Cardiopulmonary system physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Speech%22">Speech</searchLink><br /><searchLink fieldCode="DE" term="%22Body+mass+index%22">Body mass index</searchLink><br /><searchLink fieldCode="DE" term="%22Health%22">Health</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Group+dynamics%22">Group dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Telemedicine%22">Telemedicine</searchLink><br /><searchLink fieldCode="DE" term="%22Neurological+disorders%22">Neurological disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Physiological+aspects+of+speech%22">Physiological aspects of speech</searchLink><br /><searchLink fieldCode="DE" term="%22Combined+modality+therapy%22">Combined modality therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Body+movement%22">Body movement</searchLink>
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  Data: Purpose: Automated remote assessment and monitoring of patients' neurological and mental health is increasingly becoming an essential component of the digital clinic and telehealth ecosystem, especially after the COVID-19 pandemic. This review article reviews various modalities of health information that are useful for developing such remote clinical assessments in the real world at scale. Approach: We first present an overview of the various modalities of health information--speech acoustics, natural language, conversational dynamics, orofacial or full body movement, eye gaze, respiration, cardiopulmonary, and neural --which can each be extracted from various signal sources--audio, video, text, or sensors. We further motivate their clinical utility with examples of how information from each modality can help us characterize how different disorders affect different aspects of patients' spoken communication. We then elucidate the advantages of combining one or more of these modalities toward a more holistic, informative, and robust assessment. Findings: We find that combining multiple modalities of health information allows for improved scientific interpretability, improved performance on downstream health applications such as early detection and progress monitoring, improved technological robustness, and improved user experience. We illustrate how these principles can be leveraged for remote clinical assessment at scale using a real-world case study of the Modality assessment platform. Conclusion: This review article motivates the combination of human-centric information from multiple modalities to measure various aspects of patients' health, arguing that remote clinical assessment that integrates this complementary information can be more effective and lead to better clinical outcomes than using any one data stream in isolation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Speech, Language & Hearing Research is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=180765735
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        Value: 10.1044/2024_JSLHR-24-00142
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        Text: English
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      – SubjectFull: Conversation
        Type: general
      – SubjectFull: Brain
        Type: general
      – SubjectFull: Information resources
        Type: general
      – SubjectFull: Communication
        Type: general
      – SubjectFull: Case studies
        Type: general
      – SubjectFull: Competency assessment (Law)
        Type: general
      – SubjectFull: Cardiopulmonary system physiology
        Type: general
      – SubjectFull: Speech
        Type: general
      – SubjectFull: Body mass index
        Type: general
      – SubjectFull: Health
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Group dynamics
        Type: general
      – SubjectFull: Telemedicine
        Type: general
      – SubjectFull: Neurological disorders
        Type: general
      – SubjectFull: Physiological aspects of speech
        Type: general
      – SubjectFull: Combined modality therapy
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      – SubjectFull: Body movement
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      – TitleFull: Multimodal Technologies for Remote Assessment of Neurological and Mental Health.
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              M: 11
              Text: Nov2024
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              Y: 2024
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