TY - JOUR
T1 - A critical review of multimodal-multisensor analytics for anxiety assessment
AU - Senaratne, Hashini
AU - Oviatt, Sharon
AU - Ellis, Kirsten
AU - Melvin, Glenn
N1 - Publisher Copyright:
© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2022/10
Y1 - 2022/10
N2 - Recently, interest has grown in the assessment of anxiety that leverages human physiological and behavioral data to address the drawbacks of current subjective clinical assessments. Complex experiences of anxiety vary on multiple characteristics, including triggers, responses, duration and severity, and impact differently on the risk of anxiety disorders. This article reviews the past decade of studies that objectively analyzed various anxiety characteristics related to five common anxiety disorders in adults utilizing features of cardiac, electrodermal, blood pressure, respiratory, vocal, posture, movement, and eye metrics. Its originality lies in the synthesis and interpretation of consistently discovered heterogeneous predictors of anxiety and multimodal-multisensor analytics based on them. We reveal that few anxiety characteristics have been evaluated using multimodal-multisensor metrics, and many of the identified predictive features are confounded. As such, objective anxiety assessments are not yet complete or precise. That said, few multimodal-multisensor systems evaluated indicate an approximately 11.73% performance gain compared to unimodal systems, highlighting a promising powerful tool. We suggest six high-priority future directions to address the current gaps and limitations in infrastructure, basic knowledge, and application areas. Action in these directions will expedite the discovery of rich, accurate, continuous, and objective assessments and their use in impactful end-user applications.
AB - Recently, interest has grown in the assessment of anxiety that leverages human physiological and behavioral data to address the drawbacks of current subjective clinical assessments. Complex experiences of anxiety vary on multiple characteristics, including triggers, responses, duration and severity, and impact differently on the risk of anxiety disorders. This article reviews the past decade of studies that objectively analyzed various anxiety characteristics related to five common anxiety disorders in adults utilizing features of cardiac, electrodermal, blood pressure, respiratory, vocal, posture, movement, and eye metrics. Its originality lies in the synthesis and interpretation of consistently discovered heterogeneous predictors of anxiety and multimodal-multisensor analytics based on them. We reveal that few anxiety characteristics have been evaluated using multimodal-multisensor metrics, and many of the identified predictive features are confounded. As such, objective anxiety assessments are not yet complete or precise. That said, few multimodal-multisensor systems evaluated indicate an approximately 11.73% performance gain compared to unimodal systems, highlighting a promising powerful tool. We suggest six high-priority future directions to address the current gaps and limitations in infrastructure, basic knowledge, and application areas. Action in these directions will expedite the discovery of rich, accurate, continuous, and objective assessments and their use in impactful end-user applications.
KW - Anxiety
KW - multimodal analytics
KW - physiological and behavioral data
KW - ubiquitous technology
UR - http://www.scopus.com/inward/record.url?scp=85146416375&partnerID=8YFLogxK
U2 - 10.1145/3556980
DO - 10.1145/3556980
M3 - Review Article
AN - SCOPUS:85146416375
SN - 2691-1957
VL - 3
JO - ACM Transactions on Computing for Healthcare
JF - ACM Transactions on Computing for Healthcare
IS - 4
M1 - 3556980
ER -