To view the full text, please login as a subscribed user or purchase a subscription. Click here to view the full text on ScienceDirect.

Figures

Fig. 1

Affective computing systems commonly consist of affective modalities, derived features, and machine learning models that predict affect.

Fig. 2

An affective computing-based dashboard that could support human-delivered therapy.

Fig. 3

Example therapy workflows that may benefit from affective computing-based applications.

Fig. 4

Depiction of the shift from Web2 to Web3 infrastructure, which may benefit health applications.

First page of article

Affective computing, the discipline aimed at enabling computers to interpret, express, and modify emotion, is rapidly maturing. Although not aimed exclusively at health care, the technology holds promise to increase our understanding of psychotherapy, train future generations of clinicians, and directly deliver care alongside human therapists. It may also enhance autonomous therapy systems to deliver care in a stepped-care model. This article reviews the principles of, and recent advances in, affective computing for psychotherapy. It outlines emerging and potential applications and pitfalls, concluding with steps toward ethical development and clinical adoption.

To access this article, please choose from the options below

Purchase access to this article

Claim Access

If you are a current subscriber with Society Membership or an Account Number, claim your access now.

Subscribe to this title

Purchase a subscription to gain access to this and all other articles in this journal.

Institutional Access

Visit ScienceDirect to see if you have access via your institution.

 

Linked Articles

Unknown widget #d2170c4d-a9cf-482f-ac17-ef77d57a1866

of type linkedContentList

Related Articles

Unknown widget #c2ffda61-8426-42f7-926b-03d7330eede2

of type relatedArticleListWidget