The Use of Closed-Circuit Television and Video in Suicide Prevention: Narrative Review and Future Directions | JoVE Visualize

The Use of Closed-Circuit Television and Video in Suicide Prevention: Narrative Review and Future Directions

Sandersan Onie¹, Xun Li², Morgan Liang²

¹Black Dog Institute, University of New South Wales, Sydney, Sydney, Australia.

JMIR Mental Health|May 7, 2021

View abstract on PubMed

Summary

Closed-circuit television (CCTV) and video analysis are increasingly used in suicide prevention research. This review highlights their application in identifying risk factors and developing automated detection systems for early intervention.

Area of Science:

Public Health
Computer Vision
Behavioral Science

Background:

Suicide is a major global public health concern, claiming approximately 800,000 lives annually.
Closed-circuit television (CCTV) and video technologies have found diverse applications in crisis assessment and suicide attempt detection.
A comprehensive review of CCTV and video in suicide research and interventions was previously lacking.

Purpose of the Study:

To systematically review existing literature on the use of CCTV and video data in understanding and preventing suicide.
To identify gaps in current research, specifically the lack of systems for detecting pre-attempt behaviors.
To report on ongoing research utilizing computer vision for early identification of suicidal behaviors.

Main Methods:

Conducted a literature search using keywords "suicide," "cctv," and "video" across PubMed, Inspec, and Web of Science.
Included all studies utilizing CCTV or video footage for suicide understanding or prevention, irrespective of study quality.
Noted methodological limitations in the reviewed studies to provide context for findings.

Main Results:

CCTV and video have been applied in three primary ways: identifying suicide risk factors, analyzing post-attempt behavior (forensic applications), and enabling real-time interventions via automated systems.
Emerging research focuses on using computer vision to detect pre-attempt behaviors in high-risk locations.
This work addresses a critical gap identified in the literature regarding proactive suicide prevention.

Conclusions:

CCTV and video technologies are pivotal in developing automated suicide detection systems for early intervention.
Significant progress is being made in creating systems to identify pre-attempt behaviors, offering potential for timely interventions.
The field is advancing towards automated solutions for suicide prevention, despite existing challenges.