JMIR Publications

ABSTRACT

Background:

Suicide is a recognised public health issue, with approximately 800,000 people dying by suicide each year. Among the different technologies used in suicide research, CCTV and video have been used for a wide array of applications, including assessing crisis behaviours at metro stations, and using computer vision to identify a suicide attempt in progress. However, there has been no review of suicide research and interventions using CCTV and video.

Objective:

The objective is to review the literature to understand how CCTV and video have been used in understanding and preventing suicide. Furthermore, to more fully capture progress in the field, we report on an ongoing study to respond to an identified gap in the narrative review, by using a computer vision-based system to identify behaviours prior to a suicide attempt.

Methods:

We conducted a search of the keywords “suicide”, “cctv” and “video” on PubMed, Inspec and Web of Science. We included any studies which used cctv or video footage to understand or prevent suicide. If a study fell into our area of interest, we included it regardless of the quality as our goal was to understand the scope of how cctv and video had been used rather than quantify any specific effect size, but we noted the shortcomings in their design and analyses when discussing the studies.

Results:

The review found that CCTV and video has primarily been used in three ways: i) to identify risk factors for suicide e.g. inferring depression from facial expressions, ii) understanding suicide after an attempt e.g. forensic applications, and iii) as part of an intervention e.g. using computer vision to and automated systems to identify if a suicide attempt is in progress. Furthermore, work in progress demonstrates how we can identify behaviours prior to an attempt at a hotspot, an important gap identified by papers in the literature.

Conclusions:

Thus far, CCTV and video have been used in a wide array of applications, most notably in designing automated detection systems, with the field heading towards an automated detection system for early intervention. Despite many challenges, we show promising progress in developing an automated detection system for pre-attempt behaviours, which may allow for early intervention.