A Review on Human Action Recognition and Machine Learning Techniques for Suicide Detection System

Chiranjeevi, V. Rahul; Elangovan, D.

doi:10.1007/978-3-030-16681-6_5

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Another form of automated detection uses behavioral identification using automated vision methods to detect a hanging. Rather than using motion sensors or an outlined region of interest in the CCTV scene, these methods use computer vision analysis to detect a person in the scene, their limbs, as well as when the position and movement of those limbs indicate a suicide attempt by hanging [32][33][34][35][36]. In one study, the authors developed a video surveillance system to detect a suicide attempt by hanging using color cameras that can also detect depth (red, green, blue-depth cameras; RGB-D) to understand the three-dimensional positions of body joints [33].…”

Section: Describing and Evaluating Interventions Using Cctv Or Video Footagementioning

confidence: 99%

“…In this stage, we aim to develop an automated system that can detect specific human behaviors in real time for suicide prevention. The video clips will be fed through a data pipeline consisting of 4 layers of function modules, each with their own goal: the first is a pedestrian detection module to detect objects and persons in the clip; the second is a pedestrian tracking module to track the individual in the scene; the third is a pose estimation module to outline the location and configuration of the human joints and limbs (similar to that used by [32][33][34][35][36] to identify hanging); and the fourth is action recognition which interprets the configuration and motion of the joints and limbs to infer behavior (Figure 2). Using this approach, we are able to cover a wide range of behaviors.…”

Section: Behavior Identification Through Computer Visionmentioning

confidence: 99%

See 1 more Smart Citation

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

Onie¹,

Li²,

Liang³

et al. 2021

JMIR Ment Health

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Background Suicide is a recognized public health issue, with approximately 800,000 people dying by suicide each year. Among the different technologies used in suicide research, closed-circuit television (CCTV) and video have been used for a wide array of applications, including assessing crisis behaviors 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 of this study was to review the literature to understand how CCTV and video data 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 behaviors prior to a suicide attempt. Methods We conducted a search using 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 have primarily been used in 3 ways: (1) to identify risk factors for suicide (eg, inferring depression from facial expressions), (2) understanding suicide after an attempt (eg, forensic applications), and (3) as part of an intervention (eg, using computer vision and automated systems to identify if a suicide attempt is in progress). Furthermore, work in progress demonstrates how we can identify behaviors 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 toward an automated detection system for early intervention. Despite many challenges, we show promising progress in developing an automated detection system for preattempt behaviors, which may allow for early intervention.

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