Explainable AI for Suicide Risk Assessment Using Eye Activities and Head Gestures

TL;DR

This paper presents a machine learning approach analyzing eye and head movements to reliably assess suicide risk, offering a systematic, high-accuracy method that aligns with psychological insights and aids healthcare providers.

Contribution

It introduces a novel dataset and demonstrates that eye and head behavior features can classify suicide risk levels with over 98% accuracy.

Findings

High-risk individuals show eye contact avoidance and slower blinking.

Features effectively distinguish different suicide risk levels.

Method aligns with psychological theories of depression and anxiety.

Abstract

The prevalence of suicide has been on the rise since the 20th century, causing severe emotional damage to individuals, families, and communities alike. Despite the severity of this suicide epidemic, there is so far no reliable and systematic way to assess suicide intent of a given individual. Through efforts to automate and systematize diagnosis of mental illnesses over the past few years, verbal and acoustic behaviors have received increasing attention as biomarkers, but little has been done to study eyelids, gaze, and head pose in evaluating suicide risk. This study explores statistical analysis, feature selection, and machine learning classification as means of suicide risk evaluation and nonverbal behavioral interpretation. Applying these methods to the eye and head signals extracted from our unique dataset, this study finds that high-risk suicidal individuals experience psycho-motor retardation and symptoms of anxiety and depression, characterized by eye contact avoidance, slower blinks and a downward eye gaze. By comparing results from different methods of classification, we determined that these features are highly capable of automatically classifying different levels of suicide risk consistently and with high accuracy, above 98%. Our conclusion corroborates psychological studies, and shows great potential of a systematic approach in suicide risk evaluation that is adoptable by both healthcare providers and naive observers.