Automatic Detection of Suicidal Behaviors Using an RGB-D Camera: Azure Kinect

Authors: Maha Jazouli

Abstract:

Suicide is one of the leading causes of death among prisoners, both in Canada and internationally. In recent years, rates of attempts of suicide and self-harm suicide have increased, with hangings being the most frequently used method. The objective of this article is to propose a method to automatically detect suicidal behaviors in real time. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Tests show that the proposed system gives satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: Suicide detection, Kinect Azure, RGB-D camera, SVM, gesture recognition.

References:

[1] World Health Organization. (2019). Suicide in the world: global health estimates (No. WHO/MSD/MER/19.3). World Health Organization.
[2] Dang, L. M., Min, K., Wang, H., Piran, M. J., Lee, C. H., & Moon, H. (2020). Sensor-based and vision-based human activity recognition: A comprehensive survey. Pattern Recognition, 108, 107561.
[3] Ashwini, K., & Amutha, R. (2020, July). Skeletal Data based Activity Recognition System. In 2020 International Conference on Communication and Signal Processing (ICCSP) (pp. 444-447). IEEE.
[4] Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2017). Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science, 5(3), 457-469.
[5] Pestian, J. P., Sorter, M., Connolly, B., Bretonnel Cohen, K., McCullumsmith, C., Gee, J. T., ... & STM Research Group. (2017). A machine learning approach to identifying the thought markers of suicidal subjects: a prospective multicenter trial. Suicide and Life‐Threatening Behavior, 47(1), 112-121.
[6] Walsh, C. G., Ribeiro, J. D., & Franklin, J. C. (2018). Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning. Journal of child psychology and psychiatry, 59(12), 1261-1270.
[7] Torous, J., Larsen, M. E., Depp, C., Cosco, T. D., Barnett, I., Nock, M. K., & Firth, J. (2018). Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: a review of current progress and next steps. Current psychiatry reports, 20(7), 51.
[8] Bernert, R. A., Hilberg, A. M., Melia, R., Kim, J. P., Shah, N. H., & Abnousi, F. (2020). Artificial intelligence and suicide prevention: a systematic review of machine learning investigations. International journal of environmental research and public health, 17(16), 5929.
[9] Graichen, C., Ashe, J., Ganesh, M., & Yu, L. (2012, December). Unobtrusive vital signs monitoring with range-controlled radar. In 2012 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (pp. 1-6). IEEE.
[10] Lee, S., Kim, H., Lee, S., Kim, Y., Lee, D., Ju, J., & Myung, H. (2014). Detection of a suicide by hanging based on a 3-D image analysis. IEEE sensors journal, 14(9), 2934-2935
[11] Chiranjeevi, V. R., & Elangovan, D. (2019, March). Surveillance Based Suicide Detection System Using Deep Learning. In 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) (pp. 1-7). IEEE.
[12] Elforaici, M. E. A., Chaaraoui, I., Bouachir, W., Ouakrim, Y., & Mezghani, N. (2018). Posture recognition using an RGB-D camera: exploring 3D body modeling and deep learning approaches. In 2018 IEEE life sciences conference (LSC) (pp. 69-72). IEEE.
[13] Bouachir, W., Gouiaa, R., Li, B., & Noumeir, R. (2018). Intelligent video surveillance for real-time detection of suicide attempts. Pattern Recognition Letters, 110, 1-7.
[14] Li, B., Bouachir, W., Gouiaa, R., & Noumeir, R. (2017). Real-time recognition of suicidal behavior using an RGB-D camera. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1-6). IEEE.
[15] Bouachir, W., & Noumeir, R. (2016). Automated video surveillance for preventing suicide attempts.
[16] Main de Boissière, A. (2020). Détection de comportements et d’événements potentiellement mortels dans les prisons à partir d’analyse de vidéos par intelligence artificielle (Doctoral dissertation, École de technologie supérieure).
[17] Microsoft Azure Kinect DK documentation: https://docs.microsoft.com/en-us/azure/kinect-dk/,2020
[18] Azure Kinect hardware specification:
[19] https://docs.microsoft.com/en-us/azure/kinect-dk/hardware-specification, 2020