Abstract
It is not uncommon for a person to be alone when they have a heart attack. Getting help quickly can mean the difference between life and death. The pain a person feels during a heart attack may prevent them from seeking help in time, so automated methods are needed to detect and alert to such events. This article presents a proposal based on machine vision and deep learning to identify possible heart attacks. First, a typical human posture of a possible heart attack is identified using the upper body joints from skeletal studies. As similar non-infarct postures are possible, the posture analysis is integrated with the facial expression of pain to reduce the number of false positives. The proposed method has achieved 93.33% accuracy in detecting myocardial infarction.
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Acknowledgements
Grant PID2023-149753OB-C21 funded by Spanish MCIU/ AEI/ 10.13039/5011 00011033/ERDF, EU. Grant PID2020-115220RB-C21 funded by Spanish MCIN/ AEI/ 10.13039/ 501100011033 and by “ERDF A way to make Europe”. Grant 2022-GRIN-34436 funded by Universidad de Castilla-La Mancha and by “ERDF A way of making Europe”.
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Rojas-Albarracín, G., Fernández-Caballero, A., Pereira, A., López, M.T. (2024). Heart Attack Detection Using Body Posture and Facial Expression of Pain. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_39
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