Pain assessment in non-verbal individuals remains a significant clinical challenge. While physiological sensing has shown promise for automated pain detection, identifying the anatomical location of pain from physiological responses remains largely unexplored. In this paper, we investigate pain localization using multi...
Tatsui Inoue, Haruzo Kaneko, Takuro Yonezawa et al.· 2026 14th International Conf...· 0 citations
Assessing pain in patients who cannot self-report remains an open clinical problem. The AI4Pain 2026 Grand Challenge extends this task from detection to localization, requiring three-class discrimination of No-Pain, Pain-Arm and Pain-Hand from four peripheral physiological signals (blood volume pulse, electrodermal act...
Soyeb Pervez Jim, Ahasanul Kabir Rifat, Tahera Hossain et al.· 2026 14th International Conf...· 0 citations
Pain assessment traditionally relies on self-report measures, limiting its applicability to non-verbal patients and individuals with communication impairments. While prior studies have focused on pain detection and intensity estimation, pain location classification from physiological signals remains largely unexplored....
Tahera Hossain, Shahera Hossain, Keiko Yamashita et al.· 2026 14th International Conf...· 0 citations
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