TY - GEN
T1 - Telehealth 2.0
T2 - 19th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2023, in conjunction with the 11th International Conference on Orange Technology, Applications, and Tools, ICOT 2023
AU - Subramanian, Barathi
AU - Ugliz, Rakhmonov Akhrorjon Akhmadjon
AU - Varnousefaderani, Bahar Amirian
AU - Kim, Jeonghong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Telemedicine is revolutionizing healthcare delivery, especially in remote areas and situations where face-to-face consultations are infeasible. However, as the adoption of digital health solutions accelerates, it brings forth challenges related to the authenticity of remote sessions. A significant threat in recent years has been spoofing attacks, such as photo impersonations or video replays, which may threaten the authenticity of remote interactions. It is even more difficult for healthcare professionals to gauge a patient’s emotional well-being in the absence of physical signals. This can lead them to miss the subtle signs of distress or anxiety. To address these multifaceted issues, this paper introduces a novel framework for real-time liveliness detection and mood analysis. By merging the capabilities of the YOLO (you only look once) v8 model enhanced with spatial attention mechanism (SAM) and frequency analysis (FA) using fast Fourier transform (FFT) integrated with a deep convolutional neural network (CNN). Preliminary findings, showcased through real-time emotion tracking and comprehensive emotion distribution charts, highlight the unique ability of our system to accurately measure emotions in real time. This not only offers profound insights into a patient’s mental state but also paves the way for proactive healthcare interventions in telemedicine.
AB - Telemedicine is revolutionizing healthcare delivery, especially in remote areas and situations where face-to-face consultations are infeasible. However, as the adoption of digital health solutions accelerates, it brings forth challenges related to the authenticity of remote sessions. A significant threat in recent years has been spoofing attacks, such as photo impersonations or video replays, which may threaten the authenticity of remote interactions. It is even more difficult for healthcare professionals to gauge a patient’s emotional well-being in the absence of physical signals. This can lead them to miss the subtle signs of distress or anxiety. To address these multifaceted issues, this paper introduces a novel framework for real-time liveliness detection and mood analysis. By merging the capabilities of the YOLO (you only look once) v8 model enhanced with spatial attention mechanism (SAM) and frequency analysis (FA) using fast Fourier transform (FFT) integrated with a deep convolutional neural network (CNN). Preliminary findings, showcased through real-time emotion tracking and comprehensive emotion distribution charts, highlight the unique ability of our system to accurately measure emotions in real time. This not only offers profound insights into a patient’s mental state but also paves the way for proactive healthcare interventions in telemedicine.
KW - Face emotion recognition
KW - Liveliness detection
KW - Remote session
KW - Telehealth
UR - https://www.scopus.com/pages/publications/105004252788
U2 - 10.1007/978-981-97-8760-9_14
DO - 10.1007/978-981-97-8760-9_14
M3 - Conference contribution
AN - SCOPUS:105004252788
SN - 9789819787593
T3 - Smart Innovation, Systems and Technologies
SP - 149
EP - 159
BT - Advances in Intelligent Information Hiding and Multimedia Signal Processing, Volume 2 - Proceeding of the 19th International Conference on IIH-MSP in conjunction with 11th International Conference on Orange Technology, Applications and Tools
A2 - Tseng, Shih-Pang
A2 - Paul, Anand
A2 - Pan, Jeng-Shyang
A2 - Favorskaya, Margarita
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 5 December 2023 through 7 December 2023
ER -