Abstract
In the era of the Internet of Medical Things (IoMT), providing excellent patient care requires an understanding of patient emotions and how they are feeling. The method described in this abstract, which makes use of OpenCV algorithms, is new for examining patient emotional patterns. This technology uses IoMT devices in conjunction with physiological signals from the body and facial expressions to ascertain the emotional states of patients. OpenCV’s powerful image processing methods, such as feature extraction, emotion recognition, and facial identification, are used to do real-time facial cue analysis. In order to contextualize emotions, IoMT devices also collect data on physiological traits including skin conductance, body temperature, and heart rate variability. Machine learning models are given the processed data in order to find connections linking emotional states to bodily reactions. Convolutional neural networks and recurrent neural networks are two examples of deep learning algorithms that are used to extract complex patterns from merged data. An important asset of the system is its versatility; it can be tailored for a variety of medical situations, such as chronic pain management, mental health disorders, and post-operative care. In the end, real-time analysis improves patient well-being and the general standard of healthcare services by enabling prompt responses, such as warning healthcare personnel of distress signals.
