# Emotional Health Detection in HAR: New Approach Using Ensemble SNN
###### Abstract  
Computer recognition of human activity is an important area of research in computer vision. Human activity recognition (HAR) involves identifying human activities in real-life contexts and plays an important role in interpersonal interaction. Artificial intelligence usually identifies activities by analyzing data collected using different sources. These can be wearable sensors, MEMS devices embedded in smartphones, cameras, or CCTV systems. As part of HAR, computer vision technology can be applied to the recognition of the emotional state through facial expressions using facial positions such as the nose, eyes, and lips. Human facial expressions change with different health states. Our application is oriented toward the detection of the emotional health of subjects using a self-normalizing neural network (SNN) in cascade with an ensemble layer. We identify the subjects' emotional states through which the medical staff can derive useful indications of the patient's state of health. HAR; face emotion recognition; face detection; computer vision; deep learning; SNN; ensemble; vectorflow +
Footnote †: journal: Medical  
sensors, which, when processed and analyzed, provide valuable indications to healthcare personnel and doctors to improve care and provide adequate services. This is aimed at improving the lifestyle of people in need of assistance. The recognition of facial emotions is important because, from the analysis of the face, it is possible to detect the state of health of the subject, such as anxiety, depression, stress, or malaise, making a facial diagnosis possible. It is a beneficial technique in caring for the elderly; through the information provided, medical personnel can evaluate the type of intervention to reduce the state of the discomfort of the subjects. Some manifestations of the face can be associated with the first pathological symptoms facilitating the prevention of diseases that can degenerate. The eyes, in particular, can report physical and mental changes. This technique has ancient roots, in particular in traditional Chinese medicine (TCM), in which doctors performed a diagnosis of a disease by looking at facial features. They believed that pathological changes of internal organs reflected directly on the face allowed them to determine a diagnosis. Each anatomical part of the face reflected the state of functioning of each organ; for example, the cheeks were associated with the lungs, the lips with the digestive system and stomach, and the eyebrows with the nervous and respiratory systems. This technique, called \"facial diagnosis,\" requires considerable experience in order to perform an accurate diagnosis. In modern times, the difficulty of obtaining a medical examination due to economic conditions and the scarce availability of medical resources for those who live in underdeveloped geographical areas has stimulated research to develop techniques and diagnoses using artificial intelligence. Finally, thanks to the technological evolution of deep learning, it has been found that computer-assisted facial diagnosis has low error rates [8]. While being a helpful tool, the use of deep learning in facial recognition presents some problems on which researchers work to improve efficiency. The main challenges must be addressed: accuracy, security, and privacy. Accuracy is an essential aspect of the recognition process. The detected face may have lighting problems, poor image quality, low resolution, a blurred face, or different types of occlusions: systematic (hair, masks, clothes, and make-up) and temporary (hands covering the face or pose variations). These factors create errors in the faceprint feedback with faces in the database. Using a large amount of training data and 3D imagery improves accuracy. Security refers to the risk of using facial recognition for identity theft or illicit purposes. Efficient ML and DL security systems already present in systems can provide adequate protection against illegal use.