Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review.

BACKGROUND: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. OBJECTIVE: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. METHODS: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. RESULTS: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. CONCLUSIONS: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather insufficient level of detail.

Fuzzy monitoring of in-bed postural changes for the prevention of pressure ulcers using inertial sensors attached to clothing.

Postural changes while maintaining a correct body position are the most efficient method of preventing pressure ulcers. However, executing a protocol of postural changes over a long period of time is an arduous task for caregivers. To address this problem, we propose a fuzzy monitoring system for postural changes which recognizes in-bed postures by means of micro inertial sensors attached to patients' clothes. First, we integrate a data-driven model to classify in-bed postures from the micro inertial sensors which are located in the socks and t-shirt of the patient. Second, a knowledge-based fuzzy model computes the priority of postural changes for body zones based on expert-defined protocols. Results show encouraging performance in the classification of in-bed postures and high adaptability of the knowledge-based fuzzy approach.