Fall events are important health issues in elderly living environments like homes. Hence, a confident and real-time video surveillance device that pays attention could better their everyday lives. We proposed an optical flow feedback convolutional neural network according to the video stream in a home environment. Our proposed model uses rule-based filters before an input convolutional layer and the recorded optical flow for supervising the optical flow of variation. Detecting human posture is a key factor while fall events are like a falling posture. By sequencing frames of action it is possible to recognize a fall. Our system can clearly detect the normal lying posture and lying after falling. Our proposed method can efficiently detect action motion and recognize the action posture. We compared the performance with other standard benchmark datasets and deployed our model to simulate a real home situation, and the correct ratio achieved 82.7% and 98% separately.

Under the vigorous development of global anticipatory computing in recent years, there have been numerous applications of artificial intelligence (AI) in people’s daily lives. Learning analytics of big data can assist students, teachers, and school administrators to gain new knowledge and estimate learning information; in turn, the enhanced education contributes to the rapid development of science and technology. Education is sustainable life learning, as well as the most important promoter of science and technology worldwide. In recent years, a large number of anticipatory computing applications based on AI have promoted the training professional AI talent. As a result, this study aims to design a set of interactive robot-assisted teaching for classroom setting to help students overcoming academic difficulties. Teachers, students, and robots in the classroom can interact with each other through the ARCS motivation model in programming. The proposed method can help students to develop the motivation, relevance, and confidence in learning, thus enhancing their learning effectiveness. The robot, like a teaching assistant, can help students solving problems in the classroom by answering questions and evaluating students’ answers in natural and responsive interactions. The natural interactive responses of the robot are achieved through the use of a database of emotional big data (Google facial expression comparison dataset). The robot is loaded with an emotion recognition system to assess the moods of the students through their expressions and sounds, and then offer corresponding emotional responses. The robot is able to communicate naturally with the students, thereby attracting their attention, triggering their learning motivation, and improving their learning effectiveness.

Purpose: The purpose of this study is to explore the determinants of teachers’ continuance intention concerning cloud services. Design/methodology/approach: This study uses a cloud service, namely, Google Sites, to support teacher collaboration and further develop a research model to explore the determinants of their continued usage intention. Findings: The findings reveal that continued intention to use the cloud services is primarily determined by their attitude towards using it; attitude towards using is affected by perceived ease of use and perceived usefulness; and perceived ease of use is influenced by facilitating conditions, while perceived usefulness is influenced by social influence. Practical implications: This study determines that teachers intend to continue using cloud services once training in using such services has been provided and if they hear favourable opinions from others. Originality/value: The paper enables us to better understand factors affecting teachers' continuance intention toward cloud services.