Falling, as one of the main harm threats to the elderly, has drawn researchers’ attentions and has always been one of the most valuable research topics in the daily health-care for the elderly in last two decades. Before 2014, several researchers reviewed the development of fall detection, presented issues and challenges, and navigated the direction for the study in the future. With smart sensors and Internet of Things (IoT) developing rapidly, this field has made great progress. However, there is a lack of a review and discussion on novel sensors, technologies and algorithms introduced and employed from 2014, as well as the emerging challenges and new issues. To bridge this gap, we present an overview of fall detection research and discuss the core research questions on this topic. A total of 6830 related documents were collected and analyzed based on the key words. Among these documents, the twenty most influential and highly cited articles are selected and discussed profoundly from three perspectives: sensors, algorithms and performance. The findings would assist researchers in understanding current developments and barriers in the systems of fall detection. Although researchers achieve fruitful work and progress, this research domain still confronts challenges on theories and practice. In the near future, the new solutions based on advanced IoT will sustainably urge the development to prevent falling injuries.

Generative AI (GAI) and AI‐generated content (AIGC) have been increasingly involved in our work and daily life, providing a new learning experience for students. This study examines whether AI‐generated instructional videos (AIIV) can facilitate learning as effectively as traditional recorded videos (RV). We propose an instructional video generation pipeline that includes customized GPT (Generative Pre‐trained Transformer), text‐to‐speech and lip synthesis techniques to generate videos from slides and a clip or a photo of a human instructor. Seventy‐six students were randomly assigned to learn English words using either AIIV or RV, with performance assessed by retention, transfer and subjective measures from cognitive, emotional, motivational and social perspectives. The findings indicate that the AIIV group performed as well as the RV group in facilitating learning, with AIIV showing higher retention but no significant differences in transfer. RV was found to offer a stronger sense of social presence. Although other subjective measures were similar between the two groups, AIIV was perceived as slightly less favourable. However, the AIIV was still found to be moderately to highly attractive, addressing concerns related to the uncanny valley effect. This research demonstrates that AIGC can be an effective tool for learning, offering valuable implications for the use of GAI in educational settings. Practitioner notes: What is already known about this topic Instructional videos, especially those featuring a teacher's presence, have been widely used in second language learning to facilitate learning. Producing instructional videos is costly and burdensome. Generative AI has great potential for generating educational content. What this paper adds An AI‐generated instructional video (including generated lecture text, voice and appearance) demonstrated greater improvement in students' retention performance in English word learning than a traditional recorded video. Students perceived no significant differences between the AI‐generated instructional video and recorded video in satisfaction, motivation, trust, cognitive load, emotions and parasocial interaction dimensions, although the AI‐generated instructional video group reported slightly lower values. Despite AI‐generated instructional video eliciting a significantly lower value of social presence than recorded video, it led to a reduction in cognitive load and better performance. Implications for practice and/or policy We recommend using the AI‐generated instructional video in both physical and online classes for its positive effects on both learning achievement and learning experience. The findings indicate the equivalence principle in AI‐generated content, highlighting that the appearance, voice and lecture text generated by current AI technology have reached a certain level of quality.

Objective. Due to individual differences in electroencephalogram (EEG) signals, the learning model built by the subject-dependent technique from one person’s data would be inaccurate when applied to another person for emotion recognition. Thus, the subject-dependent approach for emotion recognition may result in poor generalization performance when compared to the subject-independent approach. However, existing studies have attempted but have not fully utilized EEG’s topology, nor have they solved the problem caused by the difference in data distribution between the source and target domains. Approach. To eliminate individual differences in EEG signals, this paper proposes the domain adversarial graph attention model, a novel EEG-based emotion recognition model. The basic idea is to generate a graph using biological topology to model multichannel EEG signals. Graph theory can topologically describe and analyze EEG channel relationships and mutual dependencies. Then, unlike other graph convolutional networks, self-attention pooling is used to benefit from the extraction of salient EEG features from the graph, effectively improving performance. Finally, following graph pooling, the domain adversarial model based on the graph is used to identify and handle EEG variation across subjects, achieving good generalizability efficiently. Main Results. We conduct extensive evaluations on two benchmark data sets (SEED and SEED IV) and obtain cutting-edge results in subject-independent emotion recognition. Our model boosts the SEED accuracy to 92.59% (4.06% improvement) with the lowest standard deviation (STD) of 3.21% (2.46% decrements) and SEED IV accuracy to 80.74% (6.90% improvement) with the lowest STD of 4.14% (3.88% decrements), respectively. The computational complexity is drastically reduced in comparison to similar efforts (33 times lower). Significance. We have developed a model that significantly reduces the computation time while maintaining accuracy, making EEG-based emotion decoding more practical and generalizable.

The fall is one of the most important research fields of solitary elder healthcare at home based on Internet of Things technology. Current studies mainly focus on the fall detection, which helps medical staffs bring a fallen elder out of danger in time. However, it neither predicts a fall nor provides an effective protection against a fall. This article studies the fall prediction based on human biomechanics equilibrium and body posture characteristics through analyzing three-dimensional skeleton joints data from the depth camera sensor Kinect. The research includes building a human bionic mass model using skeleton joints data from Kinect, determining human balance state, and proposing a fall prediction algorithm based on recurrent neural networks by unbalanced posture features. We evaluate the model and algorithm on an open database. The performance indicates that the fall prediction algorithm by studying human biomechanics can predict a fall (91.7%) and provide a certain amount of time (333 ms) before the elder injuring (hitting the floor). This work provides a technical basis and a data analytics approach for the fall protection.