Over the past decade, wearable sensors for fall detection have gained significant attention due to their potential in improving the safety of elderly users and reducing fall-related injuries. This review employs a network-based visualization approach to analyze research trends, key technologies, and collaborative networks. Using studies from SCI- and SSCI-indexed journals from 2015 to 2024, we analyzed 582 articles and 65 reviews with CiteSpace, revealing a significant rise in research on wearable sensors for fall detection. Additionally, we reviewed various datasets and machine learning techniques, from traditional methods to advanced deep learning frameworks, which demonstrate high accuracies, F1 scores, sensitivities, and specificities in controlled settings. This review provides a comprehensive overview of the progress and emerging trends, offering a foundation for future advancements in wearable fall detection systems.

This paper analyses the strategies of the substitutable suppliers competing to collaborate with a main manufacturer in “main manufacturer–supplier” (M-S) mode. In the research and development (R&D) of complex products, only one supplier can be chosen for one kind of part as a long-term collaboration partner with the manufacturer. The competition between substitutable suppliers focuses on the technology docking and price-concluding strategies. In this paper, one original supplier as the first-mover and one new supplier as the second-mover chose between the two strategies sequentially to compete for the collaborative preference of the manufacturer. We also took the delay cost brought by strategy changing into the consideration of the risks, which the suppliers and the manufacturer should prepare to share. With evolutionary game theory applied, we can conclude that the initial costs have little impact on suppliers’ making decisions, while the initial prices are correlated with both suppliers’ decision making. Results also show that hesitation and fluctuation periods exist in suppliers’ decision making, which have a relationship with existing strategy conditions, initial prices, and the cost caused by modifying the part. These results provide practical and reasonable managerial implications for M-S collaboration.

In this work, we propose a novel priors-based attention neural network (PANN) for image captioning, which aims at incorporating two kinds of priors, i.e., the probabilities being mentioned for local region proposals (PBM priors) and part-of-speech clues for caption words (POS priors), into a visual information extraction process at each word prediction. This work was inspired by the intuitions that region proposals have different inherent probabilities for image captioning, and that the POS clues bridge the word class (part-of-speech tag) with the categories of visual features. We propose new methods to extract these two priors, in which the PBM priors are obtained by computing the similarities between the caption feature vector and local feature vectors, while the POS priors are predicated at each step of word generation by taking the hidden state of the decoder as input. After that, these two kinds of priors are further incorporated into the PANN module of the decoder to help the decoder extract more accurate visual information for the current word generation. In our experiments, we qualitatively analyzed the proposed approach and quantitatively evaluated several captioning schemes with our PANN on the MS-COCO dataset. Experimental results demonstrate that our proposed method could achieve better performance as well as the effectiveness of the proposed network for image captioning.