Group activity recognition aims to understand the overall activity involving multi-person interactions within complex environments. These activities are intricate, encompassing a multitude of interactions such as communication, coordination, and cooperation among individuals. Identifying and analyzing these interactions poses substantial challenges. Researchers in this field often struggle to integrate diverse information levels, as current methods tend to focus separately on individual behavior, interactional dynamics, and collective patterns. This paper addresses this issue by introducing introduces the Multilevel Information Fusion (MIC-Former) Model, a model that integrates these various aspects into a cohesive framework using the Banzhaf power index ratio from game theory. By fusing trajectory and image data, MIC-Former captures the nuanced intricacies of group activities. The model, segmented into three branches, scrutinizes individual movements, member interactions, and overarching group dynamics via transformer networks. A novel fusion technique, grounded in game theory principles, seamlessly integrates these features. Experimental validation on volleyball and collective activity datasets demonstrates MIC-Former’s superior recognition capabilities, underscoring its potential as a robust tool for unraveling the complexities of group behavior and social interactions.

Rapid eye movement (REM) sleep without atonia (RSWA) is a critical diagnostic criterion for REM sleep behavior disorder (RBD). Current clinical practices rely on time-consuming manual annotation, increasing workload, and introducing variability. Existing automated methods, including CNN, LSTM, Transformer, and their combinations, fail to fully exploit the inherent physiological relationship between sleep staging and RSWA detection, while also facing challenges such as severe data imbalance and the complex fusion of multichannel signals. To address these limitations, we propose a multi-task learning framework that jointly optimizes both tasks. Our Multi-scale Information Attention Bottleneck Network (MIABNet) backbone improves multichannel fusion. Building upon MIABNet, the Multi-task RBD Network (MtRBD) implements dynamic feature enhancement, facilitating cross-task information flow while preserving physiological relationships. On the clinical CZ-RBD dataset collected from 30 patients over 59 nights at Shanghai Changzheng Hospital, our framework achieved the accuracy of 83.0% for sleep staging and 93.6% for RSWA detection, with an accuracy of 77.6% for critical RSWA event identification, outperforming single-task methods in cross-subject validation. Through modality selection and Grad-CAM visualization, we enhance clinical interpretability, providing reliable support for early detection of RBD and related neurodegenerative diseases.

In recent years, fiber-optical distributed acoustic sensing (DAS) has been applied to various large-scale infrastructure monitoring areas in smart cities, leading to a new generation of fiber-optic IoT for ground listening. However, its single-task-focused postprocessing methods cannot achieve real-time efficient ground event recognition and localization concurrently. In this article, a two-level multitask learning (MTL) enhanced smart fiber-optical DAS (sDAS) system is proposed, for the first time, to simultaneously realize ground event recognition and localization. Performances and efficiency of both tasks are significantly improved by sharing knowledge across them. Besides, the imbalanced incremental learning ability for new events is also enhanced in the proposed MTL network. The total computation time for the two tasks is greatly shortened to 0.3 ms for a spatial-temporal sample with 129-m fiber length and 5-s time frame, which equals to a processing time of 0.04 s over a total fiber length of 18.7-km with a spatial sampling interval of 1.29 m, and is even better than the fastest single recognition reported to date. In the field test, such an MTL-enhanced sDAS system indicates excellent feature extraction performance with classification accuracy of up to 99.46% for five events and location error of ±1 m for two core-events at 8/16 different radial distances, which are much better than the DAS systems with multiclassifier and the combined single-task learning methods. Also, the MTL-enhanced sDAS shows strong robustness against environmental noises. Hence, it provides a breakthrough technology for time-efficient multitask processing in smart distributed sensors.

Mongolian is an agglutinative language, which re-sults in a large number of words derived from the same stems connecting different suffixes. This morphological richness leads to high out-of-vocabulary (OOV) rates and causes problems of data sparsity. In this paper, our proposed recognition system is composed of three parts: handwritten image preprocessing, mapping of images to grapheme sequences, and sub-word-based language model (LM) decoding. We present a sub-word-based n-gram LM to solve the high OOV rate problem. According to the characteristics of Mongolian, we modified the traditional token passing algorithm to improve decoding speed and to easy to combine with any n-gram LM. We evaluated the performance of sub-words at different levels on the open Mongolian offline handwriting dataset (MHW). The bi-syllable 2-gram LM showed the best performance, with 18.32% and 23.22% word-error rates (WERs) on two test sets. Our various experiments show that, this method can predict in vocabulary words with a higher accuracy rate and also predict OOV words with a certain accuracy rate.

Personal health record (PHR) systems offer a technology for personal health information management (PHIM) activities. Despite efforts to increase the use of PHR systems as a mechanism to support better patient-centered care and improve information management across the continuum of care, PHR adoption remains low. The purpose of this study was to explore how to design a PHR system that can adequately support personal health information management activities. Using a mixed-methods approach (questionnaires and interviews), we identified the factors affecting a person’s intention to use PHRs and also described the personal health information management activities among people from a wide age range in the United States. Results indicated that the intention to use PHR systems was affected by system-related factors, such as perceived usefulness, health information understandability, personalization, and patient–clinician communication support, and user-related factors, such as social influence, self-efficacy, and willingness to share. Furthermore, five types of personal health information management activities were found, including storage, organization, maintenance, retrieval, and sharing. Informed by the study findings, we developed seven design recommendations to improve PHR systems. Future studies can focus on further validating these findings using other methods and be based on larger and more representative PHR users.

Video is widely used as a data collection method in observational research. However, the process of analyzing video data can be overwhelming. In this study, we discuss several issues that we found during analyzing video data, such as the boundary of behaviors, priority for intertwined behaviors, and misunderstanding of behavior codes among analysts. We propose an iterative approach with reliability analysis to refine the coding scheme and solve these problems. This process involves two analyses, alternating between independent coding and coding in pairs.

The practical environment settings are always dynamic and unpredictable. And the adaptive solution is deteriorated seriously unless we can accurately determine when the filter's adaptation actually converges. In this paper, based on the principle of orthogonality, a scheme is proposed for sharply judging the iteration's convergence to obtain the optimal estimation in practically unknown stationary or nonstationary circumstances. The discriminant obtained through the estimated mean-square values of the desired, output and error signals at each iteration cycle, can be updated according to the varying characteristics of the actual input signal. Cases of both computer simulations and real applications are studied to validate its effectiveness in stationary and nonstationary environments