With the continuous development of science and technology, artificial intelligence (AI) is coming into our lives and changing our lives. Since China entered the aging society in 2000, the degree of population aging has deepened. Comprehensive geriatric assessment (CGA) is now the accepted gold standard for the care of older people in hospitals. However, some problems limit the clinical application, such as complexity and time consuming. Therefore, by analyzing previous studies, we summarize some existing AI tools in order to find a more optimized assessment tool to complete the entire CGA process.

With the intensifying aging of the population, the phenomenon of the elderly living alone is also increasing. Therefore, using modern internet of things technology to monitor the daily behavior of the elderly in indoors is a meaningful study. Video-based action recognition tasks are easily affected by object occlusion and weak ambient light, resulting in poor recognition performance. Therefore, this paper proposes an indoor human behavior recognition method based on wireless fidelity (Wi-Fi) perception and video feature fusion by utilizing the ability of Wi-Fi signals to carry environmental information during the propagation process. This paper uses the public WiFi-based activity recognition dataset (WIAR) containing Wi-Fi channel state information and essential action videos, and then extracts video feature vectors and Wi-Fi signal feature vectors in the datasets through the two-stream convolutional neural network and standard statistical algorithms, respectively. Then the two sets of feature vectors are fused, and finally, the action classification and recognition are performed by the support vector machine (SVM). The experiments in this paper contrast experiments between the two-stream network model and the methods in this paper under three different environments. And the accuracy of action recognition after adding Wi-Fi signal feature fusion is improved by 10% on average.

With the increase in the aging population, the need for elderly care services has diversified, and smart elderly care has become an effective measure to cope with this increasing aging population. Based on the data from the platform “Guan Hu Tong” of RQ Company in the community of Shaanxi Province in western China, this study mined the data of smart elderly care services through the recency, frequency and monetary value (RFM) model and the backpropagation (BP) neural network model, constructed the user profile of the elderly, and predicted users’ practical demands. The following conclusions were drawn: The oldest users are important target users of smart elderly care service platforms; Elderly women living alone rely more on smart elderly care services; Meal delivery and health follow-up services are the most popular among elderly users.

Depression is one of the most severe mental health illnesses among senior citizens. Aiming at the low accuracy and poor interpretability of traditional prediction models, a novel interpretable depression predictive model for the elderly based on the improved sparrow search algorithm (ISSA) optimized light gradient boosting machine (LightGBM) and Shapley Additive exPlainations (SHAP) is proposed. First of all, to achieve better optimization ability and convergence speed, various strategies are used to improve SSA, including initialization population by Halton sequence, generating elite population by reverse learning and multi-sample learning strategy with linear control of step size. Then, the ISSA is applied to optimize the hyper-parameters of light gradient boosting machine (LightGBM) to improve the prediction accuracy when facing massive high-dimensional data. Finally, SHAP is used to provide global and local interpretation of the prediction model. The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.

The advantages of structural magnetic resonance imaging (sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation. However, its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause “dimensional catastrophe”. Therefore, this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation (BrainAGE) biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction, which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions, intervening at the preclinical stage.

Sodium homeostasis disorder is one of the most common abnormal symptoms of elderly patients in intensive care unit (ICU), which may lead to physiological disorders of many organs. The current prediction of serum sodium in ICU is mainly based on the subjective judgment of doctors’ experience. This study aims at this problem by studying the clinical retrospective electronic medical record data of ICU to establish a machine learning model to predict the short-term serum sodium value of ICU patients. The data set used in this study is the open-source intensive care medical information set Medical Information Mart for Intensive Care (MIMIC)-IV. The time point of serum sodium detection was selected from the ICU clinical records, and the ICU records of 25 risk factors related to serum sodium were extracted from the patients within the first 12 h for statistical analysis. A prediction model of serum sodium value within 48 h was established using a feedforward neural network, and compared with previous methods. Our research results show that the neural network learning model can predict the development of serum sodium in patients using physiological indicators recorded in clinical electronic medical records within 12 h, and has better prediction effect than the serum sodium formula and other machine learning models.

Since the outbreak and spread of corona virus disease 2019 (COVID-19), the prevalence of mental disorders, such as depression, has continued to increase. To explore the abnormal changes of brain functional connections in patients with depression, this paper proposes a depression analysis method based on brain function network (BFN). To avoid the volume conductor effect, BFN was constructed based on phase lag index (PLI). Then the indicators closely related to depression were selected from weighted BFN based on small-worldness (SW) characteristics and binarization BFN based on the minimum spanning tree (MST). Differences analysis between groups and correlation analysis between these indicators and diagnostic indicators were performed in turn. The resting state electroencephalogram (EEG) data of 24 patients with depression and 29 healthy controls (HC) was used to verify our proposed method. The results showed that compared with HC, the information processing of BFN in patients with depression decreased, and BFN showed a trend of randomization.

Interdisciplinary applications between information technology and geriatrics have been accelerated in recent years by the advancement of artificial intelligence, cloud computing, and 5G technology, among others. Meanwhile, applications developed by using the above technologies make it possible to predict the risk of age-related diseases early, which can give caregivers time to intervene and reduce the risk, potentially improving the health span of the elderly. However, the popularity of these applications is still limited for several reasons. For example, many older people are unable or unwilling to use mobile applications or devices (e.g. smartphones) because they are relatively complex operations or time-consuming for older people. In this work, we design and implement an end-to-end framework and integrate it with the WeChat platform to make it easily accessible to elders. In this work, multifactorial geriatric assessment data can be collected. Then, stacked machine learning models are trained to assess and predict the incidence of common diseases in the elderly. Experimental results show that our framework can not only provide more accurate prediction (precision:0.8713, recall:0.8212) for several common elderly diseases, but also very low time-consuming (28.6 s) within a workflow compared to some existing similar applications.

The surface electromyography (sEMG) is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion. However, limited by feature extraction and classifier selection, the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications. Moreover, due to the different characteristics of sEMG data and image data, the conventional convolutional neural network (CNN) have yet to fit sEMG signals. In this paper, a novel hybrid model combining CNN with the graph convolutional network (GCN) was constructed to improve the performance of the gesture recognition. Based on the characteristics of sEMG signal, GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal. Such strategy optimizes the structure and convolution kernel parameters of the residual network (ResNet) with the classification accuracy on the NinaPro DBl up to 90.07%. The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.

Depression has become a major health threat around the world, especially for older people, so the effective detection method for depression is a great public health challenge. Electroencephalogram (EEG) can be used as a biomarker to effectively explore depression recognition. Motivated by the studies that multiple smaller scale kernels could increase nonlinear expression compared to a larger kernel, this article proposes a model named the three-dimensional multiscale kernels convolutional neural network model for the depression disorder recognition (3DMKDR), which is a three-dimensional convolutional neural network model with multiscale convolutional kernels for depression recognition based on EEG signals. A three-dimensional structure of the EEG is built by extending one-dimensional feature sequences into a two-dimensional electrode matrix to excavate the related spatiotemporal information among electrodes and the collected electrode matrix. By the major depressive disorder (MDD) and the multi-modal open dataset for mental-disorder analysis (MODMA) datasets, the experiment shows that the accuracies of depression recognition are up to 99.86% and 98.01% in the subject-dependent experiment, and 95.80% and 82.27% in the subject-independent experiment, which are higher than alternative competitive methods. The experimental results demonstrate that the proposed 3DMKDR is potentially useful for depression recognition in older persons in the future.

Accurate carbon price forecasting is essential to provide the guidance for production and investment. Current research is mainly dependent on plenty of historical samples of carbon prices, which is impractical for the newly launched carbon market due to its short history. Based on the idea of transfer learning, this paper proposes a novel price forecasting model, which utilizes the correlation between the new and mature markets. The model is firstly pretrained on large data of mature market by gated recurrent unit algorithm, and then fine-tuned by the target market samples. An integral framework, including complexity decomposition method for data pre-processing, sample entropy for feature selection, and support vector regression for result post-processing, is provided. In the empirical analysis of new Chinese market, the root mean square error, mean absolute error, mean absolute percentage error, and determination coefficient of the model are 0.529, 0.476, 0.717% and 0.501 respectively, proving its validity.