The early prediction of Alzheimer’s disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT). Time–frequency features were extracted using the Multitaper method, followed by multidimensional reduction techniques. Subspace features (F24 and F216) were selected via t-tests and False Discovery Rate (FDR) multiple comparisons correction, and subsequently analyzed in the Time–Frequency Area Average Test (TFAAT) and Prefrontal Beta Time Series Test (PBTST). The experimental results reveal that the MSIT task achieves optimal cross-subject classification performance using the Support Vector Machine (SVM) approach with the TFAAT feature set, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 58%. Similarly, the Sternberg Memory Task demonstrates classification ability with the logistic regression model applied to the PBTST feature set, emphasizing the beta band power spectrum in the prefrontal cortex as a potential marker of AD risk. These findings confirm that task-state EEG provides stronger classification potential compared to resting-state EEG, offering valuable insights for advancing early AD prediction research.

Wide-range application scenarios, such as industrial, medical, rescue, etc., are in various demand for human spatial positioning technology. However, the existing MEMS-based sensor positioning methods have many problems, such as large accuracy errors, poor real-time performance and a single scene. We focused on improving the accuracy of IMU-based both feet localization and path tracing, and analyzed three traditional methods. In this paper, a planar spatial human positioning method based on high-resolution pressure insoles and IMU sensors was improved, and a real-time position compensation method for walking modes was proposed. To validate the improved method, we added two high-resolution pressure insoles to our self-developed motion capture system with a wireless sensor network (WSN) system consisting of 12 IMUs. By multi-sensor data fusion, we implemented dynamic recognition and automatic matching of compensation values for five walking modes, with real-time spatial-position calculation of the touchdown foot, enhancing the 3D accuracy of its practical positioning. Finally, we compared the proposed algorithm with three old methods by statistical analysis of multiple sets of experimental data. The experimental results show that this method has higher positioning accuracy in real-time indoor positioning and path-tracking tasks. The methodology can have more extensive and effective applications in the future.

Contact measurement technology is commonly used for analysis and modeling of three-dimensional (3D) object. This paper presents a contact measurement method, using a claw type probe based on a set of rotary encoders that can flexibly contact objects with efficiently multi-lines scanning. In this paper, a 3D measurement system by flexible contact of multiple fingers was built. The claw probe was set on a coordinate measurement machine. By moving the position of the claw for interpolation detection to improve the sampling rate, higher precision 3D reconstruction was achieved. Also, a compensation algorithm was proposed for this type detector to improve measuring accuracy. In the experiments, two surface models were produced by 3D printing as the test objects. By comparing the designed surface with the reconstructed surface, it is demonstrated that this system can effectively and softly measure smooth surface, like the back of human body, which is valuable for further study and use in rehabilitation massage, apparel design, and etc.

Accurate measurement and characterization of geometry-dependent forming errors are essential for closed-loop knead forming deformation manufacturing. To improve the forming accuracy under volume conservation, a maximal inscribed constraint-driven radial expansion method (MIC-REM) is proposed for stable shape mapping and geometric compensation. Repeatability and closed-loop compensation experiments were conducted for five representative geometries based on point cloud registration and RMSE evaluation. The results show that the forming error exhibits good repeatability and is mainly dominated by systematic factors rather than random fluctuations. The proposed compensation method reduces geometric errors by 35%--72%, while its effectiveness shows clear geometry dependence, with limited improvement for geometries involving localized large deformation. Further analysis reveals that the forming error is strongly correlated with layer-wise deformation behavior. A process-constrained geometry-dependent error model is established, representing the layer-wise error as a quadratic function of cross-sectional deformation. Under identical process conditions, the model provides a unified formulation across different geometries through parameter variations. This study provides a unified framework for geometric error measurement, modeling, and closed-loop compensation in knead forming, and reveals the geometry-dependent mechanism of forming error.