DOI：https://doi.org/10.61189/961030gznunx

Received June 21, 2024; Accepted November 20, 2024; Published December 31, 2024

Highlights

● In prosthetics, using AI algorithms to identify the fused sensor data as known walking patterns has extremely strong expandability. Moreover, as the learning data continues to expand, the robustness of the model itself also increases accordingly.
● There are numerous AI algorithms currently available. The effective utilization of algorithm combination techniques to learn from each other’s strengths can significantly improve the accuracy of identification. The combined model of convolutional neural networks (CNN) and bidirectional long short term memory (LSTM) attempted in this paper has witnessed a significant improvement in its comprehensive recognition rate.
● In the practical application of prosthetics, the real-time performance during the mode switching transition period is particularly important as it can reflect the flexibility of the prosthetics. In this paper, the algorithm optimized by the AI model has controlled the delay rate within one gait cycle, greatly enhancing the safety and reliability of pro-sthetics in actual use.

DOI: https://doi.org/10.61189/673672yizrwd

Received September 8, 2024; Accepted November 6, 2024; Published December 31,2024

Highlights

●Gait prediction relies on multimodal sensor data, and the acquisition of multimodal information, such as physical sensors and bioelectrical signal sensors, is introduced in order to monitor and analyze the lower limb movement in real time, and provide a data basis for prediction.
● The application of machine learning algorithms in gait prediction technology, such as Support Vector Machine, Random Forest, and Back Propagation Neural Network, is reviewed to construct an optimized gait prediction model, which provides effective support for the intelligent control of exoskeleton.
● Compared with machine learning algorithms, the article summarizes the researchers’ efforts to extract and un derstand the hidden patterns in gait data by constructing neural network models related to different deep learning algorithms, which are used to improve the accuracy and robustness of gait prediction.

DOI: https://doi.org/10.61189/403384jfzmyx

Highlights

● In the field of bioanalysis, a new biosensor technology based on functionalized magnetic beads is leading a new direction in protein detection. With its excellent separation efficiency and sensitivity, it provides a powerful tool for early disease diagnosis and biomarker monitoring.
● This article explores the latest advancements in this technology, including innovative magnetic bead designs, diverse detection strategies, and the technical challenges and future development directions. It reveals the potential and application prospects of biosensor technology in biomarker detection.

DOI：https://doi.org/10.61189/974215qcjfzk

Highlights

● This paper presents a comprehensive review of the advancements in finite element analysis (FEA) within the field of prosthodontics over the past five years.
● It examines the role of FEA in aiding the selection of restorative materials, enhancing prosthetic designs, and in vestigating the dynamic interactions between prostheses and natural dentition.
● Integrating FEA findings with clinical practice enhances treatment outcomes and patient satisfaction.