Endless possibilities in academia
Endless possibilities in academia
Yibin Zhang1, Yan Wang1, Hongliu Yu2
1School of Medical Devices, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China. 2School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Address correspondence to: Hongliu Yu, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai city Jungong road 516, Shanghai 200093, China. E-mail: yhl98@hotmail.com.
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.
Research Article |Published on: 31 December 2024
[Progress in Medical Devices] 2024; 2 (4): 144-152
Haonan Geng1, Xudong Guo1, Haibo Lin1, Youguo Hao2, Guojie Zhang3
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Shanghai Putuo District People’s Hospital, Shanghai 200060, China. 3LingYuan Iron and Steel CO., LTD, Lingyuan 122500, Liaoning Province, China.
Address correspondence to: Xudong Guo, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Yangpu District, Shanghai 200093, China. Email: guoxd@usst.edu.cn; Youguo Hao, Shanghai Putuo District Central Hospital, No.1291 Jiangning Road, Putuo District, Shanghai, 200060, China. Email: youguohao6@163.com.
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.
Review Article |Published on: 31 December 2024
[Progress in Medical Devices] 2024; 2 (4): 161-173
Haoyuan Su1, Yuehua Liao2, Shu Wu1, Jun Ji1, Shuya An1, Dongdong Zeng2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2School of Medical device, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
Address correspondence to: Dongdong Zeng, School of Medical device, Shanghai University of Medicine & Health Sciences, No. 268 Zhouzhu Highway, Pudong, Shanghai 201318, China. E-mail: zengdd@sumhs.edu.cn.
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.
Review Article |Published on: 31 December 2024
[Progress in Medical Devices] 2024; 2 (4): 174-186
Yan Wang, Liwen Chen
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Address correspondence to: Liwen Chen, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Yangpu District, Shanghai 200093, China. E-mail: chenlw@usst.edu.cn.
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.
Review Article |Published on: 31 December 2024
[Progress in Medical Devices] 2024; 2 (4): 187-202
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