Volume 2, Issue 4

Objectives: To investigate how impedance values and sensory thresholds at various human upper limb sites af fect the parameter settings of electrical stimulation equipment in low and medium frequency envelope electrical stimulation therapy. Methods: The study involved testing different upper limb sites on 22 healthy subjects (ages 21-25, 11 males and 11 females) by adjusting the modulation wave frequency, carrier frequency, and current in tensity of the output. Five types of electrodes of various sizes were used in the tests. Results: The impedance test results for the human upper limb showed a wide range of impedance values across electrodes of different sizes. A new impedance model of the human upper limb was proposed, which accurately fits the relationship between frequency and impedance values. In electrical stimulus sensory experiments, the voltage perception threshold (VPT) introduced in this study was identified as a novel metric for electrical stimulus sensation. Unlike the current perception threshold, VPT does not consider the effects of current magnitude and output frequency. The range of sensory thresholds was 6-8 V, while the suprathreshold was 9-11 V. Neither experiment showed gender differenc es. Conclusions: Determining the value of the power supply and the output intensity of device power amplification circuitry based on the VPT can provide a more precise therapeutic dose for electrical stimulation therapy.

To enhance the adaptive learning, self-organization, and fault tolerance capabilities of gait pattern recognition in intelligent knee prostheses, an online walking pattern recognition method based on the convolutional neural networks (CNN)-long short term memory (LSTM) model is proposed. Five test subjects wore the intelligent knee prostheses and performed four walking modes: level walking, uphill walking, downhill walking, and stair descent. The preprocessed gait data were fed into four neural network models: CNN, LSTM, CNN-LSTM, and CNN-bidirectional LSTM. Through hyperparameter tuning, the recognition accuracy of these models was compared. Real-time indicator, gait recognition delay, was also measured. Experimental results showed each model had its strengths and weaknesses. Overall, the CNN-LSTM model achieved the best recognition performance with accuracy rates of: level walking 89%±2.5%, uphill 72.8%±3.2%, downhill 71%±3.2%, and stair descent 96%±2.5%. When switching from level walking to downhill, gait recognition delay was 51.7%±15.6%, and vice versa it was 75.8%±11.5%; when switching from level walking to stair descent, gait recognition delay was 47.1%±17.1%, and vice versa it was 38.6%±10.5%. In summary, the application of the CNN-LSTM model for walking pattern recognition in unilateral intelligent knee prostheses is feasible, with accuracy and real-time performance meeting the control requirements of the prostheses.

The Visual-inertial navigation system (VINS) integrates visual and inertial sensors, providing advanced navigation capabilities. Recent improvements in these sensors have made VINS widely applicable in areas such as robotic navigation and autonomous driving, due to its complementary functionality and decreasing sensor costs. This paper reviews the latest developments in VINS, introducing visual simultaneous localization and mapping and its role in VINS. Key technologies, including direct and indirect methods for image processing in visual odometry and Inertial Measurement Unit preintegration for robot motion estimation, are discussed. Both filter-based and optimization-based state estimation methods in VINS are examined. Filter-based methods, like the Extended Kalman Filter, offer real-time state updates for attitude tracking and map construction, while optimization-based methods focus on minimizing reprojection or other error metrics to improve localization accuracy and robustness. The application of dynamic simultaneous localization and mapping, which addresses dynamic objects in complex environments, is also explored. This paper summarizes current research challenges and proposes future directions in the field of dynamic simultaneous localization and mapping.

In recent years, gait prediction has gradually become a cutting-edge research direction in the fields of biomechanics and artificial intelligence. Gait prediction technology, which analyzes an individual’s walking patterns to predict future changes, is crucial for the precision of rehabilitation and exoskeleton robot control. This paper reviews the recent research progress in the field of gait prediction, focusing on the multimodal information acquisition methods based on physical sensors and bioelectric signals, as well as the application of machine learning and deep learning algorithms in gait prediction. By analyzing different sensor data fusion strategies, the importance of multimodal information fusion for improving the accuracy of gait prediction is emphasized. Furthermore, this paper introduces the performance of traditional machine learning algorithms such as Support Vector Machine, Random Forest, and Back Propagation Neural Network, as well as deep learning models such as Long Short-Term Memory, Convolutional Neural Network, and Transformer in gait prediction, highlighting the advantages of deep learning in feature extraction and adaptability to complex scenarios. Finally, this paper explores future directions for the development of gait prediction technology, emphasizing improvements in timeliness, accuracy, and personalization to advance exoskeleton robotics and related fields.

In the field of bioanalysis, the integration of magnetic beads and biosensors provides a protein detection platform with high separation efficiency and sensitivity. The superparamagnetism of magnetic beads, combined with surface functional modifications, forms the basis for selectively capturing and effectively separating target proteins. Additionally, the high sensitivity and specificity of biosensors ensure precise quantitative analysis of captured proteins. This article systematically reviews the synthesis strategies of functionalized magnetic beads, detection methods for proteins and nucleic acids, as well as the current technical challenges and future development directions.

Finite element analysis (FEA) is a computer-aided tool widely employed in the field of prosthodontics, offering a comprehensive understanding of biomechanical behavior and assisting in the design and evaluation of dental prostheses. By dividing a model into finite elements, FEA enables accurate predictions of stress, strain, and displacement of structures. This review summarizes recent research developments in the application of FEA across various aspects of prosthodontics, including dental implant, removable partial denture, fixed partial denture and their combinations. FEA plays a significant role in selecting restoration materials, optimizing prosthetic designs, and examining the dynamic interactions between prostheses and natural teeth. Its computational efficiency and accuracy have expanded its application potentials for preoperative planning in custom-made prosthodontics. Upon the physician’s assessment of the repair requirements tailored to the individual patient’s condition, FEA can be employed to evaluate the stress distribution, displacement, and other relevant outcomes associated with the proposed restoration. When integrated with clinical expertise, it facilitates assessing design feasibility, identifying necessary adjustments, and optimizing prosthetic solutions to mitigate the risk of failure. Additionally, FEA helps identify potential complications arising from long-term prosthetics use, allowing for the implementation of preventive strategies. Presenting FEA results to patients enhances their understanding of the scientific basis and rationale behind the design, thereby bolstering patient confidence in the proposed intervention. Despite its ongoing limitations, FEA underscores the importance of integrating computational findings with clinical judgment and supplementary diagnostic tools. This review emphasizes the growing role of FEA in advancing prosthodontics by offering computational analysis and design optimization, ultimately improving treatment outcomes and patient satisfaction.