External Devices Research Articles

Automated External Bone Fixation Robot Design and Orthopedic Analysis

Background: Bone external fixation technology is an important therapeutic approach for limb correction, primarily involving a class of external fixation correction mechanisms. It has achieved good results in limb lengthening and correcting deformities to restore limb function. In clinical practice, existing correction mechanisms face challenges such as high resistance between components, complex installation procedures, and high precision requirements for installation, requiring manual adjustment by physicians, which limits precise control and quantification of correction parameters. Objective: To address these issues, an automated correction bone external fixation robot was designed based on traditional Ilizarov external fixation devices(TIEFD). Methods: By increasing the number of unidirectional hinge connections, the robot can effectively reduce motion resistance. Subsequently, the robot was combined with the tibia for correction motion simulation, obtaining the motion parameters θ of the deformed bones during the correction process based on the trajectory of the tibia's point mass. Finally, correction motion experiments and finite element analysis(FEA) were conducted on the robot combined with a control system. Results: The results showed that the robot could precisely control correction parameters, with the error range for rotational correction not exceeding 0.5 radians and the error range for traction correction not exceeding 0.2 mm. FAE, based on corrective force data, concluded that compared to (TIEFD), the robot could reduce bone stress by 16 MPa during the correction process. Conclusion: The robot effectively reduces patient discomfort, this provides a reliable theoretical basis for bone external fixation technology and holds positive significance for the treatment of skeletal deformities.

Characterization of a conductive hydrogel@Carbon fibers electrode as a novel intraneural interface

Peripheral neural interfaces facilitate bidirectional communication between the nervous system and external devices, enabling precise control for prosthetic limbs, sensory feedback systems, and therapeutic interventions in the field of Bioelectronic Medicine. Intraneural interfaces hold great promise since they ensure high selectivity in communicating only with the desired nerve fascicles. Despite significant advancements, challenges such as chronic immune response, signal degradation over time, and lack of long-term biocompatibility remain critical considerations in the development of such devices. Here we report on the development and benchtop characterization of a novel design of an intraneural interface based on carbon fiber bundles. Carbon fibers possess low impedance, enabling enhanced signal detection and stimulation efficacy compared to traditional metal electrodes. We provided a 3D-stabilizing structure for the carbon fiber bundles made of PEDOT:PSS hydrogel, to enhance the biocompatibility between the carbon fibers and the nervous tissue. We further coated the overall bundles with a thin layer of elastomeric material to provide electrical insulation. Taken together, our results demonstrated that our electrode possesses adequate structural and electrochemical properties to ensure proper stimulation and recording of peripheral nerve fibers and a biocompatible interface with the nervous tissue.

Discussion: Comparative Study of Internal Device versus External Device in Le Fort III Distraction for Syndromic Craniosynostosis.

Discussion: Comparative Study of Internal Device versus External Device in Le Fort III Distraction for Syndromic Craniosynostosis.

Transparent Encryption for External Storage Media with Mobile-Compatible Key Management by Crypto Ciphershield

Evidently, there is a widespread adoption of external storage devices in contemporary situations, including USB sticks, SD cards, and flash memory devices. If, on the other hand, these devices are misplaced or lost, the presence of sensitive data on them can provide a potential security risk. The currently available encryption solutions frequently have usability issues, which necessitate that users continually input keys or login credentials across a variety of portable devices. The study offers a solution that overcomes these difficulties by integrating key caching with time-delayed deletion within the MobileShielded Encryption Framework (MoSEF). This method is our reaction to the problem. There are two distinct iterations of this idea that we have developed. The initial version, which functions flawlessly even in the absence of deliberate user intervention, achieves this by restricting the use of external storage to only temporary data transfers.Within the file system, the second variant makes it possible to handle numerous encryption keys for different files. This is made possible by a trusted host on the file system. By eliminating the need to share keys or passwords, timed key caching significantly improves both the level of security and the level of usability of a system. In addition to ensuring interoperability with mobile devices, our solution, which is incorporated within the MoSEF architecture, provides plaintext encryption for external storage media. To reduce the likelihood of data breaches and illegal access, this strategy offers users a mechanism that is both convenient and safe for managing sensitive data when they are on the move.

Evaluation of Different Visual Feedback Methods for Brain-Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals. BCIs based on code-modulated visual evoked potentials (cVEPs) are based on visual stimuli, thus appropriate visual feedback on the interface is crucial for an effective BCI system. Many previous studies have demonstrated that implementing visual feedback can improve information transfer rate (ITR) and reduce fatigue. This research compares a dynamic interface, where target boxes change their sizes based on detection certainty, with a threshold bar interface in a three-step cVEP speller. In this study, we found that both interfaces perform well, with slight variations in accuracy, ITR, and output characters per minute (OCM). Notably, some participants showed significant performance improvements with the dynamic interface and found it less distracting compared to the threshold bars. These results suggest that while average performance metrics are similar, the dynamic interface can provide significant benefits for certain users. This study underscores the potential for personalized interface choices to enhance BCI user experience and performance. By improving user friendliness, performance, and reducing distraction, dynamic visual feedback could optimize BCI technology for a broader range of users.

An in situ bioadhesive foam as a large intestinal delivery platform for antibody fragment to treat inflammatory bowel disease

Biologics have been widely used as injectables in the treatment of inflammatory bowel disease (IBD). Different local treatment attempts have been developed in recent years. However, maintaining systemic levels of biologics is still crucial for achieving colitis remission. An equilibrium between systemic and local concentrations of biologics is therefore essential for treatment of colitis. Current formulations struggle to create optimal balance between drug concentrations in plasma and the colonic wall. Addressing this challenge, we developed a rectally delivered in situ foam that generates CO2via a reaction between potassium bicarbonate (PB) and citric acid (CA) without the aid of an external device. An anti-TNF-α antibody fragment (Fab) was loaded into the foam formulation, which promoted prolonged colon retention and improved Fab distribution up to proximal colon following rectal administration to mice. In addition, we observed increased plasma Fab concentrations in mice receiving the rectal Fab foam compared to a Fab solution. In a non-everted rat gut ex vivo model, a single exposure to the CO2-containing foam improved macromolecule transepithelial flux across colonic tissue by over ten-fold. Foam efficacy for Fab was investigated in a range of colitis mouse models, from acute to chronic. This non-invasive formulation platform demonstrates potential to overcome existing limitations in delivering biologics to inflamed colonic tissue.

Efficient Departmental Coordination: Real-Time Schedule Display using Arduino

This project tackles the time-consuming task of schedule management for Heads of Departments (HODs) by developing a dynamic, automatic display system. Utilizing an Arduino microcontroller, an RTC module, and a P10 LED display (16x32), the system displays their one-week schedule in real-time, updating automatically each day. Bluetooth connectivity empowers adjustments to the schedule through an external device, offering flexibility and control. Utilizing a P10 LED display for clear visibility, it automatically updates with the real-time clock. Schedule information is embedded within the code for reliability, and the display format is divided for time and detailed schedule. Prioritizing eco- friendliness, it uses low-power components. This project aims to improve departmental communication, transparency, and efficiency by providing a clear and accessible platform for HOD schedule information.

Dynamic 4D facial capture pipeline with appearance driven progressive retopology based on optical flow

This paper presents a production-oriented 4D facial reconstruction pipeline designed to produce high-fidelity facial mesh sequences with a consistently structured topology, while preserving the wireframe structure specified by artists. We have designed and developed a compact, efficient, and fast optical capture system based on synchronized camera arrays for high-precision dynamic 3D facial imaging. Unlike prevailing methods that primarily concentrate on single-frame reconstruction, often reliant on labor-intensive manual annotation, our framework exploits the constraint of appearance consistency to autonomously establish feature correspondence and uphold temporal coherence within the mesh. Consequently, our approach eliminates mesh drifting and jitter, enabling full parallelization for dynamic facial expression capture. The proposed pipeline decouples the non-linear deformation of facial expressions from the rigid movements of the skull through a stable external device. Leveraging progressive retopology, our methodology employs artist-guided templates as priors, ensuring the preservation of wireframe structures across the result sequence. Progressive retopology is achieved by constraining different fine-grained features of 3D landmarks, scan surface shapes, and appearance textures. The results of our study showcase facial mesh sequences with production-quality topology, adept at faithfully reproducing character expressions from photographs while achieving artist-friendly stable facial movements.

Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components

Objective. Respiratory motion correction is beneficial in positron emission tomography (PET), as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the real time position management system) or a data driven method, such as those based on dimensionality reduction techniques (for instance principal component analysis (PCA)). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging. Approach. The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyer et al (2014)), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic [18F]-FDG study on patients with idiopathic pulmonary fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a real time position management system. Main results. The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods. Significance. This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).

Cardiac sensing at a spinal cord stimulation lead: a promising on-device potential biomarker for pain and wellbeing.

Introduction: In the search for objective measures of therapeutic outcomes for patients with spinal cord stimulation (SCS) devices, various metrics of cardiac performance have been linked to pain as well as overall health. To track such measures at home, recent studies have incorporated wearables to monitor cardiac activity over months or years. The drawbacks to wearables, such as patient compliance, would be obviated by on-device sensing that incorporates the SCS lead. This study sought to evaluate the feasibility of using SCS leads to record cardiac electrograms. Methods: The quality of signals sensed by externalized, percutaneous leads in the thoracic spine of 10 subjects at the end of their SCS trial were characterized across various electrode configurations and postures by detecting R-peaks and calculating signal-to-noise ratio (SNR). In a subset of 5 subjects, cardiac metrics were then compared to those measured simultaneously with a wearable. Results: The average signal quality was acceptable for R-peak detection (i.e., SNR > 5) for all configurations and positions across all 10 subjects, with higher signal quality achieved when recording in resting positions. Notably, the spinal lead recordings enabled more reliable beat detection compared to the wearable (n = 29 recording pairs; p < 0.001). When excluding wearable recordings with over 35% missed beats, the inter-beat intervals across devices were highly correlated (n = 22 recording pairs; Pearson correlation: R = 0.99, p < 0.001). Further comparisons in these aligned wearable and corresponding spinal-lead recordings revealed significant differences in the frequency domain metrics (i.e., absolute and normalized high and low frequency HRV power, p < 0.05), but not in time domain HRV parameters. Discussion: The ability of an implanted SCS system to record electrocardiograms, as demonstrated here, could provide the basis of automated SCS therapy by tracking potential biomarkers of the patient's overall health state without the need for additional external devices.

MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking.

Brain-computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements-open/close, finger tapping, and wrist rotation-along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.

Applications of 2D Nanomaterials in Neural Interface.

Neural interfaces are crucial conduits between neural tissues and external devices, enabling the recording and modulation of neural activity. However, with increasing demand, simple neural interfaces are no longer adequate to meet the requirements for precision, functionality, and safety. There are three main challenges in fabricating advanced neural interfaces: sensitivity, heat management, and biocompatibility. The electrical, chemical, and optical properties of 2D nanomaterials enhance the sensitivity of various types of neural interfaces, while the newly developed interfaces do not exhibit adverse reactions in terms of heat management and biocompatibility. Additionally, 2D nanomaterials can further improve the functionality of these interfaces, including magnetic resonance imaging (MRI) compatibility, stretchability, and drug delivery. In this review, we examine the recent applications of 2D nanomaterials in neural interfaces, focusing on their contributions to enhancing performance and functionality. Finally, we summarize the advantages and disadvantages of these nanomaterials, analyze the importance of biocompatibility testing for 2D nanomaterials, and propose that improving and developing composite material structures to enhance interface performance will continue to lead the forefront of this field.

ADVANCEMENTS IN BRAIN-COMPUTER INTERFACES: THE IMPACT OF AI AND GENERATIVE MODELS ON NEURAL DECODING AND APPLICATION

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices, offering transformative potential for individuals with neurological disorders and beyond. Recent advancements in artificial intelligence (AI), particularly in generative models, have significantly improved the accuracy and efficiency of neural decoding in BCIs. This paper provides a comprehensive overview of these advancements, focusing on the role of AI and generative models in enhancing BCI performance. I discuss the contributions of leading companies like Neuralink, Synchron, and Paradromics, and analyze key research findings on signal processing, machine learning algorithms, and ethical considerations. My synthesis of current literature and industry insights highlights the transformative potential of AI-driven BCIs while addressing the technical and ethical challenges that need to be overcome to fully realize their potential. I emphasize the importance of continued research in refining generative AI models, enhancing real-time processing capabilities, and establishing ethical frameworks to guide the development and application of BCIs.

Feasibility, safety, and efficacy of a novel external compression vascular closure device: The LockeT® study.

Hemostasis following large-bore femoral vein access remains a challenge. Manual compression has been the standard of care but requires bedside staff, prolonged bed rest, and longer length of stay. The LockeT is an external compression device that attempts to address these issues while achieving venous hemostasis. We evaluate postprocedural hemostasis and vascular closure outcomes after using LockeT following cardiac electrophysiologic procedures. We performed a single-center, observational study of patients who underwent vascular closure for electrophysiology procedures using LockeT. Postprocedural outcomes were subsequently analyzed. We studied 102 patients (N) for whom LockeT was used to close 182 separate vascular access sites (n). Common procedures were atrial fibrillation ablation (56.9%, N = 58) and left atrial appendage occlusion (28.4%, N = 29). Most often, 8-Fr [48.3% (n = 126)], 11-Fr [27.2% (n = 71)], and 8.5-Fr [16.9% (n = 44)] sheaths were used, with an average procedure time of 82.1 ± 29.4 min. Hemostasis was achieved in 97.8% (n = 187) of all LockeT cases. Time to ambulation and discharge were 3.93 ± 1.10 hand 8.1 ± 4.4 h, respectively. No major complications were noted. Postprocedurally, 52% (N = 53) of patients were discharged on the same day. There were no differences in hemostasis (p = .859) or ambulation times (p = .202) between procedure types. The LockeT can effectively close venous access sites with no major complications.

EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification

Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human–computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.

IEat: automatic wearable dietary monitoring with bio-impedance sensing

Diet is an inseparable part of good health, from maintaining a healthy lifestyle for the general population to supporting the treatment of patients suffering from specific diseases. Therefore it is of great significance to be able to monitor people’s dietary activity in their daily life remotely. While the traditional practices of self-reporting and retrospective analysis are often unreliable and prone to errors; sensor-based remote diet monitoring is therefore an appealing approach. In this work, we explore an atypical use of bio-impedance by leveraging its unique temporal signal patterns, which are caused by the dynamic close-loop circuit variation between a pair of electrodes due to the body-food interactions during dining activities. Specifically, we introduce iEat, a wearable impedance-sensing device for automatic dietary activity monitoring without the need for external instrumented devices such as smart utensils. By deploying a single impedance sensing channel with one electrode on each wrist, iEat can recognize food intake activities (e.g., cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. The principle is that, at idle, iEat measures only the normal body impedance between the wrist-worn electrodes; while the subject is doing the food-intake activities, new paralleled circuits will be formed through the hand, mouth, utensils, and food, leading to consequential impedance variation. To quantitatively evaluate iEat in real-life settings, a food intake experiment was conducted in an everyday table-dining environment, including 40 meals performed by ten volunteers. With a lightweight, user-independent neural network model, iEat could detect four food intake-related activities with a macro F1 score of 86.4% and classify seven types of foods with a macro F1 score of 64.2%.

Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System.

The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain-computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods.

A C-3 Clamp for Improved Connection Between Catheter Tube and Barbed Connector Together With Its Evaluation Using a Maximum Pull-Off Force Method

Abstract The majority of hospitalized patients require the insertion of a catheter as part of their medical treatment. For catheters to be connected to external devices, barbed connectors are often employed. If this connection is ever disrupted, a direct channel from the environment to the patient's internal body may be introduced, leading to a life-threatening emergency. Current methods of securing catheter tubes to connectors have various limitations that have prevented their widespread adoption in medical settings, ranging from low ease of use to poor geometric compatibility. The authors present a device, called the Catheter Connector Clamp (C-3), shown in Fig. 1 that will securely clamp multisize catheter tubes to existing barbed connectors to increase the security and repeatability of the connection. The authors also propose a method for comparing the effectiveness of current catheter connection solutions to their C-3 design using maximum pull-off force. On silicone medical tubing, the C-3 clamp performed more than twice as well as friction alone (2.09 times more), and 25% better than the hospital gold-standard taped connection.

The Contralateral Ankle Joint Is a Reliable Reference for Testing Syndesmotic Stability Using Bilateral External Torque CT.

Subtle chronic or latent instabilities are difficult to delineate with currently available diagnostic modalities and do not allow assessment of ligamentous functionality. The noninvasive bilateral external torque computed tomography (CT) was able to reliably detect syndesmotic lesions in a cadaveric study. The aim of the study was to test the external torque device in young, healthy subjects at 3 different torque levels and to demonstrate comparability with the contralateral side. Ten healthy subjects without history of injury or surgery to the ankle joint were enrolled in this cross-sectional study. Four CT scans were performed. During the scans, the lower legs and feet were placed in an external torque device with predefined external rotation torques of 0, 2.5, 5, and 7.5 Nm. Five different radiographic measures of syndesmotic stability were measured: anterior distance (AD), tibiofibular clear space (TCS), posterior distance (PD), external rotation (ER), and β angle. With increasing external torque, slight increases in AD, ER, and β angle were observed, whereas TCS and PD decreased slightly. Large absolute differences were found between the healthy subjects for all measured parameters, regardless of the external torque applied. Differences from the contralateral side using the same external torque were minimal for all parameters, but smallest for AD with a maximum difference of 0.5 mm. Using the healthy contralateral ankle joint is appropriate for assessing syndesmotic stability based on minimal intraindividual side differences using the external torque device. Side differences >0.5 mm in AD and >0.9 mm in PD may be considered abnormal and may indicate significant instability of the syndesmosis. However, future studies are needed to define definitive cutoff values for relevant side differences in acute and chronic syndesmotic instability to guide clinicians in their treatment decisions.

Integration of Photovoltaic Cells in Building Shading Devices:

This study focuses on the thermal performance simulation of the CSERS administrative building. It proposed the integration of shading elements on the south façade of the building to enhance thermal comfort for office occupants. These shading elements incorporate photovoltaic cells, displaying the potential of utilizing photovoltaic in external shading devices. The main objective of this approach is effectively address issues related to high internal temperatures and excessive solar radiation exposure. Furthermore, it ensures the preservation of key functions of the building envelope, such as thermal insulation, provision of natural lighting, and prevention of internal thermal glare. Comparative analysis is conducted between the building equipped with shading devices and the one without, with a focus on measuring the total electrical energy generated by the photovoltaic panels. Simulation programs such as SketchUp and EnergyPlus are utilized for this purpose. The results of the simulations reveal that strategically designed shading on south-facing windows leads to 17.15% reduction in annual heat gains transmitted to the building. In addition, the integration of photovoltaic shading devices demonstrates outstanding performance characteristics, contributing a productive capacity of around 5916.388 MW/h to the building. This integration effectively harnesses solar energy to improve the indoor environment of the building.