Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine最新文献

Physical positions and lumbar movements are directly related to lumbar disorders. It is known that the sagittal plane angle affects the person's ability to apply extension torque. However, there is no consensus on whether or not muscle activity and co-contractions change at these angles. This paper aimed to investigate the abdominal and lumbar muscles' behavior at different sagittal plane angles during maximum voluntary isometric extension (MVIE). We have evaluated our findings with the aid of a computational biomechanical model. Fourteen healthy males participated. A total of 16 muscles EMG were recorded during the lumbar MVIE on the Sharif Lumbar Isometric Strength Tester device in 5°, 15°, 30°, and 45° flexion. The torque and muscle activity changes and all co-contraction indexes (CCI) between 120 possible muscle pairs were calculated. Finally, the experimental test conditions were modeled in the AnyBody software, and the MVIE torque, muscle activity, and all CCIs were calculated. Also, muscle torque lever arms were calculated at different angles. Results show that MVIE at four angles is 137.94 ± 36.08, 148.63 ± 47.96, 168.09 ± 50.48, and 171.44 ± 53.95 N · m, respectively. Muscle activity and CCI are similar at all angles. The AnyBody model gives similar findings. Muscles torque lever arms change with angle. In conclusion, to determine the safety mode of lifting in the sagittal plane, it seems that the torque differences are due to changes in the geometrical muscle parameters (including the torque lever arm). Despite the almost constant muscular effort, subjects in the 30°-45° bending positions can apply more MVIE.

This study focuses on novel design and evaluation of Elastic 50A (EL50) mechanical metamaterials with open-cell patterns for its potential application to lower limb residuum/socket interfaces, specifically that of a transtibial (TT) amputee. Mechanical characteristics, that is, effective Young's modulus (E), was tuned by altering metamaterial porosity, which was experimentally verified. Specifically, pore radius of the unit cell was varied to achieve a range of E-values (0.05-1.71 MPa) for these 3D printed metamaterials. Finite Element Analysis (FEA) was conducted to evaluate pressure distribution across key load-bearing anatomical sites of a TT residuum. Using designed metamaterials for homogeneous liners, pressure profiles were studied and compared with a silicone liner case. Additionally, a custom metamaterial liner was designed by assigning appropriate metamaterials to four load-sensitive and tolerant anatomical sites of the TT residuum. The results suggest that lowest pressure variation (PV), as a measure of pressure distribution levels and potential comfort for amputees, was achieved by the custom metamaterial liner compared to any of the homogeneous liners included in this study. It is envisaged that this work may aid future design and development of custom liners using now commonly available 3D printing technologies and available elastomer materials to maximise comfort, tissue safety and overall rehabilitation outcomes for lower limb amputees.

Electromyography (EMG) signals are used for many different purposes, such as recording and measuring the electrical activity generated by varying the body's skeletal muscles. Biosignals are different types of biomedical signals, like EMG signals, which can be used for the neural linkage with computers and are obtained from a particular part of the body such as tissue, muscle, organ, or cell system like the nervous system. Surface electromyography (SEMG) is a non-invasive method that can be used as an effective system for controlling upper arm prostheses. This study focused on classifying the five types of distinct finger movements investigated in four unique channels.We have used a classification technique, the k-nearest neighbors (KNN), to categorize the collected samples. Two time-domain features, (a) maximum (Max) and (b) minimum (Min), were used with one of these three features separately: mean absolute value (MAV), root mean square (RMS), and simple square integral (SSI) to classify gestures. We chose classification accuracy as a criterion for evaluating the effectiveness of every classification. We figured out that the first grouping, that is, (MAV, Max, Min), was the best choice for classification. The accuracy percentage in the four channels for the first group was 91.0%, 89.9%, 89.8%, and 96.0%, respectively.

Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.

Ankle arthrodesis is the gold standard for treatment of end-stage arthritis. The goal of ankle arthrodesis is to obtain bony union between the tibia and the talus. Retrograde intramedullary nailing is typically reserved for ankle and subtalar joints arthrodesis. The purpose of this study is to evaluate the effect of two different materials, two locking pin configurations and two nail designs of a retrograde locked intramedullary nail used for ankle arthrodesis. Using the finite element analysis, a numerical study of ankle arthrodesis was developed to evaluate the effect of materials: TI-6Al-4V and stainless steel AISI 316 LVM; two locking pin configurations: five and six pins, on two intramedullary nails: Ø10 × 180 mm and Ø11 × 200 mm. A model of a healthy foot was created from tomographic scans. It was found that the mechanical stimulus required to achieve bone fusion were higher for Ø10 × 180 nails (6.868 ± 0.047) than the Ø11 × 200 nails (5.918 ± 0.047; p < 0.001; mean ± SEM). We also found that six-pin configuration had a higher mechanical stimulus (6.470 ± 0.047) than the five-pin configuration (6.316 ± 0.046; p = 0.020). Similarly, it was higher for titanium (6.802 ± 0.047) than those for stainless steel (5.984 ± 0.046; p < 0.001). Finally, the subtalar zone presented higher values (7.132 ± 0.043) than the tibiotalar zone (5.653 ± 0.050; p < 0.001). The highest mechanical stimulus around the vicinity of tibiotalar and subtalar joint was obtained by Ø10 × 180 nails, made of titanium alloy, with 6P.

A research work was undergone in a virtual bone reduction process for reconstruction of the comminuted pelvic bone fracture using a CT scan dataset of patients. This includes segmentation, 3D model optimization and bone registration technique. The accuracy of the reconstructed bone model was validated using Finite Element Method. Analysed and applied various segmentation techniques to segregate the injured bone structure. The ICP (Iterative Closest Point), Procrustes algorithm and Canny edge detection algorithm were applied to understand the bone registration process for surgery in detail. The average RMS error, mean absolute distance, mean absolute deviation, and mean signed distance of the reconstructed bone model using proposed algorithms involving 10 patient datasets in a group were found to be 1.77, 1.48, 1.51 and -0.31 mm respectively. The calculated RMS error value proved minimal error in semi-automatic registration than other existing automatic registration techniques. Therefore, the proposed approach is suitable for virtual bone reduction for comminuted pelvic bone fracture. This method could also be implemented for various other bone fracture reconstruction requirements.

Exposure to excessive whole-body vibration is linked to health issues and may result in increased rates of mortality and morbidity in infants. Newborn infants requiring specialized treatment at neonatal intensive care units often require transportation by road ambulance to specialized care centers, exposing the infants to potentially harmful vibration and noise. A standardized Neonatal Patient Transport System (NPTS) has been deployed in Ontario, Canada, that provides life saving equipment to patients and safe operation for the clinical care staff. However, there is evidence that suggests patients may experience a higher amplitude of vibration at certain frequencies when compared with the vehicle vibration. In a multi-year collaborative project, we seek to create a standardized test procedure to evaluate the levels of vibration and the effectiveness of mitigation strategies. Previous studies have looked at laboratory vibration testing of a transport system or transport incubator and were limited to single degree of freedom excitation, neglecting the combined effects of rotational motion. This study considers laboratory testing of a full vehicle and patient transport system on an MTS Model 320 Tire-Coupled Road Simulator. The simulation of road profiles and discrete events on a tire-coupled road simulator allows for the evaluation of the vibration levels of the transport system and the exploration of mitigation strategies in a controlled setting. The tire-coupled simulator can excite six degrees-of-freedom motion of the transport system for vibration evaluation in three orthogonal directions including the contributions of the three rotational degrees of freedom. The vibration data measured on the transport system during the tire-coupled testing are compared to corresponding road test data to assess the accuracy of the vibration environment replication. Three runs of the same drive file were conducted during the laboratory testing, allowing the identification of anomalies and evaluation of the repeatability. The tire-coupled full vehicle testing revealed a high level of accuracy in re-creating the road sections and synthesized random profiles. The simulation of high amplitude discrete events, such as speed hump traverses, were highly repeatable, yet yielded less accurate results with respect to the peak amplitudes at the patient. The resulting accelerations collected at the input to the manikin (sensor located under the mattress) matched well between the real-world and road simulator. The sensors used during testing included series 3741B uni-axial and series 356A01 tri-axial accelerometers by PCB Piezotronics. These results indicate a tire-coupled road simulator can be used to accurately evaluate vibration levels and assess the benefits of future mitigation strategies in a controlled setting with a high level of repeatability.

The socket of a transtibial prosthesis is a structural part customized to a patient's amputated residual lower limb. The free-form geometry of the socket can be suitable for additive manufacturing (AM) to save time and cost. However, the mechanical fracture of additively manufactured lower limb prostheses is not yet fully understood. A novel experimental method and numerical approach by finite element method (FEM) to test the strength and fracture behavior of a lower limb prosthetic socket of acrylonitrile butadiene styrene (ABS), reverse-engineered using computer-aided design (CAD) from the actual amputee's residual limb and manufactured using fused filament fabrication (FFF) are proposed in the present work. The mechanical behavior, von Mises stress distribution, and the damage status of layered AM sockets of different thicknesses were simulated by FEM using Hashin's transversely isotropic mechanical damage model, initially developed for composite materials. The experimental work showed that the fracture failure initiated at the corner of the lobe in the 4 mm thickness socket at a failure load of 918.5 N. The FEM results predicted this failure load to be 896.6 N, with only a 2.45% error as compared to the experiment. The failure loads predicted by FEM in the sockets with thicknesses of 3, 5, and 6 mm were 618.1, 1008.6, and 1105.2 N, respectively. The present work provides a dependable method for testing a below-knee prosthetic socket against static failure and arriving at a factor-of-safety (FoS) based socket thickness selection for any amputee.

Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.