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

Older adults have age-related problems in motor performance during walking. Toe-only rocker sole shoes are one of the most common shoe modifications, facilitating forward movement and, thus, affecting mobility and stability. Due to the importance of studying inter-segmental coordination and the lack of investigation of the effect of such shoes in the literature, this study aimed to evaluate the effect of these shoes in older adults. Twenty-four participants walked on a treadmill under two conditions: standard shoes and toe-only rocker sole shoes with 20° rocker angle. The continuous and mean absolute relative phase were used as sensitive indicators to evaluate the inter-segmental coordination, whereas the coordination variability was analyzed using the parameter of deviation phase. Results indicated that these shoes could lead to a reduction in excessive movement variability, which might be advantageous for gait with ease and minimal discomfort or less pain. Statistical analysis also demonstrated a decrease in the variability of coordination patterns, which could lead to a more predictable and stable gait, consequently reducing the risk of falls. The study demonstrated that these shoes enhance gait stability and consistency in older adults, so potentially reducing fall risk as a result and offer a probable shoe choice aiding those with ankle osteoarthritis. These findings highlighted the clinical importance of footwear in managing gait dynamics and therefore preventing falls. The research suggested that proximal joint adaptations and the rocker function's limitation of joint movement were beneficial for forward movement and stability. Findings underscored the potential of shoe modifications as a simple yet effective intervention for improving older adults' mobility and safety.

To improve the fit and design efficiency of pressure-relieving insoles, this study proposes an automated rapid design method for personalized gradient pressure-relieving insoles based on foot morphology and plantar pressure. The proposed method involves three key steps: acquiring and integrating foot morphology and plantar pressure data, designing structural units and mapping relationships, and generating insole contours. The insole design relies on the Point Cloud Library. The effectiveness of the insole was validated by customizing the insoles for each subject and comparing the plantar pressure distribution during static standing and dynamic walking under three conditions: without insoles, with flat insoles, and with gradient pressure-relief insoles. Results showed that the gradient pressure-relieving insoles significantly reduced peak plantar pressure and increased the contact area compared to flat insoles. The entire design-to-production process was completed within 1 week. The findings indicate that personalized gradient pressure-relieving insoles offer effective functionality, adaptability, and a short production cycle, with potential for clinical application and healthcare advancement.

The knee resultant joint moment is a critical indicator for assessing risk during the tennis serve. Traditional methods for obtaining this metric rely on laboratory-based equipment, limiting practical application. To address this limitation, this study proposes and validates a novel method for predicting the knee resultant joint moment method using a Graph Neural Network and Gated Recurrent Unit (GNN-GRU) model. An independent GRU model was used as a baseline for comparison. Biomechanical data were collected from 30 male tennis players (age: 20.30 ± 1.66 years, height: 176.60 ± 2.74 cm, weight: 70.80 ± 3.89 kg, BMI: 22.71 ± 1.38 kg/m², training experience: 9.20 ± 2.81 years) during the performance of the tennis serve. Sagittal plane joint angles of both lower limbs were used as model inputs to predict the resultant joint moment of the supporting leg. A paired-sample t-test compared predicted and actual values. Layer-wise Relevance Propagation (LRP) was applied to quantify the contribution of individual joint angles. The GNN-GRU model demonstrated significantly better prediction performance than the standalone GRU model (p < 0.05). No significant differences were observed between predicted and actual values (p > 0.05). LRP results showed knee contribution close to 1 during the Preparation Phase (PP). In the Flight Phase (FP), ankle and hip contributions increased significantly, both approaching 1. During the Landing Phase (LP), the knee joint maintained a contribution above 0.4. This study supports the identification of potentially high-risk movements in real-world tennis training and competition and provides a reference for the early detection of knee joint injuries.

Traditional bone adaptation algorithms considering bone as isotropic, though explain bone density distribution but fail to account for the complex trabecular microarchitecture and mechanical significance of bone material characterization. This study enhances predictions of spatio-temporal adaptation of femoral trabecular structure by utilizing an orthotropic material model. A bone remodeling algorithm using finite element analysis was developed to precisely assess the element-wise material properties and its local orientation within the femur. The orthopedic simulations incorporated a multiple loading scheme reflecting a wide range of daily locomotor activities, thereby providing a more comprehensive evaluation of bone adaptation. The simulations could effectively capture the material directions, directional stiffnesses and density distributions, aligning closely with the actual morphology of the femur. Findings from the present simulations highlight the differential impact of total hip arthroplasty (THA) on peri-prosthetic bone remodeling. By integrating an orthotropic material model, this study offers profound insights into the bone remodeling processes post-THA. This approach, by capturing the directionality and complex mechanical behavior of bone, improves predictions of post-surgical bone growth and healing, contributing to improved outcomes in THA. The findings underscore the importance of considering multiple loading scenarios and patient-specific factors in predicting bone response and optimizing clinical outcomes.

The effects of vehicle-induced whole-body vibration on human body have received widespread attention, and investigations have found that vibrations would cause lumbar disorders of occupational drivers. Some investigations have revealed the harmful effects of vibrations on human body, but the studies about the effect of vibration applied to different body regions on lumbar spine are limited. In this study, a whole-body finite element model was used to predict the biomechanical response of lumbar spine under vibrations applied to the back of thoracic and lumbar regions, and the bottom of buttock regions, respectively. The results showed that vibrations applied to the thoracic region would cause more intense spine motions, but the vibration applied to the lumbar region would cause higher internal forces, which might cause more injuries. The stress of lumbar spine would tend to increase after superimposed vertical vibration, and this increase might even more significant when the superimposed vibrations applied to the thoracic and buttock regions.

Cardiovascular diseases remain the leading cause of mortality worldwide, primarily resulting from the narrowing or blockage of blood vessels. Coronary artery bypass grafting (CABG) is a common surgical intervention that restores blood flow to the heart by using alternative vessels as grafts. Although saphenous vein grafts (SVGs) are frequently utilized in these procedures, they are prone to re-occlusion within 10 years, largely due to intimal hyperplasia and the development of atherosclerosis. In contrast, grafts using the mammary artery (MA) and radial artery demonstrate significantly better long-term patency and are less susceptible to occlusion. Mechanical characterization, numerical simulation, and artificial intelligence models are becoming essential to enhance surgical planning and outcomes. These digital tools provide predictive insights on intimal thickening and restenosis from medical images, thereby assisting surgeons in making well-informed decisions. This review explores the various types of grafts and the latest research in this field, focusing on graft materials, their mechanical properties, computational techniques, artificial intelligence models related to bypass surgery, and the resulting clinical implications. By highlighting the limitations of current methodologies, this review underscores the critical need for the research community to develop more advanced tools to optimize grafting outcomes.

Children, while playing cricket, may get exposed to impact-related injuries. Most available studies have been done on adult players. Pediatric biomechanics concerning cricket ball impacts have been unexamined, supported by assumptions and surrogate models. This research used a finite element model of a child dummy, a ball, and a steel chair. Four conditions based on anatomical regions: (A) Anterior precordial, (B) Subnasal, (C) Right temporal, and (D) Cranial vertex impact were simulated by impacting a cricket ball at 5, 10, or 15 m/s. Injury indicators (Cervical Spine Injury Criterion, Thoracic Compression Criteria, neck loads and bending moments, Neck Injury Criteria, head displacement, velocity and acceleration, and head injury criteria) were computed for each simulation. Von Mises stresses were computed on the skin. Computed values of indicators were compared with standard data to predict injury levels (AIS values) at different head, neck, and chest. At 15 m/s, thoracic compression attained 49.59 mm, exceeding the AIS 4 criteria for severe damage. The HIC¹⁵ value of 297.7 at 10 m/s implies mild concussions; however, the peak head acceleration of 135.82 g signifies a considerable danger of brain injury. Subnasal impacts at 10 m/s exhibited N_ij = 0.72, indicating cervical ligament tension. Research indicates elevated stress concentrations at impact locations with increased impact velocity. This study provides a comprehensive quantitative data into pediatric impact biomechanics during cricket play. This study identifies key anatomical vulnerabilities that can inform the design of improved protective gear and support the development of safer play conditions for young athletes.

Predicting human upper extremity motion in three-dimensional (3D) collision avoidance tasks involves integrating biomechanical constraints and cognitive perceived risk into an optimization-based motion prediction framework. The proposed model uses Bayesian Decision Theory to represent human perceived risk stochastically, providing a comprehensive approach to digital human modeling in manual reaching tasks with 3D obstacle collision avoidance. This paper presents an optimization formulation to predict and validate a reaching motion with collision avoidance. First, experimental data was collected in which subjects performed manual reaching tasks involving three distinct 3D obstacles with varying shapes, orientations, and materials. Then, a perceived risk-based 3D collision avoidance model is investigated. Design variables are control points of B-Spline curves representing joint angles for the optimization formulation. The objective function minimizes the joint displacement function and maximizes the end-effector velocity. Constraints include the initial and final postures, joint ranges of motion, upper extremity location related to the experimental setup, and perceived risk-related constraints. The optimization-based framework without perceived risk initially determined the optimal clearance distance, providing a baseline for modeling human motion. This paper modified this baseline through the perceived-risk 3D collision avoidance algorithm to incorporate cognitive factors. Results showed significant improvement in predicting minimum clearance distances when considering perceived risk. For instance, moving around a fragile object caused greater clearance distances, reflecting participants' cautious behavior. The study validated the prediction method by comparing joint angle profiles between experiments and simulations. This work advances digital human modeling by incorporating perceived risk into motion prediction algorithms, moving beyond the traditional reliance on artificial contact spheres. Applications span ergonomics, rehabilitation, and human-robot interaction, offering insights into workspace design, safety, and efficiency. Future research could explore multi-obstacle scenarios, dynamic environments, and alternative loss functions to further refine the model's predictive capabilities.

The development of innovative small bone replacements for the human wrist has been partially limited by the lack of a suitable preclinical animal model. This study explores the feasibility of using the Yucatan minipig (YMP) as a preclinical model for small bone replacement. Implants for the radial carpal bone (RCB), homologous to the human scaphoid, were developed for a pilot in vivo animal study. RCB size (volume, bounding box dimensions) was quantified (n = 35), and relationships between animal age, weight, and RCB volume were investigated. Bounding box dimensions were also analyzed relative to RCB volume. A mean-shaped RCB model was generated using ShapeWorks Studio and scaled to create a set of implants. These implants were evaluated in a pilot in vivo study, where the distances between the explanted bone surface and both the predicted and surgeon-selected implant surfaces were recorded for each animal. Predicted implant distances (0.8 ± 0.2 mm), were larger (p < 0.001) than surgeon-selected implant distances (0.4 ± 0.1 mm) in three animals. In one animal, the predicted implant distances (0.3 ± 0.2 mm) were smaller (p < 0.0001) than the surgeon-selected implant distances (0.5 ± 0.3 mm). The set of implants generated provided the surgeon with options suitable for the range of animals in the in vivo study. This study presents a novel approach to generating small bone replacements by scaling a mean-shaped bone in a porcine model and further evaluates the YMP as a preclinical model for small bone replacement in the human wrist.

The domains of biomedical engineering and biosensors have been revolutionized by additive manufacturing, commonly referred to as 3D printing. This study delves into the various ways additive printing has been used in these fields, showcasing its role in creating biosensor parts, implants, tissue engineering scaffolds, and medical equipment. The production of typical prosthetics and implants has been revolutionized by additive manufacturing. It allows for the manufacture of complicated geometries with a high degree of precision and personalization, which in turn improves clinical results and patient care. Additionally, innovative diagnostic tools have been developed through the combination of biosensor technologies with additive manufacturing. These tools can identify infections, physiological factors, and biomarkers with a level of sensitivity and specificity that has never been seen before. This review discusses the present and prospects of additive manufacturing for biomedical and biosensor applications, as well as the state-of-the-art techniques and materials used in this sector. It also analyzes the ongoing obstacles in this area. Additive manufacturing has enormous potential to transform bio sensing technology and healthcare delivery, opening the door to novel approaches to difficult medical problems.