Duncan E. T. Shepherd

Sit-to-stand movement is the prerequisite for other lower limb movements and determines the independent living ability of lower limb amputees. Although optimal control theory enables human motion prediction without experimental data, existing studies on sit-to-stand prediction for lower limb amputees have neither focused on transtibial amputees nor considered buttocks-seat contact. To address these limitations, this study aims to develop an optimal control-based digital computational framework for predicting the entire cycle sit-to-stand movement of transtibial amputees while analysing the biomechanical effect of prosthesis stiffness changes. The motion prediction was formulated as an optimal control problem, where the system dynamic constraints were incorporated into a biomechanical model of the unilateral transtibial amputee and the smooth Hunt-Crossley contact force model. To overcome the challenge of difficult to satisfy static equilibrium constraints at the initial and final time points of motion, a novel "free-fall method" inspired by natural motion was proposed to determine the vertical translation and pitch angle of the human model. Combining the above innovative methods with the numerically efficient direct collocation method, a digital computational framework for predicting the entire cycle sit-to-stand motion of unilateral transtibial amputees was established. After being validated by experimental data, the proposed framework was then used to analyse the effect of changes in prosthetic stiffness on the biomechanical behaviour of amputees. The predicted lower limb joint angles, ground reaction forces, and seat reaction force successfully capture key experimental features. Furthermore, the effects of changes in prosthetic stiffness on dynamic behaviour were revealed, fully demonstrating the unique advantage of predictive simulation that does not require experimental data. Our approach has great potential in multiple fields such as the design of prostheses and rehabilitation strategies, as well as motion planning for humanoid robots.

A biodegradable ultrasonically welded device has for the first time been developed for in-body sutures that eliminates the need for surgical knotting. The device comprises two parts that fit together, with a suture inserted between them. Ultrasonic welding is then used to secure the suture by welding the two parts together. The device was manufactured from three biodegradable polymers: Poly(L-lactide-co-D,L-lactide) [PLDLA]; Poly(L-lactide-co-glycolide) [PLGA]; Poly(L-lactide) [PLLA]. All devices were degraded through immersion in phosphate buffer solution at a temperature of 37°C ± 2°C. Knotted sutures on their own were also subject to degradation testing. The devices and knotted sutures were mechanically tested at week zero and after 1, 3 and 6 weeks of degradation. Mechanically testing was undertaken to measure the pull-out strength of sutures from the device. PLGA is not suitable for the device, where a significant reduction in failure force was seen after 3 weeks of degradation. By week 6 the mean failure force (±SD) for PLGA was 74.9 ± 23.4 N, which was significantly less than the use of a suture knot on its own, with a mean failure force of 153.2 ± 37.2 N. PLDLA and PLLA were found to be promising materials, with only a small reduction in mean failure force after 6 weeks of degradation. At week 6 there was no significant difference between the mean failure force of PLDLA, PLLA or the suture knot, with mean failure forces of 152.6 ± 15.0, 128.8 ± 35.0 and 153.2 ± 37.2 N, respectively.

Background: Tibiofemoral contact mechanics (TFCM) is an accepted biomechanical metrics for evaluating the meniscus in its intact, torn, and repaired states. Pressure sensors are increasingly used, with accuracy and repeatability influenced by test conditions, their design, and their properties. To identify factors optimising performance, we performed a systematic review of the literature on their use for measuring TFCM in posterior meniscal root tears. Methods: The Cochrane Controlled Register of Trials, PubMed, and Embase were used to perform a systematic review using the PRISMA criteria. As laboratory and surgical setup can influence sensor performance, we collected data on specimen preparation, repair techniques, hardware use, and biomechanical testing parameters. Results: 24 biomechanical studies were included. Specimen preparations were similar across studies with respect to femoral and tibial mounting. Single axial compressive forces were applied between 100 and 1800 N at varying flexion angles (0–90°). Tekscan (Boston, MA, USA) was the commonest sensor used to measure TFCM, followed by digital capacitive sensors and Fujifilm (Tokyo, Japan). Factors influencing their performance included fluid exposure, lack of adequate fixation, non-specific calibration protocols, load saturation exceeding calibration, damaged sensels and inappropriate pre-test conditioning. Conclusions: Understanding potential factors influencing pressure sensors may improve accuracy, area, and pressure distribution measurements.

The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally, we hypothesised that a stacked long short-term memory (LSTM) neural network architecture can perform the task without relying on any ahead-of-motion features typically provided by electromyography signals. Optical cameras and inertial measurement unit (IMU) sensors were used to track level gait kinematics. Joint angles were modelled using the musculoskeletal model. The optimal LSTM architecture in fulfilling the prediction task was determined. Joint angle predictions were performed for joints on the sagittal plane, benefiting from joint angle modelling using signals from optical cameras and IMU sensors. Our proposed method predicted the upcoming joint angles in the prediction time of 10 ms, with an averaged root mean square error of 5.3° and a coefficient of determination of 0.81. Moreover, in support of our hypothesis, the recurrent stacked LSTM network demonstrated its ability to predict intended motion accurately and efficiently in gait, outperforming two other neural network architectures: a feedforward MLP and a hybrid LSTM-MLP. The method paves the way for the development of a cost-effective, single-modal control system for assistive devices in gait rehabilitation.

Mitral valve function depends on its complex geometry and tissue health, with alterations in shape and tissue response affecting the long-term restorarion of function. Previous computational frameworks for biomechanical assessment are mostly based on patient-specific geometries; however, these are not flexible enough to yield a variety of models and assess mitral closure for individually tuned morphological parameters or material property representations. This study details the finite element approach implemented in our previously developed toolbox to assess mitral valve biomechanics and showcases its flexibility through the generation and biomechanical evaluation of different models. A healthy valve geometry was generated and its computational predictions for biomechanics validated against data in the literature. Moreover, two mitral valve models including geometric alterations associated with disease were generated and analysed. The healthy mitral valve model yielded biomechanical predictions in terms of valve closure dynamics, leaflet stresses and papillary muscle and chordae forces comparable to previous computational and experimental studies. Mitral valve function was compromised in geometries representing disease, expressed by the presence of regurgitating areas, elevated stress on the leaflets and unbalanced subvalvular apparatus forces. This showcases the flexibility of the toolbox concerning the generation of a range of mitral valve models with varying geometric definitions and material properties and the evaluation of their biomechanics.

Integration of advanced technologies have revitalised treatment methods in the current clinical practice. In orthopaedic surgery, patient-specific implants have leveraged the design freedom offered by additive manufacturing (AM) exploiting the capabilities within powder bed fusion processes. Furthermore, generative design (GD), a design exploration tool based on the artificial intelligence, can integrate manufacturing constraints in the concept development phase, consequently bridging the gap between AM design and manufacturing. However, the reproducibility of implant prototypes are severely constrained due to uncomprehensive information on manufacturing and post processing techniques in the detailed design phase. This paper explores the manufacturing feasibility of novel GD concept plate designs for High Tibial Osteotomy (HTO), a joint preserving surgery for a patient diagnosed with osteoarthritis in the knee. A design for AM (DfAM) workflow for a generatively designed HTO plate is presented, including; detailed DfAM of GD concept designs, fabrication of plate prototypes using electron beam powder bed fusion (PBF-EB) of medical grade Ti-6Al-4 V, post processing and inspection. The study established PBF-EB as a suitable manufacturing method for the highly complex GD plate fixations, through evaluating the impact of manufacturing and post processing on the surface finish and geometrical precision of the plate design features.

Acoustic emission (AE) testing detects the onset and progression of mechanical flaws. AE as a diagnostic tool is gaining traction for providing a tribological assessment of human joints and orthopaedic implants. There is potential for using AE as a tool for diagnosing joint pathologies such as osteoarthritis and implant failure, but the signal analysis must differentiate between wear mechanisms—a challenging problem! In this study, we use supervised learning to classify AE signals from adhesive and abrasive wear under controlled joint conditions. Uncorrelated AE features were derived using principal component analysis and classified using three methods, logistic regression, k-nearest neighbours (KNN), and back propagation (BP) neural network. The BP network performed best, with a classification accuracy of 98%, representing an exciting development for the clustering and supervised classification of AE signals as a bio-tribological diagnostic tool.

Understanding the tribological behaviour of articular cartilage enables the development of effective replacement biomaterials. This study presents a technique for the investigation of the frictional torque of articular cartilage, for the assessment of replacement biomaterials. A calcium alginate hydrogel was used as the biomaterial for this study. Three different specimen types were examined to include articular cartilage, calcium alginate hydrogel, alone, and in combination with articular cartilage. An axial load, varying from 10 to 100 N, was applied to the specimen and the frictional torque measured whilst an indenter underwent axial rotation from 0° to 2° to 0° for 100 cycles. The resulting frictional torque magnitude was evaluated with a smooth curve fitting function. Linear regression identified a statistically significant relationship between torque magnitude and axial load (p < 0.05) for all specimen variations. From 10 to 100 N of applied load, mean torque magnitude ranged from 0.08±0.010 to 0.11±0.013 N m, 0.08±0.012 to 0.09±0.016 N m and 0.07±0.017 to 0.09±0.020 N m (mean ± standard deviation), for articular cartilage, calcium alginate separately and in combination with articular cartilage, respectively. This study has established a suitable frictional torque testing protocol for potential cartilage replacement biomaterials.