Responsive fungal insoles for pressure detection

Mycelium bound composites are promising materials for a diverse range of applications including wearables and building elements. Their functionality surpasses some of the capabilities of traditionally passive materials, such as synthetic fibres, reconstituted cellulose fibres and natural fibres. Thereby, creating novel propositions including augmented functionality (sensory) and aesthetic (personal fashion). Biomaterials can offer multiple modal sensing capability such as mechanical loading (compressive and tensile) and moisture content. To assess the sensing potential of fungal insoles we undertook laboratory experiments on electrical response of bespoke insoles made from capillary matting colonised with oyster fungi Pleurotus ostreatus to compressive stress which mimics human loading when standing and walking. We have shown changes in electrical activity with compressive loading. The results advance the development of intelligent sensing insoles which are a building block towards more generic reactive fungal wearables. Using FitzHugh-Nagumo model we numerically illustrated how excitation wave-fronts behave in a mycelium network colonising an insole and shown that it may be possible to discern pressure points from the mycelium electrical activity

Biomechanical analysis of the diabetic foot: an integrated approach using movement analysis and finite element simulation

Objective:High plantar pressures have been associated with foot ulceration in patients with diabetes. Treatment usually includes an in-shoe intervention designed to reduce plantar pressure under the heel by using insoles. Finite element (FE) analysis provides an efficient computational framework to investigate the performance of different insoles for optimal pressure reduction [Goeske et al. 2005]. The aim of this study is to design a patient specific, 2-dimensional (2D) FE model of diabetic hindfoot and to apply on it patient-specific forces.Method: A 2D FE model of the hindfoot was developed from reconstruction of magnetic resonance images (Simpleware ScanIP-ScanFE, v.5.0 and Rhinoceros v.4.0). FE software ABAQUS was used to perform the numerical stress analyses. A diabetic subject (age, 72 years, BMI, 25.1 kg/m2) and a healthy subject (age 28 years, BMI 20.2 kg/m2) were acquired. The foot biomechanics analysis was carried out as in [Sawacha et al. 2012]. Vertical ground reaction forces (Bertec), taken from the various phases of the gait, were applied to the FE model. Validation of the pressure state was achieved by comparing model predictions of contact pressure distribution with experimental plantar pressure measures Result: A nonlinear 2D FE hindfoot model was developed and meshed with quadratic elements. The measured and model predicted peak plantar pressures of the diabetic subject was respectively 682.32 KPa and 602.82 KPa. The values for the healthy subject were 483.63 KPa for the measured peak plantar pressure and 428.63 KPa for the simulated one. The model predicted structural response of the heel pad was in agreement with experimental results unless 10% of error. Conclusion: The proposed model will be useful to simulate the different insole material and their contribution in decreasing the plantar pressure

Physical and chemical sensing applications of polypyrrole-coated foams

We live in a world of information, and emerging technologies, which compel us to look for new ways to collect, process, and distribute information. Today we are faced with an information overload problem as users struggle to locate the right information in the right way at the right time. In my view this is an “overload” of trivial information coupled with a gap in access to important information. Digitization of information and communications has seen the rise and rise of computers to a now ubiquitous position in our society. However, the problem remains as to how to merge the digital world with sensing, and respond to changes in the real world. Ubiquitous information systems are needed that will automatically sense and importantly, respond to changes in their environment and usage in order to deliver a more intelligent, proactive and personalized information service. These systems may be wearable, enabling them to disappear into our personal space, enhancing rather than burdening our daily activities. Conventional sensors are generally unsuitable for wearable body monitoring devices either due to their physical structure or their functional requirements. This thesis examines this area of wearable sensors, detailing the development and characterisation of novel sensing materials and outlines their performance in various on-body monitoring applications

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

An In-Shoe Laser Doppler Sensor for Assessing Plantar Blood Flow in the Diabetic Foot

An in-shoe laser Doppler sensor for assessing plantar blood flow in the diabetic foot. Jonathan Edwin Cobb Plantar ulceration is a complication of the diabetic foot prevalent in adults with type 11 diabetes mellitus. Although neuropathy, microvascular disease and biornechanical factors are all implicated, the mechanism by which the tissue becomes pre-disposed to damage remains unclear. Recent theories suggest that the nutritional supply to the tissue is compromised, either by increased flow through the arteriovenous anastomoses ('capillary steal' theory) or through changes in the micro vascu I ature (haemodynamic hypothesis). Clinical data to support these ideas has been limited to assessment of the unclad foot under rest conditions. A limitation of previous studies has been the exclusion of static and dynamic tissue loading, despite extensive evidence that these biornechanical factors are essential in the development of plantar ulceration. The present study has overcome these problems by allowing assessment of plantar blood flow, in-shoe, during standing and walking. The system comprises a laser Doppler blood flux sensor operating at 780nm, load sensor, measurement shoe, instrumentation, and analysis software. In-vitro calibration was performed using standard techniques. An in-vivo study of a small group of diabetic subjects indicated differences in the blood flux response between diabetic neuropaths, diabetics with vascular complications and a control group. For example, following a loading period of 120s, relative increases in response from rest to peak were: Control (150% to 259%), Vascular (-70% to 242%), Neuropathic (109%-174%) and recovery times to 50% of the peak response were: Control (33s to 45s), Vascular (43s to >120s), Neuropathic (>120s). Dynamic re-perfusion rates (arbitrary units per millisecond) obtained for the swing phase of gait were: Control (6.1 a. u/ms to 7.9 a. u/ms), Vascular (4 a. u/ms to 6.2 a. u/ms), Neuropathic (2.3 a. u/ms to 4.5 a. u/ms)

Study of Human Muscle Structure and Function with Velocity Encoded Phase Contrast and Diffusion Tensor Magnetic Resonance Imaging Techniques

The disproportionate loss of muscle force with aging and disuse atrophy compared to the loss of muscle mass is not yet completely understood. In addition to well-established neural and contractile determinants of force loss, remodeling of the extracellular matrix (ECM) has been recently shown in animal models to be another important contributor. In-vivo human studies exploring the structural remodeling of the ECM and its functional consequences are lacking due to the paucity of appropriate imaging techniques. This study focuses on the development and application of advanced Magnetic Resonance Imaging (MRI) methods to elucidate the mechanisms of loss of force with aging and disuse atrophy with the focus on ECM. Functional changes are investigated by strain and strain rate tensor mapping of muscle under different contraction paradigms using Velocity Encoded Phase-Contrast MRI. Methodological advances include improvements in hardware and software control of the dynamic studies. To overcome the limitation of long scan times, compressed sensing MR acquisition and reconstruction framework to reduce scan times to under a minute were developed. A multi-step automated analysis pipeline to extract 3D strain/strain rate tensors from the velocity images was implemented to process the large dynamic volumes. Strain indices reflecting the material properties of the ECM were shown to correlate with force loss leading to a hypothesis that shear strain may serve as a surrogate marker for lateral transmission of force. Diffusion tensor imaging has been applied previously to study skeletal muscle fiber architecture. The resolution of the images precludes direct inferences to be made about the microstructure. To address this limitation, bicompartmental and Random Permeable Barrier models of diffusion were applied to the diffusion data obtained with spin-echo and custom-developed stimulated echo echo-planar-imaging sequences respectively. Model derived parameters (fiber diameter, wall permeability) obtained from fitting time-dependent diffusion data were in physiologically reasonable range, with potential for tracking age related changes in muscle microstructure. The developed imaging and modeling techniques were applied to a cohort of young/senior subjects and to longitudinal tracking of disuse atrophy induced by Unilateral Limb Suspension. These studies may potentially provide the causal link between age- and disuse-related structural remodeling and its functional consequences

An Innovative Manufacturing Technique Which Integrates Multi-Axial Shear Stress Sensors in Footwear for Female Distance Runners to Examine the Foot-Shoe Interface

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Numerical Simulation in Biomechanics and Biomedical Engineering

In the first contribution, Morbiducci and co-workers discuss the theoretical and methodological bases supporting the Lagrangian- and Euler-based methods, highlighting their application to cardiovascular flows. The second contribution, by the Ansón and van Lenthe groups, proposes an automated virtual bench test for evaluating the stability of custom shoulder implants without the necessity of mechanical testing. Urdeitx and Doweidar, in the third paper, also adopt the finite element method for developing a computational model aim to study cardiac cell behavior under mechano-electric stimulation. In the fourth contribution, Ayensa-Jiménez et al. develop a methodology to approximate the multidimensional probability density function of the parametric analysis obtained developing a mathematical model of the cancer evolution. The fifth paper is oriented to the topological data analysis; the group of Cueto and Chinesta designs a predictive model capable of estimating the state of drivers using the data collected from motion sensors. In the sixth contribution, the Ohayon and Finet group uses wall shear stress-derived descriptors to study the role of recirculation in the arterial restenosis due to different malapposed and overlapping stent conditions. In the seventh contribution, the research group of Antón demonstrates that the simulation time can be reduced for cardiovascular numerical analysis considering an adequate geometry-reduction strategy applicable to truncated patient specific artery. In the eighth paper, Grasa and Calvo present a numerical model based on the finite element method for simulating extraocular muscle dynamics. The ninth paper, authored by Kahla et al., presents a mathematical mechano-pharmaco-biological model for bone remodeling. Martínez, Peña, and co-workers propose in the tenth paper a methodology to calibrate the dissection properties of aorta layer, with the aim of providing useful information for reliable numerical tools. In the eleventh contribution, Martínez-Bocanegra et al. present the structural behavior of a foot model using a detailed finite element model. The twelfth contribution is centered on the methodology to perform a finite, element-based, numerical model of a hydroxyapatite 3D printed bone scaffold. In the thirteenth paper, Talygin and Gorodkov present analytical expressions describing swirling jets for cardiovascular applications. In the fourteenth contribution, Schenkel and Halliday propose a novel non-Newtonian particle transport model for red blood cells. Finally, Zurita et al. propose a parametric numerical tool for analyzing a silicone customized 3D printable trachea-bronchial prosthesis