In recent research, microwave sensors have been used to follow up the recovery of lower extremity trauma patients. This is done mainly by monitoring the changes of dielectric properties of lower limb tissues such as skin, fat, muscle, and bone. As part of the characterization of the microwave sensor, it is crucial to assess the signal penetration in in vivo tissues. This work presents a new approach for investigating the penetration depth of planar microwave sensors based on the Split-Ring Resonator in the in vivo context of the femoral area. This approach is based on the optimization of a 3D simulation model using the platform of CST Microwave Studio and consisting of a sensor of the considered type and a multilayered material representing the femoral area. The geometry of the layered material is built based on information from ultrasound images and includes mainly the thicknesses of skin, fat, and muscle tissues. The optimization target is the measured S-11 parameters at the sensor connector and the fitting parameters are the permittivity of each layer of the material. Four positions in the femoral area (two at distal and two at thigh) in four volunteers are considered for the in vivo study. The penetration depths are finally calculated with the help of the electric field distribution in simulations of the optimized model for each one of the 16 considered positions. The numerical results show that positions at the thigh contribute the highest penetration values of up to 17.5 mm. This finding has a high significance in planning in vitro penetration depth measurements and other tests that are going to be performed in the future.

Background:

Proximal femur fractures are a common injury after low energy trauma in the elderly. Most rehabilitation programs are based on restoring mobility and early resumption of weight-bearing. However, therapy compliance is low in patients following lower extremity fractures. Moreover, little is known about the relevance of gait parameters and how to steer the rehabilitation after proximal femur fractures in the elderly. Therefore, the aim of this prospective, randomized controlled trial is to gain insight in gait parameters and evaluate if real-time visual biofeedback can improve therapy compliance after proximal femur fractures in the elderly.

Methods:

This is a two-arm, parallel-design, prospective, randomized controlled trial. Inclusion criteria are age >= 60 years, a proximal femur fracture following low energy trauma, and unrestricted-weight bearing. Exclusion criteria are cognitive impairment and limited mobility before trauma. Participants are randomized into either the control group, which receives care as usual, or the intervention group, which receives real-time visual biofeedback about weight-bearing during gait in addition to care as usual. Spatiotemporal gait parameters will be measured in 94 participants per group during a 30-m walk with an ambulatory biofeedback system (SensiStep). The progress of rehabilitation will be evaluated by the primary outcome parameters maximum peak load and step duration in relation to the discharge date. Secondary outcome parameters include other spatiotemporal gait parameters in relation to discharge date. Furthermore, the gait parameters will be related to three validated clinical tests: Elderly Mobility Scale; Functional Ambulation Categories; and Visual Analogue Scale. The primary hypothesis is that participants in the intervention group will show improved and faster rehabilitation compared to the control group.

Discussion:

The first aim of this multicenter trial is to investigate the normal gait patterns after proximal femur fractures in the elderly. The use of biofeedback systems during rehabilitation after proximal femur fractures in the elderly is promising; therefore, the second aim is to investigate the effect of real-time visual biofeedback on gait after proximal femur fractures in the elderly. This could lead to improved outcome. In addition, analysis of the population may indicate characteristics of subgroups that benefit from feedback, making a differentiated approach in rehabilitation strategy possible.

The European project “Senseburn” aims to develop a non-invasive diagnostic instrument for assessing the depth and propagation of human burns in the clinical scenario. This article introduces an improved flexible microwave split-ring resonator-based sensor, as a new development in this project. The excitation system and the fabrication process are the major improvements with respect to its precedent microwave sensor, both based in polydimethylsiloxane (PDMS) and copper. Both improvements are introduced together with the design of the sensor and of the experimental setup. Human tissue emulating phantoms are designed, fabricated, validated, and employed to emulate different burn depths and to validate the conceptual functionality of the proposed sensor. The Keysight dielectric probe 85070E is employed for the phantom validation. The analysis suggests that the sensor could estimate the burn depth. Future works will be carried out with ex vivo human tissues. 

The bone density analysis system is a new method to analyze the amount of bone healed in cranial vault defects. Craniotomy creates such defects to treat pediatric patients with craniosynostosis. This method uses the variations in the resonance of a microwave sensor attached to the area under test in the patient. Data were collected from infants treated for craniosynostosis through craniotomy surgery in clinical trials. The data, which consist of resonance parameters (frequency, amplitude, and Q-factor) is collected using a microwave-based biomedical sensor. This sensor could detect changes in the resonance as changes in the permittivity of the various tissues at the ISM band. By observing differences between a reference and target defect points, bone healing over time could be accessed. In this paper, we analyze the validity of a proposed Computational Simulation Technology (CST) based numerical model for the sensor and extend the clinical data analysis from previous works with our bone density analysis system. The validity of the model is analyzed by comparing its outcomes to available measurements from numerical simulations, phantoms mimicking living tissues and clinical trial. In the data analysis, a hypothesis is formulated and tested regarding the healing over time. By deriving a set of parameters for each collected dataset in the clinical trials, a distinct pattern was found which shows visible changes such as edema and tissue thickening over the course of the healing process with this technique. Moreover, we manage to distinguish significant differences between the reference and defect points after the craniosynostosis surgery.

Microwave sensing technique is a possible and attractive alternative modality to standard Xrays,magnetic resonance imaging, and computed tomography methods for medical diagnostic applications.This technique is beneficial since it uses non-ionizing radiation and that can be potentially used for themicrowave healthcare system. The main purpose of this paper is to present a microwave sensing techniqueto analyze the variations in biological tissue thickness, considering the effect of physiological and biologicalproperties on microwave signals. With this goal, we have developed a two-port non-invasive sensor systemcomposed of two split ring resonators (SRRs) operating at an Industrial, Scientific, and Medical frequencyband of 2.45 GHz. The system is verified using the amplitude and phase of the transmitted signal in ex-vivomodels, representing different tissue thicknesses. Clinical applications such as the diagnosis of muscularatrophy can be benefitted from this study.