The dielectric properties are key parameters that quantify the interaction between electromagnetic waves and human biological tissues. In particular, the development of electromagnetic-based medical technologies rely on knowledge of the dielectric properties of bone, specifically for applications such as electrical stimulation and bone health monitoring. Electrical stimulation is used in clinics to promote the healing of bone fractures, treating non-unions, congenital pseudarthrosis, bone regeneration and during bone implant procedures. The response of the bone to any external electrical stimulation is governed by the dielectric properties of the bone, which vary with the applied frequency of the stimuli. Bone mineral density is considered a key indicator of osteoporosis diagnosis, and is assumed to be related to the dielectric properties of the bone. Therefore, dielectric properties of bones may potentially be used to diagnose osteoporosis. The bone dielectric properties can be assessed non-invasively for bone health monitoring. Several research studies have reported dielectric properties of cortical and trabecular bones in recent literature. Since dielectric properties of bone determine the response of the tissue to therapies, it is important to compile and analyze the reported dielectric data in order to have a thorough understanding of these properties. It is established from the literature that the low frequency (10 Hz–1 GHz) dielectric properties of bone are particularly important in diagnostic applications, as the correlation between the dielectric properties and bone mineral density is more significant than at higher frequencies. In this paper, the low frequency dielectric properties of the bone reported in the literature are compiled and quantitatively analysed. The results suggest that there is a significant inter- and intra-species variation in the reported dielectric data from human, bovine, porcine, and rat bone tissues. Moreover, the relationship between the dielectric properties and bone mineral density is inconsistent across the various studies, indicating that further research in this area is needed.

The objective of this study is to determine whether in vitro dielectric properties of human trabecular bones, can distinguish between osteoporotic and osteoarthritis patients' bone samples. Specifically this study enlightens intra-patient variation of trabecular bone microarchitecture and dielectric properties, inter-disease comparison of bone dielectric properties, and finally establishes the correlation to traditional bone histomorphometry parameter (bone volume fraction) for diseased bone tissue. Bone cores were obtained from osteoporotic and osteoarthritis patients (n = 12). These were scanned using microCT to examine bone volume fraction. An open-ended coaxial probe measurement technique was employed to measure dielectric properties over the 0.5 - 8.5 GHz frequency range. The dielectric properties of osteoarthritis patients are significantly higher than osteoporotic patients; with an increase of 41% and 45% for relative permittivity and conductivity respectively. The dielectric properties within each patient vary significantly, variation in relative permittivity and conductivity was found to be greater than 25% and 1.4% respectively. A weak correlation (r = 0.5) is observed between relative permittivity and bone volume fraction. Osteoporotic and osteoarthritis bones can be differentiated based on difference of dielectric properties. Although these do not correlate strongly to bone volume fraction, it should be noted that bone volume fraction is a poor predictor of fracture risk. The dielectric properties of bones are found to be influenced by mineralization levels of bones. Therefore, dielectric properties of bones may have potential as a diagnostic measure of osteoporosis.

Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from a large temporal dataset are time-consuming. It requires researchers to consider several signal-processing algorithms and time-series analysis methods to identify and extract meaningful features from the given time-series data. These features are the core of a machine learning-based predictive model and are designed to describe the informative characteristics of the time-series signal. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Recently, a lot of work has been carried out on automating the extraction and selection of times-series features. In this paper, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The algorithm calculates a list of 1578 features of heart rate and respiratory rate signals (combined) using the tsfresh library. These features are then shortlisted to the more specific time-series features using Principal Component Analysis (PCA) and Pearson, Kendall, and Spearman correlation ranking techniques. A comparative study of conventional statistical features (like, mean, standard deviation, median, and mean absolute deviation) versus correlation-based selected features is performed using linear (logistic regression), ensemble (random forest), and clustering (k-nearest neighbours) predictive models. The correlation-based selected features achieved higher classification performance with an accuracy of 98.6% as compared to the conventional statistical feature’s 67.4%. The outcome of the proposed study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification.

The evaluation of the microwave imaging (MWI) prototype and imaging algorithms on experimental bone phantoms is a precursor step before clinical testing for measuring in vivo dielectric properties of human bones. To this end, this paper presents microwave tomographic image reconstruction of experimental phantoms of normal and diseased human calcaneus bone using an MWI prototype and distorted Born iterative method (DBIM) algorithm for bone health monitoring application. A two-layered simplified cylindrical-shaped 3-D printed phantom was used to mimic the human calcaneus bone. The external and internal layers of the bone phantom mimic the cortical bone and trabecular bone, respectively. Liquid tissue-mimicking mixtures (TMM) for normal bone, osteoporotic bone, and osteoarthritis bone were prepared. The phantoms were placed in the imaging prototype and the electromagnetic inverse scattering problem was solved using the DBIM to create the complex permittivity images. An $L_{2}$ -based regularization approach was adopted along with the iterative method with adaptive thresholding for compressed sensing (IMATCS) to overcome the ill-posedness and to solve the underdetermined set of linear equations at each DBIM iteration. The reconstruction of dielectric properties of bone phantoms have shown that $L_{2}$ -IMATCS approach provides a robust reconstruction of diverse bone phantoms with acceptable accuracy. Moreover, the osteoporotic and osteoarthritis bone phantoms were distinguished based on reconstructed dielectric properties with an average percentage difference of 26% at 3 GHz. This paper has made the first attempt to validate an MWI prototype for bone imaging application. A DBIM-based iterative method has been employed to classify normal and diseased bone phantoms.