Multi-frequency electrical impedance tomography (EIT) systems require stable voltage controlled current generators that will work over a wide frequency range and with a large variation in load impedance. In this paper we compare the performance of two commonly used designs: the first is a modified Howland circuit whilst the second is based on a current mirror. The output current and the output impedance of both circuits were determined through PSPICE simulation and through measurement. Both circuits were stable over the frequency ranges 1 kHz to 1 MHz. The maximum variation of output current with frequency for the modified Howland circuit was 2.0% and for the circuit based on a current mirror 1.6%. The output impedance for both circuits was greater than 100 kohms for frequencies up to 100 kHz. However, neither circuit achieved this output impedance at 1 MHz. Comparing the results from the two circuits suggests that there is little to choose between them in terms of a practical implementation.

Bipolar and tetrapolar impedance measurements from a volume of biological tissue are compared. In the bipolar case, sensitivity is always positive and decreases with depth. However, for tetrapolar measurements the mean sensitivity is found to be zero over the surface plane, rising to a maximum at a depth proportional to electrode spacing.

Pathogens and adulterants in human feeding consumables can be readily identified according to their electrical properties. Electrical bioimpedance analysis (BIA) has been widely used for body contents characterization, such as blood, urine, lactate, and sweat. If the concentration of glucose in blood alters the electrical properties of the blood medium, then the impedance spectrum obtained by BIA can be used to measure glycemia. For some applications, artificial neural networks allow the correlation of these parameters both impedance and concentration of glucose by means of symbolic and statistical rules. According to our literature review, there is not any physical model that allows the interpretation of the relationship between blood’s electrical properties from impedance spectra and the concentration of glucose in blood plasma. This article proposes a simplified physical model for blood electrical conductivity as a function of concentration of glucose, based on Bruggeman’s effective medium theory. The equations of this model were obtained considering an insulating phase distribution diffused in a conductive matrix, in which red blood cells are represented by macroscopic insulating nuclei and glucose molecules by microscopic insulating particles. The impedance spectrum for different glucose concentrations (4.0 to 6.8 mmol/L) in a blood sample, published by Kamat Bagul (2014), were compared to the proposed model. The results showed a significant correlation with the experimental data, showing a maximum error of 5.2%. The proposed model might be useful in the design of noninvasive blood glucose monitoring systems.

For Electrical Impedance Spectroscopy (EIS) applications, the Enhanced Howland Current Source (EHCS) is a popular choice as an excitation circuit due to its simplicity, reliability, and safety. However, its output impedance degradation at high frequency leads to errors that are unacceptable for high load impedance applications, such as the ones which use dry or microelectrodes. Based on a proposed mathematical model, this work designed an EHCS circuit which includes an output current buffer and frequency compensation. PSpice simulations were performed as proof of concept, and then the measured data were collected for comparison. For the proposed circuit, called here Load-in-the-Loop Compensated Enhanced Howland Source (LLC-EHCS), the results showed that the output current errors are lower than 1% up to 3.7 MHz over the load range of 560–2200 Ω and 1.2 MHz with 5.6 kΩ. On the other hand, for the case of the standard EHCS circuit, these frequencies are 170 and 80 kHz, respectively. Also, the output linear swing was found to be 3 times higher than the EHCS. It can be concluded that the proposed LLC-EHCS may be widely used as an excitation circuit for high load and wide bandwidth EIS applications.

Hemiparetic stroke patients can have several muscular and postural disorders which compromise their balance. Serious Games (SG) emerged as a new approach to enhance conventional treatment by making it a motivating method to meet individual needs. This paper evaluated the therapeutic effects of a biomedical SG system developed for balance evaluation and training of hemiparetic stroke patients. The system consists of a balance board with inertial sensors and a computer system that runs the game. A novel scoring system for balance evaluation which extracts metric information regarding patients’ performance while gaming was proposed. A quasi-experimental study was performed with six hemiparetic patients in an exercise program using the SG twice per week for 10 weeks. Twelve healthy subjects were recruited for determining the baseline score for balance by using the proposed system. Significant Effect Sizes (ES) were observed for dynamic balance (ES=0.9), functional mobility (ES=0.4) and center of pressure displacement of the balance board (ES=1.9). Significant correlations between game scores and clinical scales suggest that the use of the scoring system for balance evaluation is feasible. The results of this study support the clinical potential of a biomedical SG for balance rehabilitation of hemiparetic stroke patients.

One of the health challenges in the 21st century is to rethink approaches to non-communicable disease prevention. A solution is a smart city that implements technology to make health smarter, enables healthcare access, and contributes to all residents’ overall well-being. Thus, this paper proposes an architecture to deliver smart health. The architecture is anchored in the Internet of Things and edge computing, and it is driven by artificial intelligence to establish three foundational layers in smart care. Experimental results in a case study on glucose prediction noninvasively show that the architecture senses and acquires data that capture relevant characteristics. The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in $R^2$, respectively, on training and test sets. Future research works involve extending the performance of the algorithms with new datasets, creating and optimizing embedded AI models, deploying edge-IoT with embedded AI for wearable devices, implementing an autonomous AI cloud engine, and implementing federated learning to deliver scalable smart health in a smart city context.