A low-cost, portable, two-dimensional bioimpedance distribution estimation system based on the AD5933 impedance converter

Machine learning-based bioimpedance assessment of knee osteoarthritis severity

This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA. All participants performed 20 repetitions of each of the following five tasks: (i) sitting with the knee bent, (ii) sitting with the knee extended, (iii) sitting and performing successive extensions and flexions of the knee, (iv) standing, and (v) walking. Data from the 15 experimental setups (3 types of measurements×5 exercises) were used to train a multiclass random forest. The training and validation cycle was repeated 100 times using random undersampling. At each of the 100 cycles, 80% of the data were used for training and the rest for testing. The results showed that the proposed approach achieved average sensitivities and specificities of 100% for the four KOA severity grades in the extension, cyclic, and gait tasks. This suggests that the proposed method can serve as a screening tool to determine which individuals should undergo x-rays or magnetic resonance imaging for further evaluation of KOA.

Efficient and Accurate Analog Voltage Measurement Using a Direct Sensor-to-Digital Port Interface for Microcontrollers and Field-Programmable Gate Arrays

Portable sensor systems are usually based on microcontrollers and/or Field-Programmable Gate Arrays (FPGAs) that are interfaced with sensors by means of an Analog-to-Digital converter (ADC), either integrated in the computing device or external. An alternative solution is based on the direct connection of the sensors to the digital input port of the microcontroller or FPGA. This solution is particularly interesting in the case of devices not integrating an internal ADC or featuring a small number of ADC channels. In this paper, a technique is presented to directly interface sensors with analog voltage output to the digital input port of a microcontroller or FPGA. The proposed method requires only a few passive components and is based on the measurements of the duty cycle of a digital square-wave signal. This technique was investigated by means of circuit simulations using LTSpice and was implemented in a commercial low-cost FPGA device (Gowin GW1NR-9). The duty cycle of the square-wave signal features a good linear correlation with the analog voltage to be measured. Thus, a look-up table to map the analog voltage values to the measured duty cycle is not required with benefits in terms of memory occupation. The experimental results on the FPGA device have shown that the analog voltage can be measured with a maximum accuracy of 1.09 mV and a sampling rate of 9.75 Hz. The sampling rate can be increased to 31.35 Hz and 128.31 Hz with an accuracy of 1.61 mV and 2.68 mV, respectively.

A low-cost, portable 32-channel EIT system with four rings based on AFE4300 for body composition analysis

A proposed low-cost, portable, 32-channel (4 rings of 8-channel) Electrical Impedance Tomography (EIT) system based on the AFE4300 analog front-end for body composition measurement. Each ring allows obtaining the conductivity distribution of 4 cross sections, 4 cm apart; to analyze the behavior of conductivity in a volume. The switching of the 4 rings and the current injection and voltage measurement patterns are done with three Texas Instruments 74HC4067 multiplexers, which are managed by an ESP32 board. The proposed system has an average signal-to-noise ratio of 74.71 dB and a frame rate of 50 fps. The sensitivity tests to impedance and volume changes consisted of introducing 4 tubes of different diameters (2 steel and 2 polyvinyl chloride) into a tank with saline solution; then conductivity distribution images were generated in 4 cross-sections of the tank, using the algorithms Gauss-Newton and Noser. Finally, the global impedance index (GI) is calculated to estimate the volume of each tube inside the tank. The results show that the proposed system is highly sensitive to impedance and volume changes, being a promising system for monitoring tissues, and fluids biological.