Phase angles measured at low frequencies with the tetrapolar technique are sometimes scattered or even positive. We used Monte Carlo simulations of lock-in detection to quantify how large phase errors that measurement noise alone can cause. For a signal-to-noise ratio after demodulation (SNRout) of about 3 or higher, the standard deviation of the phase error was close to 57° divided by SNRout, and 95% of the errors were within about twice this value. At SNRout = 10 the error standard deviation was 6°; at SNRout = 1 it was 64° and 16% of the measurements were wrong by more than 90°. As SNRout decreased towards zero, the phase gradually approached a completely random value. In an illustrative low-frequency tetrapolar measurement, the absolute phase errors were small (standard deviation 0.007° at 1 Hz and 0.04° at 0.1 Hz), but the true tissue phase was even smaller. The error was therefore 1.2 times the true phase at 1 Hz and 37 times at 0.1 Hz, and the measured phase had the wrong sign in 20% and 49% of the measurements, respectively. The sign of a small phase angle is only reliable (wrong in less than 1% of measurements) when the true phase is larger than 2.3 times the error standard deviation.
Upper-limb amputation and neuromuscular disorders impose functional limitations on millions of people worldwide. Commercially available myoelectric prostheses, controlled by surface electromyography (sEMG) signals from residual limb muscles, cost approximately USD 75,000–100,000 (about INR 62–83 lakh), limiting accessibility in resource-limited settings. We present a 3D-printed, tendon-driven research hand prototype with a component cost of USD 122–232 (about INR 10,100–19,300), built around a single MyoWare 1.0 sensor and an Arduino Nano, with gesture classification performed on a tethered host PC.
Three-dimensional tumor-on-chip systems better reproduce the spatial organization of tumor cells within extracellular-matrix-based microenvironments than conventional two-dimensional cultures, yet their integration with quantitative, non-destructive monitoring tools remains limited. Electrical bioimpedance provides a label-free approach for real-time assessment of living 3D tissues, but its implementation in perfused millimeter-scale microfluidic environments requires rigorous electrical and geometrical validation.
This study aims to develop a reproducible, biomimetic, anthropomorphic leg phantom as a controlled platform for the development and evaluation of wearable sensors for low-frequency bioimpedance monitoring of lymphedema. As a proof of concept, a 20-cm multilayer model was developed to establish and characterise the materials and fabrication approach required for future full-scale sensor validation. Electrical and mechanical characterisation of polyvinyl alcohol cryogel (PVA-C) and PVA-based fat and muscle phantoms was performed. The influence of freeze-thaw cycle number and duration on electrical properties and Shore hardness was investigated to support large-scale multilayer fabrication. Muscle anisotropy was reproduced via directional structuring. Reproducibility of electrical properties and long-term stability of PVA-C hydrogels were evaluated under refrigerated, vacuum-sealed storage. A practical methodology for assembling multilayer anthropomorphic leg phantoms was implemented. Electrical properties remained stable across the investigated freeze/thaw conditions, whereas Shore hardness increased with the number of cycles. Two 20-cm-high, three-layer anthropomorphic leg phantoms, representing healthy and stage 2 pathological condition, were fabricated using 3D-printed moulds integrating bone, muscle, and fat. Interfacial bonding between the layers provided mechanical cohesion and electrical continuity. Bioimpedance measurements showed consistent differences between the healthy and lymphedema-mimicking configurations. The methodology enables fabrication and assembly of multilayer anthropomorphic leg phantoms with electrical stability and mechanical cohesion. This proof-of-concept provides a basis for the development of a full-scale anthropomorphic model and its subsequent integration with wearable bioimpedance sensing technologies for lymphedema monitoring.
Electrical Impedance Tomography (EIT) provides low-cost functional impedance imaging, whereas ultrasound offers high-resolution morphological visualization. In this study, a hybrid Ultrasound–EIT (EIT–US) fusion framework is developed for soft-tissue characterization in layered ex vivo biological phantoms using controlled chicken tissue models. A custom EIT acquisition system recorded 208 boundary voltage measurements per acquisition across three tissue layers and multiple frequency–voltage conditions, with experiments repeated across three anatomical tissue configurations. Five FEM-based reconstruction methods—Back Projection (BP), Gauss–Newton (GN), Tikhonov regularization (TIK), GREIT, and a linearized EIDORS-like approach—were implemented within a stand-alone MATLAB framework. Ultrasound images acquired using a GE VScan Air CL probe (musculoskeletal mode) were integrated with EIT reconstructions through a multimodal fusion pipeline incorporating impedance imaging, ultrasound texture–echogenicity mapping, and statistical correlation analysis. Quantitative evaluation demonstrated consistent layer differentiation with moderate structural–electrical agreement (r ≈ 0.37–0.45). ESI increased by more than 60% at the evaluated layer interfaces during the fusion approach compared to EIT-only reconstructions, along with an increase in boundary detection performance (AUC improvement of approximately 0.13–0.15). Structural similarity (SSIM) of the fused images relative to ultrasound boundaries ranged from 0.71 to 0.78, indicating preserved spatial coherence. Frequency and voltage variations influenced reconstruction stability and contrast recovery. Overall, multimodal fusion improved the structural interpretability of EIT by anchoring conductivity distributions to ultrasound-derived morphological boundaries. These results indicate that incorporating ultrasound-derived structural priors improves the interpretability of EIT reconstructions by providing spatial context to conductivity distributions. This framework provides a practical experimental approach for integrating complementary electrical and acoustic sensing modalities, with potential relevance to future low-cost and portable soft-tissue imaging systems.
