We present a simple-structured strain sensor based on a low-cost ionic liquid. The ionic liquid was made of sodium chloride/propylene glycol solution and was embedded in a linear microfluidic channel fabricated using Ecoflex. The proposed sensor is capable of measuring strain up to 100% with excellent repeatability. The highest gauge factor is obtained as 6.19 under direct current excitation and 3.40 under alternating current excitation at 1 kHz. The sensor shows negligible hysteresis and overshoot, and survived 10,000 rapid stretch-release cycles of a 100% peak strain with a minor deviation in the response signal. The sensor can be mounted to different locations on the human body and suits a variety of applications in the field of motion detection, human–machine interface and healthcare monitoring.

Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging technology known as electrical impedance tomography (EIT), the sensor produces images that display the electrical conductivity distribution of its sensitive region to enable touch detection. The sensor was made from a natural gelatin hydrogel whose electrical conductivity is considerably less than that of human skin. The low conductivity of the sensor enabled a touch-detection mechanism based on a novel short-circuiting approach, which resulted in the reconstructed images being predominantly affected by the electrical contact between the sensor and fingertips, rather than the conventionally used piezoresistive response of the sensing material. The experimental results indicated that the proposed sensor was promising for detecting subtle contacts without the necessity of exerting a noticeable force on the sensor.

This paper improves the accuracy of quantification in the arterial diameter-dependent impedance variance by altering the electrode configuration. The finite element analysis was implemented with a 3D human wrist fragment using ANSYS Electronics Desktop, containing fat, muscle, and a blood-filled radial artery. Then, the skin layer and bones were stepwise added, helping to understand the dielectric response of multi-tissues and blood flow from 1 kHz to 1 MHz, the current distribution throughout the wrist, and the optimisation of electrode configurations for arterial pulse sensing. Moreover, a low-cost wrist phantom was fabricated, containing two components: the surrounding tissue simulant (20 wt % gelatine power and 0.017 M sodium chloride (NaCl) solution) and the blood simulant (0.08 M NaCl solution). The blood-filled artery was constricted using a desktop injection pump, and the impedance change was measured by the Multi-frequency Impedance Analyser (MFIA). The simulation revealed the promising capabilities of band electrodes to generate a more uniform current distribution than the traditional spot electrodes. Both simulation and phantom experimental results indicated that a longer spacing between current-carrying (CC) electrodes with shorter spacing between pick-up (PU) electrodes in the middle could sense a more uniform electric field, engendering a more accurate arterial diameter estimation. This work provided an improved electrode configuration for more accurate arterial diameter estimation from the numerical simulation and tissue phantom perspectives.

An accurate heart rate (HR) estimation using a radar sensor is challenging in real-life situations due to frequent changes in a person’s sitting position and posture. Furthermore, radar signal is affected by the presence of clutter noise, the harmonics associated with respiration (RR), and the movements of the body. To address these challenges, this article proposes a real-time beam steering algorithm and a signal processing technique based on Resonance Sparse Spectrum Decomposition (RSSD). Our beam steering method dynamically calculates the range–angle values of the target during the scanning phase and determines the target’s position at the beginning of each measurement cycle. This allows the beam-steered signal to be directed toward the individual, thereby improving the signal-to-noise ratio (SNR). We present a novel signal processing method based on RSSD that leverages sub-band energy distribution to optimize the quality factor (Q) and the subsequent extraction of HR using harmonics. The Q factor defines the resonance property of an oscillatory signal, and hence, signal components with similar center frequency bands but with a different quality factor, Q, can be separated and sparsely represented. The RSSD-based algorithm mitigates the effects of clutter and random body motion from the phase signal, which significantly enhances HR estimation accuracy. Comprehensive experiments performed under various realistic conditions demonstrate that the HR accuracy remains consistently high at 98.72% within a 4 m range across all azimuth angles.