Magnetography with superconducting quantum interference device (SQUID) sensor arrays is a well-established technique for measuring subtle magnetic fields generated by physiological phenomena in the human body. Unfortunately, the SQUID-based systems have some limitations related to the need to cool them down with liquid helium. The room-temperature alternatives for SQUIDs are optically pumped magnetometers (OPM) operating in spin exchange relaxation-free (SERF) regime, which require a very low ambient magnetic field. The most common two-layer magnetically shielded rooms (MSR) with residual magnetic field of 50 nT may not be sufficiently magnetically attenuated and additional compensation of external magnetic field is required. A cost-efficient compensation system based on square Helmholtz coils was designed and successfully used for preliminary measurements with commercially available zero-field OPM. The presented setup can reduce the static ambient magnetic field inside a magnetically shielded room, which improves the usability of OPMs by providing a proper environment for them to operate, independent of initial conditions in MSR.

Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.

The aim of the study was to verify the possibility of application of multifilament carbon fibre electrodes for electrical discharge machining process. In the article, the novel type of tool electrode in form of a rod that is composed of carbon fibres was proposed. The performed analysis taking into account the influence of input parameter from RC generator like voltage, capacitance and resistance along with evaluation of the process with this novel type of electrodes was considered. The manufacturing technique was assessed by analysing the geometry parameters of machined holes like diameters and maximum depth. The detailed analysis and quantitative values of the impact of each of the input factors were presented and response surfaces according to the experimental methodology were obtained. The key efficiency parameter which was expressed by material removal rate (MRR) was also calculated and compared with data for other materials used as tool electrodes that were presented previously in literature. The comparison study proved that with multifilament carbon fibre electrodes the comparable range of MRR parameter in similar electro-erosive conditions can be obtained. Thus, they can be considered as complementary material for tool electrode purposes.

The paper describes the semi-automatised calibration procedure of an electronic skin comprising screen-printed graphene-based sensors intended to be used for robotic applications. The variability of sensitivity and load characteristics among sensors makes the practical use of the e-skin extremely difficult. As the number of active elements forming the e-skin increases, this problem becomes more significant. The article describes the calibration procedure of multiple e-skin array sensors whose parameters are not homogeneous. We describe how an industrial robot equipped with a reference force sensor can be used to automatise the e-skin calibration procedure. The proposed methodology facilitates, speeds up, and increases the repeatability of the e-skin calibration. Finally, for the chosen example of a nonhomogeneous sensor matrix, we provide details of the data preprocessing, the sensor modelling process, and a discussion of the obtained results.

An integrated circuit for real-time, simultaneous recording of electrical and chemical(pH) signatures of neural activity is presented. It consists of six recording channels, three for electrical and three for chemical signals. Each channel has a low-noise amplifier which utilizes a new and optimized technique of flicker noise suppression. Our approach is based on a modified switched bias current technique. Both electrical and chemical amplifiers use the same circuit topology and have a common footprint, but the type of amplifier is reconfigured by controlling the bias current, which controls the bandwidth of the amplifier. The chip was fabricated using an AMS 0.35μm and four metal double poly CMOS technology. Measured noise reduction depends on the switching frequency. Bench tests have shown that our technique achieves up to 60% intrinsic 1/f noise suppression by determining an optimal switching frequency, without additional circuit complexity or an increase in transistor and chip area. The amplifier was designed to exhibit a 60 dB closed loop gain for electrical neural signals, and a 20 dB closed loop gain neurochemical signals. Each channel within the design occupied an area of 3.66mm ² . This circuit design will be used as an integrated circuit for a real-time bimodal recording of neural activity for closed-loop neuromodulation therapies.

We demonstrate a novel feedback mechanism to implement closed loop neuromodulation based on a combination of electrical and chemical neural signal recording. The platform consists of our custom designed multi-channel amplifier for recording electrical and chemical signals (such as pH), data processing unit and the stimulator IC unit. A novelty of our approach is that the neural chemical signals are used to initiate stimulation and electrical compound action potentials (CAPs) to determine stimulation dosage. The proposed adaptive stimulation platform has been implemented in the context of Vagus Nerve Stimulation (VNS) for obesity therapy. The demonstration is based on externally generated signals derived from our in vivo experiments on physiological stimulation of the gastric branch of vagus nerve by injection of gut hormone cholecystokinin (CCK).