Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users’ conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.

Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in diamond nano or microcrystals is a promising technology for sensitive, integrated magnetic-field sensors. Currently, this technology is still cost-intensive and mainly found in research. Here we propose one of the smallest fully integrated quantum sensors to date based on nitrogen vacancy (NV) centers in diamond microcrystals. It is an extremely cost-effective device that integrates a pump light source, photodiode, microwave antenna, filtering and fluorescence detection. Thus, the sensor offers an all-electric interface without the need to adjust or connect optical components. A sensitivity of 28.32$\mathrm{nT}/\sqrt{\mathrm{Hz}}$ and a theoretical shot noise limited sensitivity of 2.87 $\mathrm{nT}/\sqrt{\mathrm{Hz}}$ is reached. Since only generally available parts were used, the sensor can be easily produced in a small series. The form factor of (6.9 × 3.9 × 15.9) ${\mathrm{mm}}^3$ combined with the integration level is the smallest fully integrated NV-based sensor proposed so far. With a power consumption of around $0.1\mathrm{W}$, this sensor becomes interesting for a wide range of stationary and handheld systems. This development paves the way for the wide usage of quantum magnetometers in non-laboratory environments and technical applications.

Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.

We investigate the magnetic field-dependent fluorescence lifetime of microdiamond powder containing a high density of nitrogen-vacancy centers. This constitutes a non-intensity quantity for robust, all-optical magnetic field sensing. We propose a fiber-based setup in which the excitation intensity is modulated in a frequency range up to $100\mathrm{MHz}$. The change in magnitude and phase of the fluorescence relative to $B=0$ is recorded where the phase shows a maximum in magnetic contrast of ${5.8}^{\circ }$ at $13\mathrm{MHz}$. A lock-in amplifier-based setup utilizing the change in phase at this frequency shows a 100 times higher immunity to fluctuations in the optical path compared to the intensity-based approach. A noise floor of $20\mathsf{\mu }\mathrm{T}/\sqrt{\mathrm{Hz}}$ and a shot-noise-limited sensitivity of $0.95\mathsf{\mu }\mathrm{T}/\sqrt{\mathrm{Hz}}$ were determined.

In quantum sensing of magnetic fields, ensembles of nitrogen-vacancy centers in diamond offer high sensitivity, high bandwidth and outstanding spatial resolution while operating in harsh environments. Moreover, the orientation of defect centers along four crystal axes forms an intrinsic coordinate system, enabling vector magnetometry within a single diamond crystal. While most vector magnetometers rely on a known bias magnetic field for full recovery of three-dimensional (3D) field information, employing external 3D Helmholtz coils or permanent magnets results in bulky, laboratory-bound setups, impeding miniaturization of the device. Here, a novel approach is presented that utilizes a fiber-integrated microscale coil at the fiber tip to generate a localized uniaxial magnetic field. The same fiber-tip coil is used in parallel for spin control by combining dc and microwave signals in a bias tee. To implement vector magnetometry using a uniaxial bias field, the orientation of the diamond crystal is preselected and then fully characterized by rotating a static magnetic field in three planes of rotation. The measurement of vector magnetic fields in the full solid angle is demonstrated with a shot-noise-limited sensitivity of $19.4{\text{nT/Hz}}^{1/2}$ and microscale spatial resolution while achieving a fiber sensor head cross section of less than $1{\text{mm}}^2$.

The demand for energy storage is increasing massively due to the electrification of transport and the expansion of renewable energies. Current battery technologies cannot satisfy this growing demand as they are difficult to recycle, as the necessary raw materials are mined under precarious conditions, and as the energy density is insufficient. Metal–air batteries offer a high energy density as there is only one active mass inside the cell and the cathodic reaction uses the ambient air. Various metals can be used, but zinc is very promising due to its disposability and non-toxic behavior, and as operation as a secondary cell is possible. Typical characteristics of zinc–air batteries are flat charge and discharge curves. On the one hand, this is an advantage for the subsequent power electronics, which can be optimized for smaller and constant voltage ranges. On the other hand, the state determination of the system becomes more complex, as the voltage level is not sufficient to determine the state of the battery. In this context, electrochemical impedance spectroscopy is a promising candidate as the resulting impedance spectra depend on the state of charge, working point, state of aging, and temperature. Previous approaches require a fixed operating state of the cell while impedance measurements are being performed. In this publication, electrochemical impedance spectroscopy is therefore combined with various machine learning techniques to also determine successfully the state of charge during charging of the cell at non-fixed charging currents.