Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall $\ge 92\%$, precision $\ge 97\%$). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a $95\%$ confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.

Dedicated research is currently being conducted on novel thin film magnetoelectric (ME) sensor concepts for medical applications. These concepts enable a contactless magnetic signal acquisition in the presence of large interference fields such as the magnetic field of the Earth and are operational at room temperature. As more and more different ME sensor concepts are accessible to medical applications, the need for comparative quality metrics significantly arises. For a medical application, both the specification of the sensor itself and the specification of the readout scheme must be considered. Therefore, from a medical user’s perspective, a system consideration is better suited to specific quantitative measures that consider the sensor readout scheme as well. The corresponding sensor system evaluation should be performed in reproducible measurement conditions (e.g., magnetically, electrically and acoustically shielded environment). Within this contribution, an ME sensor system evaluation scheme will be described and discussed. The quantitative measures will be determined exemplarily for two ME sensors: a resonant ME sensor and an electrically modulated ME sensor. In addition, an application-related signal evaluation scheme will be introduced and exemplified for cardiovascular application. The utilized prototype signal is based on a magnetocardiogram (MCG), which was recorded with a superconducting quantum-interference device. As a potential figure of merit for a quantitative signal assessment, an application specific capacity (ASC) is introduced. In conclusion, this contribution highlights metrics for the quantitative characterization of ME sensor systems and their resulting output signals in biomagnetism. Finally, different ASC values and signal-to-noise ratios (SNRs) could be clearly presented for the resonant ME sensor (SNR: $-90$ dB, ASC: $9.8\times {10}^{-7}$ dB Hz) and also the electrically modulated ME sensor (SNR: $-11$ dB, ASC: 23 dB Hz), showing that the electrically modulated ME sensor is better suited for a possible MCG application under ideal conditions. The presented approach is transferable to other magnetic sensors and applications.

Objective: Electroneurography has been an essential method for assessing peripheral nerve disorders for decades. During this procedure, a nerve is briefly electrically excited, and nerve conduction properties are identified by indirect means from the behavior of the innervated muscle. The magnetic field of the resulting muscle response can also be recorded by novel, uncooled magnetometers, which have become very attractive for different medical applications over recent years. These highly sensitive magnetometers are called optically pumped magnetometers. Methods: We performed unaveraged and averaged magnetic signal detection of electrically evoked muscle responses using optically pumped magnetometers. We then discussed the suitability of this procedure for clinical applications in the context of diagnostic value and in direct comparison with the current electrical gold standard. Results: The magnetic detection of muscle responses is possible using optically pumped magnetometers. Our magnetic results (averaged and unaveraged) closely match those from electrical measurements. Conclusion: Optically pumped magnetometers provide an alternative, contactless technology for electrode-based motor studies, but they are currently not ready for routine clinical use. This costly technology requires additional earth magnetic shielding because this is a prerequisite for proper operation. Currently, there are no diagnostic advantages over electrical measurements. Additionally, the required measurement setup and procedure are much more complicated. Significance: In contrast to already published proof-of-principle studies for magnetomyography, we report in detail the results of the magnetic measurements of electrically evoked muscle responses in a shielded environment by applying supramaximal stimulation and finally validate our findings with electroneurography data as a reference.

In principle, electrode-based bioelectrical signal acquisition can be complemented by biomagnetic sensing and therefore requires a more detailed assessment, especially because of the availability of novel noncryogenic sensor technologies. The current development of thin film magnetoelectric (ME) sensors ensures that the ME technology is becoming a prospective candidate for biomagnetometry. The main obstacle for large-scale usage is the lack of extremely low noise floors at the final sensor system output. This article highlights the current state of ME sensor development based on a magnetocardiography pilot study involving a healthy volunteer in a magnetically shielded chamber. For assessment, an ME prototype ( converse magnetoelectric thin film sensor ) will be applied for the first time. This sensor type ensures a noise amplitude spectral density below 20 pT/√ Hz at 10 Hz by using a sophisticated magnetic layer system. The main aim of this pilot study is to evaluate the applicability of this promising sensor for the detection of a human heart signal and to evaluate the sensor output with competitive optical magnetometry technology. A magnetic equivalent of a human R wave could be successfully detected within a one-minute measurement period with the sensor presented here. Finally, the article will provide an outlook on future ME perspectives and challenges, especially for cardiovascular applications.

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease.

In the development of any type of magnetic field sensor based on magnetic films, special consideration must be given to the magnetic layer component. The presented work investigates the use of scalable flux closing magnetostrictive multilayers for inverse magnetoelectric sensors. In such a type of magnetic field sensor, highly sensitive AC and DC field detection relies on strong excitation of the incorporated magnetic layers by piezoelectrically driven cantilever oscillation at mechanical resonances. The provoked periodic flux change is influenced by the magnetic field to be measured and is picked up by a coil, which generates the measured output. The multilayered inverse magnetoelectric sensor is investigated with regard to linearity, noise behavior, and detection limit of DC and AC signals. A significant advancement for inverse magnetoelectric thin film sensors is demonstrated in this study. Using exchange bias stabilized magnetic multilayers with flux closure structures, detection limits are improved by an order of magnitude to less than 8 pT/Hz1/2 at 10 Hz and 18 pT/Hz1/2 at DC.