Rhythmicity is an important feature of tremor that is widely viewed as having diagnostic significance. We hypothesized that rhythmicity might be a generic function of tremor severity, reflecting greater oscillatory entrainment of motor pathways. Postural hand tremor and forearm electromyograms were recorded from 49 controls, 78 Parkinson patients, and 133 essential tremor patients. Rhythmicity was quantified in terms of approximate entropy and three measures of cycle-to-cycle frequency variability: tremor stability index, cycle-to-cycle variability, and power spectral bandwidth. Physiological tremor was less rhythmic than Parkinson tremor and essential tremor, but the two pathological tremors did not differ significantly. Hand tremor amplitude and forearm electromyogram-hand tremor coherence were moderate statistical predictors of rhythmicity in both pathological tremors. Adding a 1-kg weight to the hand had little effect on the rhythmicity metrics, except for a moderate reduction in the approximate entropy of physiological tremor. We conclude that these four rhythmicity metrics are not helpful in distinguishing postural tremors in Parkinson disease and essential tremor. The moderate correlations of rhythmicity with tremor amplitude and electromyogram coherence suggest that rhythmicity of pathological tremors is largely a generic reflection of oscillatory neuronal entrainment. Comparisons of tremor rhythmicity in pathological conditions must control for tremor amplitude and electromyogram coherence.

Auditory feedback modulation (AFM)-altering how speakers hear their own voice during phonation-is a well-established method for investigating vocal motor control. Healthy speakers typically respond to AFM with compensatory vocal adjustments in the opposite direction of the perturbation, such as lowering pitch in response to upward pitch shifts. However, whether degradations in voice quality within auditory feedback elicit comparable compensatory behavior-specifically, vocal adjustments that enhance acoustic voice quality-remains unknown. To address this gap, the present study pursued two aims: (1) to introduce and evaluate a real-time voice resynthesis system, VQ-Synth, designed to induce the percept of hoarseness in otherwise healthy voices, and (2) to test whether hoarseness-induced auditory feedback leads to compensatory improvements in acoustic voice quality, measured by smoothed cepstral peak prominence (CPPS). In Study 1, participants rated recordings of their own voice processed with four different resynthesis methods. Overall, the anti-peak-window method, which inserted noise between the pitch-related amplitude peaks, produced the strongest percept of dysphonia. In Study 2, this method was applied in an AFM experiment to modulate voice quality in real time. Participants sustained the vowel // 140 times. In the AFM group, auditory feedback was modulated according to a phase design (baseline, ramp, hold, and after), whereas the control group received unaltered feedback throughout. CPPS in the AFM group increased significantly from baseline through ramp and hold and remained elevated in the after phase, while there was no CPPS increase in the control group. These results indicate that the observed improvement in acoustic voice quality cannot be attributed to practice effects, but was specifically driven by hoarseness-induced AFM. Future work will explore VQ-Synth's potential in connected speech and its application as a therapeutic tool for individuals with dysphonia.

The use of multibeam MIMO-SONAR systems on marine vehicles (e.g. remotely operated vehicles, ROVs) enables the visual 3D reconstruction of the water-column by hydroacoustic ensonification of the surrounding environment in real-time. For underwater target detection and classification purposes, the processed SONAR data must be visualized for interpretation of the results by a human operator and to allow for a corresponding re-adjustment of the system parametrization during operation. Therefore, conventional 2D visualization approaches such as the plan position indicator (PPI) plot must be adapted to 3D. Challenges such as different signal-to-noise ratios, beamforming artifacts, and overlapping objects must be considered when choosing how to visualize the processed data to allow for the correct semantic interpretation of the scanned water-column. In this presentation, an approach for the 3D voxel- and mesh-based visualization of real-time processed multibeam SONAR data is shown. The focus will be on how to consider the 3D beamforming and signal correlation processing, combined with data interpolation and filtering techniques, to allow for a visual reconstruction of the water-column from the SONAR data. An implementation of this approach in the C++ programming language using the Qt visualization framework will be shown in the Kiel Real-time Application Toolkit (KiRAT) for a virtual ocean environment.

Magnetic sensors are highly relevant in clinical and industrial applications such as localization tasks and geological investigations. The spatial behavior of these sensors is of great interest for accurate forward modeling and the consequential possibilities for sophisticated applications, e.g., solutions to inverse problems. In this contribution, we present a novel characterization approach using adaptive system identification approaches. We utilize a gradient-based algorithm for estimating impulse and corresponding frequency responses for a directivity analysis in 1D, 2D, and 3D. For this, we built a triaxial Helmholtz coil setup to generate a 3D directive field. This is controlled by an algorithm that exploits similarities in sensor behavior with respect to small differences in excitation field angles. We found advantages for a controlled adaptation, with faster convergence and a smaller system distance between estimations and measurements with a proposed control based on the contraction-expansion approach (CEA). With runtimes averaging less than 1.5 s per direction for full impulse response estimation, this proof of concept shows the potential of the proposed algorithm for enabling a feasible frequency and directivity characterization method.

An impaired voice quality (i.e., hoarseness) is a key symptom of voice disorders, including functional dysphonia. Auditory feedback modulation (AFM) of voice quality, in which patients' existing hoarseness is intensified through headphones, could potentially offer a noninvasive treatment approach by encouraging compensatory vocal adjustments that improve voice quality. This study aims to artificially induce hoarseness using neural networks (NNs) for the modification of the cepstrum. Since cepstral parameters like cepstral peak prominence (CPP), jitter, and shimmer reflect voice quality, we propose that targeted cepstral modifications could be used to simulate hoarseness. A multilayer perceptron (MLP), as a very simple form of a neural network, ensures real-time processing, paving the way for using artificial intelligence in future AFM applications in speech therapy and training.

Clinical motion analysis plays an important role in the diagnosis and treatment of mobility-limiting diseases. Within this assessment, relative (point-to-point) tracking of extremities could benefit from increased accuracy. Given the limitations of current wearable sensor technology, supplementary spatial data such as distance estimates could provide added value. Therefore, we propose a distributed magnetic tracking system based on early-stage demonstrators of novel magnetoelectric (ME) sensors. The system consists of two body-worn magnetic actuators and four ME sensor arrays (body-worn and fixed). It is enabled by a comprehensive signal processing framework with sensor-specific signal enhancement and a gradient descent-based system calibration. As a pilot study, we evaluated the technical feasibility of the described system for motion tracking in general (Scenario A) and for operation during treadmill walking (Scenario B). At distances of up to 60 cm, we achieved a mean absolute distance error of 0.4 cm during gait experiments. Our results show that the modular system is capable of centimeter-level motion tracking of the lower extremities during treadmill walking and should therefore be investigated for clinical gait parameter assessment.

AbstractConventional active SONAR systems often use beamformers and matched filters separately to extract bearing and range information from the received signal and offer a straightforward way of creating a two‐dimensional map of the environment. In SONAR systems the minimum‐variance‐distortionless‐response beamformer (MVDR beamformer) is a commonly used type of beamformer, which will reconstruct the receive signal from a certain direction optimally. In terms of detecting the transmit signal, the most used method is the conventional matched filter. Both algorithms are simple to implement and perform well under various noise scenarios. The proposed method combines the beamformer and matched filter by introducing an extended channel model that allows the derivation of a multichannel Wiener filter to solve for the unknown reflection coefficients of the complete two‐dimensional environment. This results in adaptively calculated filter weights that will drastically improve the performance compared to a separate MVDR beamformer and matched filter. In addition, a parameter is introduced with which one can arbitrarily adjust the focus between angular and temporal resolution depending on the application. After the derivation, the performance is demonstrated with simulations and measurements.

Accurate tremor classification is crucial for effective patient management and treatment. However, clinical diagnoses are often hindered by misdiagnoses, necessitating the development of robust technical methods. Here, we present a two-stage convolutional neural network (CNN)-based system for classifying physiological tremor, essential tremor (ET), and Parkinson’s disease (PD) tremor. Employing acceleration signals from the hands of 408 patients, our system utilizes both medically motivated signal features and (nearly) raw data (by means of spectrograms) as system inputs. Our model employs a hybrid approach of data-based and feature-based methods to leverage the strengths of both while mitigating their weaknesses. By incorporating various data augmentation techniques for model training, we achieved an overall accuracy of 88.12%. This promising approach demonstrates improved accuracy in discriminating between the three tremor types, paving the way for more precise tremor diagnosis and enhanced patient care.

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.

Electroanatomical mapping (EAM) is an essential tool for diagnosing and treating cardiac arrhythmias. Traditional non-invasive EAM methods, especially those based on magnetocardiography (MCG), face challenges due to the limited amount of sensors. This paper introduces a dynamic sensor array approach designed to overcome these limitations. Unlike traditional static sensor arrays, our approach leverages sensor repositioning between heartbeats, enabling enhanced spatial res-olution and more accurate current density estimation without increasing the sensor count. Our simulations demonstrate that the dynamic sensor array significantly enhances the performance of the EAM algorithm, achieving better estimations of current densities and improved separation of healthy and pathological regions. Specifically, the dynamic array with six motion steps per axis improved the Dice score from 0.82 to 0.95, precision from 0.95 to 0.98, and recall from 0.73 to 0.92 compared to a static array. These findings suggest that dynamic sensor arrays could substantially reduce the cost and improve the performance of non-invasive EAM, potentially transforming cardiac diagnostics.

Magnetic motion tracking offers advantages in applications where other established methods are constrained due to limited mobility, non-line-of-sight conditions, or missing long-term stability. This contribution combines previous work on a hardware setup and a real-time magnetic signal preprocessing pipeline with a revised algorithm for position and orientation estimation. Finally, we demonstrate full 6D tracking with an early-stage triaxial array demonstrator of the magnetoelectric (ME) sensing principle. Therefore, we track relative movements toward a coil and validate the results against an optical motion capturing system. While moving the sensor at ranges of up to 50 cm, we obtained average errors of 1.4 cm for distance, 6.4 cm for position, and 27 degrees for orientation.

The swallowing process involves complex muscle coordination mechanisms. When alterations in such mechanisms are produced by neurological conditions or diseases, a swallowing disorder known as dysphagia occurs. The instrumental evaluation of dysphagia is currently performed by invasive and experience-dependent techniques. Otherwise, non-invasive magnetic methods have proven to be suitable for various biomedical applications and might also be applicable for an objective swallowing assessment. In this pilot study, we performed a novel approach for deglutition evaluation based on active magnetic motion sensing with permanent magnet cantilever actuators. During the intake of liquids with different consistency, we recorded magnetic signals of relative movements between a stationary sensor and a body-worn actuator on the cricoid cartilage. Our results indicate the detection capability of swallowing-related movements in terms of a characteristic pattern. Consequently, the proposed technique offers the potential for dysphagia screening and biofeedback-based therapies.

The so-called Lombard effect, first published by Étienne Lombard more than a hundred years ago, describes the observation of an involuntary change in a speaker’s voice to improve the own audibility while speaking in loud environments. This change affects not only loudness but also other perceptual properties of human voice, such as pitch, speaking rate, and duration of syllables. Since the Lombard effect occurs in noisy acoustic environments, the disturbing sounds must be removed before the effects on the voice could be investigated. However, it is difficult to remove the noise from the recordings without distorting the speech in a real noise environment. In most cases, closed headphones are used to generate noise-free recordings. This way of generating Lombard recordings changes the feedback path from the mouth to the ear. This disturbance of the hearing impression of the own voice can be avoided by equalizing the recorded speech and playing it back as a sidetone. But even the presence of headphones changes the perception and thus influences the Lombard effect. To address this issue, this chapter describes an acoustic simulation environment, which is used to reproduce different noise scenarios of vehicle interiors. Loudspeakers are used to generate the noise of a driving car. The system can be used in a soundproof room as well as in a vehicle, which increases the realism. Furthermore, orthogonal loudspeaker signals are used to facilitate the removal of the simulated noise by a multichannel noise cancellation algorithm.

Magnetic motion sensing enables non-contact tracking of relative position and orientation in 3D space. With recent advances in sensor and actuator devices, applications in human movement analysis seem feasible. However, establishing a setup from scratch in terms of hardware and software is challenging. Therefore, we introduce a comprehensive simulation pipeline based on a digital twin concept that enables the design and validation of new approaches based on kinematics, magnetics, and digital real-time signal processing. We also elaborate on related applications.

IntroductionThe clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings.MethodsHere, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data.Results and discussionThe results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

There are numerous magnetic field sensors available, but no simple, robust, sensitive sensor for biomedical applications that does not require cryogenic cooling or shielding has yet been developed. In this contribution, a new approach for building a magnetoelectric field sensor is presented, which has the potential to fill this gap. The sensor is based on a resonant cantilever with a piezoelectric readout layer and a pair of opposing permanent magnets. One is attached to the cantilever, and the other one is fixed to a sample holder below. This new concept can be deduced from the most basic composite-based sensor [1], where the magnets interact analog to two particles in a polymer matrix. The bias-free, empirical measurements show a limit-of-detection of 46 pT/root Hz with a sensitivity of 2170 V/T using the sensor's resonance frequency of 223.5 Hz under ambient conditions. The sensor fabrication is based on low resolution silicon technology, which promises high compatibility and the possibility to be integrated into MEMS devices. The design of this new sensor can be easily altered and adjusted according to the requirements of the specific sensor application. For example, tuning of the operating resonance frequency cannot solely be modified in the production of the cantilever but also by the arrangement of the permanent magnets. In addition, the concept can also be applied to energy harvesters. Beside possible mechanical excitation, the presence of a magnetic stray field alone allows the sensor to convert 20 mu T into a power of 1.31 mu W/cm(3)Oe(2). The fact that the device does not require any DC bias field makes it very attractive for energy harvesting applications since this allows a purely passive operation. In this manuscript, the sensor assembly, measurements of directional sensitivity, noise level, limit-of-detection, evaluation for energy harvesting applications from magnetic fields and a quantitative sensor model are presented.

Accurate calibration is key for any reliable sensor system. Magnetoelectric (ME) sensors, in particular, are influenced in their operating point by external parameters such as the Earth’s magnetic field or the ambient temperature. In this paper, we introduce a new planar coil design for the generation of a magnetic test field within the plane of the ME sensor. Furthermore, we implemented a method for measuring the sensor behaviour using a short-term magnetic noise signal. The combination of the printed circuit board (PCB) coil and the accelerated sensor characterization method allows the sensor system to be calibrated at the measurement site (in situ) without the need for laboratory equipment. We can show that the presented method for calibration achieves high-quality results in 10 seconds for a sensor affected by external interference fields.

Characterising underwater acoustic transducers is essential to optimal signal processing in sound navigation and ranging systems. Precise characterisation allows for equalisation of the input and output hardware, resulting in improved performance of the overall system. A critical quantity of the characterisation is the impulse response of the underwater transducers, which are usually measured in a special low-noise water tank. An established method in other fields to estimate impulse responses of unknown systems is using adaptive filters and include an inherent quality measure. Such approaches allow for very fast and very reliable measurements. However, when using fixed control parameters, a trade-off between convergence speed and final mismatch needs to be found, which can be eliminated using variable control parameters. In this article, a method for determining an optimal step size for adaptive algorithms based on the normalised least mean square method is derived based on a theoretical analysis of the convergence process by taking the reverberation parameters of such measurement tanks into account. The new method is specialised on the application of underwater transducer characterisation and allows a very reliable approximation of the optimal step size and thus a maximally fast adaption behaviour-leading to a very short measurement time. This is firstly shown in simulations and afterwards demonstrated in a real measurement with unknown transducers in a measurement water tank.

In this paper we present a novel noninvasive approach to estimate current densities in the heart from magnetocardiography. The proposed algorithm uses nested optimization to model current densities in equally-sized voxels of myocardial tissue. First-order Thiran all-pass filters are used to describe the propagation between voxels.We demonstrate feasibility of the algorithm for a noise-free single-layer simulation. However, challenges remain, such as addressing measurement noise and optimizing propagation velocity. Overall, this approach has the potential to complement or replace invasive catheter-based electrophysiological studies for localization of arrhythmogenic tissue.

In today's world, conference calls are becoming more and more important. It is possible to talk to participants from different countries anytime and anywhere. The disadvantage of such meetings is that it is sometimes incredibly difficult to identify talkers by voice alone. This is even more noticeable in meetings with many participants. One solution is to highlight the talker with a visual indicator. If such meetings are held in a car, for example, this is not possible. This chapter therefore presents a solution that uses virtual sound sources to spatially distribute participants in order to convey the acoustic impression that the other participants are distributed in the same room. Various methods for spatial distribution, such as amplitude panning, are presented. Furthermore, a Wiener filter-based cross talk suppression is discussed. The distribution of participants works dynamically with a fading method if the number of participants changes. Furthermore, the presented system includes a base station functionality.