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Surface electromyography (sEMG) patterns have been decoded using learning-based methods that determine complicated nonlinear decision boundaries. However, overlapping classes in sEMG pattern recognition still degrade the classification accuracy because they cannot be separated by the decision boundaries. We hypothesized that certain overlapping classes can be separated while tracing the temporal history of sEMG patterns. Therefore, a novel post-processing method is proposed to adjust classification errors using the separated patterns from the temporal history of overlapping classes. The proposed method confirms the confidence of the prediction result by calculating the instantaneous pattern separability for the sequential sEMG input. The prediction result with high separability pattern is considered to have a high confidence of being correct (reliable). This result is stored for adjusting the next sEMG input. When the subsequent prediction is identified as having low confidence (unreliable), the predicted result is adjusted using the stored reliable predicted results. The proposed method adds an adjustment step to an existing classifier (maximum likelihood classifier (MLC), k-nearest neighbor (KNN), and support vector machine (SVM)), such that it can be attached to the back-end regardless of the type of classifier. Ten subjects performed 13 types of hand gestures, including overlapping patterns. The overall classification accuracy was enhanced to 88.93%(+8.12%p, MLC), 91.31%(+7.68%p, KNN), and 99.65%(+11.63%p, SVM) after the implementation of the proposed post-processing. Additionally, a faster and more accurate gesture classification was achieved with accuracy enhancement before gesture completion as 85.62%(+4.23%p, MLC), 89.77%(+4.23%p, KNN), and 97.62%(+11.12%p, SVM).

Haptic interactions play an essential role in education to enhance learning efficiency; however, haptic information for virtual educational content remains lacking. This article proposes a planar cable-driven haptic interface with movable bases that can display isotropic force feedback with maximum workspace extension on a commercial screen display. A generalized kinematic and static analysis of the cable-driven mechanism is derived by considering movable pulleys. Based on the analyses, a system including movable bases is designed and controlled to maximize the workspace subject to isotropic force exertion for the target screen area. The proposed system is evaluated experimentally as a haptic interface represented by the workspace, isotropic force-feedback range, bandwidth, Z-width, and user experiment. The results indicate that the proposed system can maximize workspace to the target rectangular area and exert isotropic force up to 94.0% of the theoretical computed one within the workspace.

Monochromatic infrasound waves are scarcely reported volcanic infrasound signals. These waves have the potential to provide constraints on the conduit geometry of a volcano. However, to further investigate the waves scientifically, such as how the conduit shape modulates the waveforms, we still need to examine many more examples. In this paper, we provide the most detailed descriptions of these monochromatic infrasound waves observed at Aso volcano in Japan. At Aso volcano, a 160-day-long magmatic eruption occurred in 2014–2015 after a 20-year quiescent period. This eruption was the first event that we could monitor well using our infrasound network deployed around the crater. Throughout the entire eruption period, when both ash venting and Strombolian explosions occurred, monochromatic infrasound waves were observed nearly every day. Although the peak frequency of the signals (0.4–0.7 Hz) changed over time, the frequency exhibited no reasonable correlation with the eruption style. The source location of the signals estimated by considering topographic effects and atmospheric conditions was highly stable at the active vent. Based on the findings, we speculate that these signals were related to the resonant frequencies of an open space in the conduit: the uppermost part inside the vent. Based on finite-difference time-domain modeling using 3-D topographic data of the crater during the eruption (March 2015), we calculated the propagation of infrasound waves from the conduit. Assuming that the shape of the conduit was a simple pipe, the peak frequency of the observed waveforms was well reproduced by the calculation. The length of the pipe markedly defined the peak frequency. By replicating the observed waveform, we concluded that the gas exhalation with a gas velocity of 18 m/s occurred at 120 m depth in the conduit. However, further analysis from a different perspective, such as an analysis of the time difference between the arrivals of infrasound and seismic waves, is required to more accurately determine the conduit parameters based on observational data.

In recent years, two sets of chemical explosions have been conducted at the Nevada National Security Site: six explosions from Phase I of the Source Physics Experiment and four explosions from the Forensics Surface Events. We have estimated the explosive yield and depth‐of‐burial/height‐of‐burst of both from the synthesis of seismic and low‐frequency acoustic data. Analysis of the seismic signal is performed using a waveform envelope technique. Using seismic data only, however, there is a trade‐off between the yield and depth or height of the explosion, which becomes especially acute for near‐surface events. This trade‐off between yield and depth/height can be broken by including complementary analysis of the acoustic signal using the acoustic waveform inversion method. The acoustic signal also has a strong trade‐off between a yield and depth/height, but the slope is opposite that of the seismic for near‐surface events. We use a likelihood function to combine the seismic and acoustic analysis and improve our estimates of yield and depth/height for these sets of explosions. The results are generally consistent with the known parameters, but can be improved upon by higher‐frequency seismic analysis and improved acoustic impulse scaling for large‐scaled depths‐of‐burial. These events serve as prime examples of data fusion from multiple data streams. The challenge will be to apply the method to events at longer regional distances where the seismic signals will include phases traveling in the upper mantle, and the acoustic signals will be recorded as infrasound signals, which are subject to atmospheric variability. We combine seismic waves traveling in the solid Earth with acoustic waves traveling in the air to estimate the explosive yield of a series of chemical explosions conducted in Nevada. We find that combining the two data sets significantly improves on the analysis performed with either seismic waves or acoustic waves alone. Using seismoacoustic analysis, we estimate the yield and depth of a series of chemical explosions conducted in NevadaExplosion parameter estimation is improved by combining seismic and acoustic analysis using likelihood functionsWe suggest several steps to further improve seismoacoustic explosion source analysis

Local infrasound is widely used to constrain source parameters of near‐surface events (e.g., chemical explosions and volcanic eruptions). While atmospheric conditions are critical to infrasound propagation and source parameter inversion, local atmospheric variability is often ignored by assuming homogeneous atmospheres, and their impact on the source inversion uncertainty has never been accounted for due to the lack of quantitative understanding of infrasound variability. We investigate atmospheric impacts on local infrasound propagation by repeated explosion experiments with a dense acoustic network and in situ atmospheric measurement. We perform full 3‐D waveform simulations with local atmospheric data and numerical weather forecast model to quantify atmosphere‐dependent infrasound variability and address the advantage and restriction of local weather data/numerical weather model for sound propagation simulation. Numerical simulations with stochastic atmosphere models also showed nonnegligible influence of atmospheric heterogeneity on infrasound amplitude, suggesting an important role of local turbulence. Energetic events near or above the Earth's surface can release explosion energy in the form of acoustic waves in the atmosphere. Local infrasound (low‐frequency acoustic wave) can contain valuable source information and becomes increasingly useful to constrain source parameters of the events (e.g., the size of chemical explosions and the erupted mass during volcanic explosions). Local weather conditions can also affect infrasound propagation, and these propagation path effects must be removed from the signals to obtain accurate source information. However, these atmospheric effects are often ignored by assuming oversimplified atmospheres in many applications due to the lack of quantitative understanding of it. In this study, we investigate atmospheric impacts on local infrasound propagation by using explosion experiment data and in situ atmospheric measurements. Full 3‐D waveform simulations are performed with local atmospheric data, providing quantitative information about the sound pressure variation due to atmospheric conditions. In addition, we simulate sound propagation with stochastic atmosphere models and show nonnegligible impacts of atmospheric heterogeneity on infrasound amplitude, suggesting an important role of local turbulence. Local infrasound variability is studied by explosion experiments with local atmospheric measurementsSound pressure levels are predicted by full 3‐D waveform modeling using in‐situ atmospheric profileWaveform simulations suggest nonnegligible impacts of local atmospheric heterogeneity on infrasound

The August 2020 Beirut explosion is the largest single‐fired ammonium nitrate explosion documented in history. The massive explosion excited loud infrasound in the atmosphere, and clear waveforms were recorded by a regional infrasound array at an epicentral distance of 100 km, allowing for accurate measurements of explosion energy. We estimate the explosion size based on the infrasound waveform inversion. Unlike conventional inversions using empirical models, we perform full 3‐D finite‐difference simulations to obtain a physics‐based propagation model for the inversion. Accurate numerical modeling of infrasound is challenging as the propagation is substantially affected by the turbulent atmosphere. Instead of a single deterministic prediction, we provide a range of waveform predictions by running multiple simulations with stochastic weather forecast models, which allows for comprehensive uncertainty analysis of numerical modeling and estimated yields. Finally, we expand the yield estimation technique for seismoacoustic analysis and demonstrate the substantial advantage of the joint approach. On 4 August 2020, 2,750 metric tons of ammonium nitrate stored at the Port of Beirut in Lebanon exploded. It is the largest single‐fired ammonium nitrate explosion documented in history and produced unusually loud infrasound (low‐frequency sound below 20 Hz) up to about 6,000 km away. We estimate the size of the Beirut explosion based on the infrasound, whose waveforms were clearly recorded at a regional infrasound array at an epicentral distance of 100 km. These clear waveforms provide a rare opportunity to invert infrasound signals for the explosion yield. Unlike conventional inversions using a simple regression analysis, we use full 3‐D physics‐based numerical simulations for infrasound propagation to predict the observed waveforms. We calculate energy attenuation from the explosion site to the observing stations and then infer the explosion size based on the calculation. Numerical modeling of regional infrasound at this range is particularly challenging because of the highly turbulent atmosphere. We use a stochastic weather forecast model to capture the variability of weather condition and provide a quantitative uncertainty analysis of waveform prediction. Explosion yield of the 2020 Beirut explosion is estimated based on infrasound waveformsFull 3‐D physics‐based numerical simulations are performed to predict infrasound waveform and energy attenuation in atmospheric propagationJoint seismoacoustic analysis is explored to improve the explosion yield and depth constraint Explosion yield of the 2020 Beirut explosion is estimated based on infrasound waveforms Full 3‐D physics‐based numerical simulations are performed to predict infrasound waveform and energy attenuation in atmospheric propagation Joint seismoacoustic analysis is explored to improve the explosion yield and depth constraint

We present an acoustic waveform inversion technique for infrasound data to estimate volume fluxes from volcanic eruptions. Previous inversion techniques have been limited by the use of a 1‐D Green's function in a free space or half space, which depends only on the source‐receiver distance and neglects volcanic topography. Our method exploits full 3‐D Green's functions computed by a numerical method that takes into account realistic topographic scattering. We apply this method to vulcanian eruptions at Sakurajima Volcano, Japan. Our inversion results produce excellent waveform fits to field observations and demonstrate that full 3‐D Green's functions are necessary for accurate volume flux inversion. Conventional inversions without consideration of topographic propagation effects may lead to large errors in the source parameter estimate. The presented inversion technique will substantially improve the accuracy of eruption source parameter estimation (cf. mass eruption rate) during volcanic eruptions and provide critical constraints for volcanic eruption dynamics and ash dispersal forecasting for aviation safety. Application of this approach to chemical and nuclear explosions will also provide valuable source information (e.g., the amount of energy released) previously unavailable. First waveform inversion of volcano infrasound using a 3‐D Green's functionStrong topographic scattering must be considered for source inversionThe method substantially improves the accuracy of eruption source parameter estimation

Acoustic source inversions estimate the mass flow rate of volcanic explosions or yield of chemical explosions and provide insight into potential source directionality. However, the limitations of applying these methods to complex sources and their ability to resolve a stable solution have not been investigated in detail. We perform synthetic infrasound waveform inversions that use 3‐D Green’s functions for a variety of idealized and realistic deployment scenarios using both a flat plane and Yasur volcano, Vanuatu as examples. We investigate the ability of various scenarios to retrieve the input source functions and relative amplitudes for monopole and multipole (monopole and dipole) inversions. Infrasound waveform inversions appear to be a robust method to quantify mass flow rates from simple sources (monopole) using deployments of infrasound sensors placed around a source, but care should be taken when analyzing and interpreting results from more complex acoustic sources (multipole) that have significant directional components. In the examples we consider the solution is stable for monopole inversions with a signal‐to‐noise ratio greater than five and the dipole component is small. For most scenarios investigated, the vertical dipole component of the multipole explosion source is poorly constrained and can impact the ability to recover the other source term components. Because multipole inversions are ill‐posed for many deployments, a low residual does not necessarily mean the proper source vector has been recovered. Synthetic studies can help investigate the limitations and place bounds on information that may be missing using monopole and multipole inversions for potentially directional sources. Explosions such as those from volcanoes create low‐frequency sound below the threshold of human hearing (infrasound) that can be recorded at a variety of distances. Infrasound can be used to calculate the amount of material being ejected from a volcano and if the sound waves are preferentially emitted in a certain direction. However, this directionality can be difficult to determine because infrasound sensors are often placed on the ground surface, which makes determining the vertical directionality of an explosion source difficult. We use numerical model examples of infrasound deployments around a volcanic source and vary parameters including the station locations, noise level, strength of directionality, and impact of wind to investigate the ability to recover the input source parameters. We find that assuming an infrasound source radiates equally in all directions is good enough even for directional sources if the directional component is small and the signal is five times higher in amplitude than the noise. However, caution should be used for sources that are directional for many infrasound sensor deployments. We investigate the ability to resolve multipole acoustic sources using infrasound waveform inversion methods for a variety of scenariosA monopole inversion yields results that may be sufficient for practical purposes, even for multipole sources with a small dipole componentSynthetic inversion studies can increase understanding of the limitations of multipole infrasound inversions based on deployment scenarios We investigate the ability to resolve multipole acoustic sources using infrasound waveform inversion methods for a variety of scenarios A monopole inversion yields results that may be sufficient for practical purposes, even for multipole sources with a small dipole component Synthetic inversion studies can increase understanding of the limitations of multipole infrasound inversions based on deployment scenarios

Acoustic waves in the atmosphere are commonly recorded on seismometers as they couple into the ground. These signals, here called ground coupled airwaves, are not commonly considered in numerical modeling of infrasound propagation, which often assumes a rigid unmeshed boundary. Starting from an analytically‐tractable spherical wave model, we analyze how the coupling of an acoustic wave into a planar elastic halfspace changes over a wide range of scenarios. We use energy admittance to quantify acoustic to seismic coupling over both a planar elastic halfspace and meshed topography. Our spectral element and analytic calculations have different maxima as a function of incidence angle, with very high admittance values for near‐vertical incidence (maximum ≈78%). Energy admittance calculations at shallow incidence angles are much smaller (less than 1%). In simulations over the complex topography of Sakurajima Volcano, we attribute the variable spatial pattern of energy admittance to changes in earth parameters between each model. The observed pressure difference over the simulated 15 km region appears to be <2%$< 2\%$. While this value is relatively small, the cumulative addition over 100s of km and multiple acoustic bounce points may be significant. Acoustic to seismic coupling along the propagation path may bias long distance yield estimates, particularly when infrasound propagates over regions with steep topography or particularly slow seismic velocities, such as alluvial planes. When numerically modeling how low‐frequency sound waves travel over the Earth's surface, it is usually assumed that the Earth does not react to the sound waves. This approximation can make the numerical modeling faster and easier, but it ignores the fact the we often observe that sound waves convert into seismic waves when they encounter the Earth's surface. Here we estimate how much energy is moved from the sound wave into the seismic wave in order to better understand potential inaccuracies in previous modeling efforts. We find that the change is small, but now we have an estimated number. This kind of estimate is important when trying to analyze how a complex source like a volcanic eruption or a chemical explosion releases seismic and infrasound waves, which can in turn help inform future estimates of explosion size and other important characteristics. Fully‐coupled simulations with SPECFEM3D show medium‐dependent acoustic to seismic coupling patterns over realistic, complex topographyCoupling into the earth results in a small but often notable reduction in the peak amplitude of observed infrasound waves (<2%)Acoustic to seismic coupling along the propagation path may be an additional attenuation mechanism for long range infrasound propagation Fully‐coupled simulations with SPECFEM3D show medium‐dependent acoustic to seismic coupling patterns over realistic, complex topography Coupling into the earth results in a small but often notable reduction in the peak amplitude of observed infrasound waves (<2%) Acoustic to seismic coupling along the propagation path may be an additional attenuation mechanism for long range infrasound propagation

This paper reviews current issues of haptic feedback through peripheral nerve stimulation for upper-extremity prostheses. Haptic feedback is essential for daily activities, such as handling objects, cooking, bathing, and dressing. However, this capability is lost when a limb is amputated. Considerable effort has been made to recover haptic sensations in upper-extremity prosthetics through non-invasive mechano-tactile or electro-tactile stimulation of the skin, or invasive electrical stimulation of peripheral nerves. In recent years, there have been new approaches to restoring haptic sensation through peripheral nerve stimulation, which allows both somatotopic and modality-matching haptic feedback and a more natural high-level sensory feedback. This paper introduces state-of-the-art haptic feedback approaches that can evoke comprehensive sensory feedback, such as stick-slip, slippage, texture, proprioception, enhanced embodiment, and high-resolution submodalities, such as touch, pressure, and vibration, primarily through invasive peripheral nerve interfaces.

Estimation of confidence intervals for neural network outputs is important when the uncertainty of a neural network system must be addressed for safety or reliability. This paper presents a new approach for estimating confidence intervals, which can help users validate neural network outputs. The estimation of confidence intervals, called error estimation by series association, is performed by a supplementary neural network trained to predict the error of the main neural network using input features and the output of the main network. The accuracy of this approach is shown using a simple nonlinear mapping and more complicated, realistic nuclear power plant fault diagnosis problems. The results demonstrate that the approach performs confidence estimation successfully.