Individuals with physical limb disabilities are often restricted to perform activities of daily life (ADLs). While efficacy of bilateral training has been demonstrated in improving physical coordination of human limbs, few robots have been developed in simulating people’s ADLs integrated with task-specific force field control. This study sought to develop a bilateral robot for better task rendering of general ADLs (gADLs), where gADL-consistent workspace is achieved by setting linear motors in series, and haptic rendering of multiple bimanual tasks (coupled, uncoupled and semi-coupled) is enabled by regulating force fields between robotic handles. Experiments were conducted with human users, and our results present a viable method of a single robotic system in simulating multiple physically bimanual tasks. In future, the proposed robotic system is expected to be serving as a coordination training device, and its clinical efficacy will be also investigated.Transcutaneous electrical stimulation is a promising technique for providing prosthetic hand users with information about sensory events. However, questions remain over how to design the stimulation paradigms to provide users the best opportunity to discriminate these events. Here, we investigate if the refractory period influences how the amplitude of the applied stimulus is perceived. Twenty participants completed a two-alternative forced choice experiment. We delivered two stimuli spaced between 250 ms to 450 ms apart (inter-stimulus-interval, isi). The participants reported which stimulus they perceived as strongest. Each stimulus consisted of either a single or paired pulse delivered transcutaneously. The inter-pulse interval (ipi) for the paired pulse stimuli varied between 6 and 10 ms. We found paired pulses with an ipi of 6 ms were perceived stronger than a single pulse less often than paired pulses with an ipi of 8 ms (p = 0.001) or 10 ms (p less then 0.0001). Additionally, we found when the isi was 250 ms, participants were less likely to identify the paired pulse as strongest, than when the isi was 350 or 450 ms. This study emphasizes the importance of basing stimulation paradigms on the underlying neural physiology. The results indicate there is an upper limit to the commonly accepted notion that higher stimulation frequencies lead to stronger perception. If frequency is to be used to encode sensory events, then the results suggest stimulus paradigms should be designed using frequencies below 125 Hz.Acupuncture can regulate the cognition of brain system, and different manipulations are the keys of realizing the curative effect of acupuncture on human body. Therefore, it is crucial to distinguish and monitor the different acupuncture manipulations automatically. In this brief, in order to enhance the robustness of electroencephalogram (EEG) detection against noise and interference, we propose an acupuncture manipulation detecting framework based on supervised ISOMAP and recurrent neural network (RNN). Primarily, the low-dimensional embedding neural manifold of brain dynamical functional network is extracted via the reconstructed geodetic distance. It is found that there exhibits stronger acupuncture-specific reconfiguration of brain network. Besides, we show that the distance travel along this manifold correlates strongly with changes of acupuncture manipulations. The low-dimensional brain topological structure of all subjects shows crescent-like feature when acupuncturing at Zusanli acupoints, and fixed-points are varying under diverse manipulation methods. Moreover, Takagi-Sugeno-Kang (TSK) classifier is adopted to identify acupuncture manipulations according to the nonlinear characteristics of neural manifolds. Compared with different classifier, TSK can further improve the accuracy of manipulation identification at 96.71%. The results demonstrate the effectiveness of our model in detecting the acupuncture manipulations, which may provide neural biomarkers for acupuncture physicians.The acoustic scattering by highly inhomogeneous objects is analyzed by a method-of-moment solver for the volume integral equation. To enable the treatment of acoustically large scatterers of various topologies, the iterative numerical solution of the resulting system is accelerated via a kernel independent algebraic compression scheme blocks of the hierarchically partitioned moment stiffness matrix are expressed in butterfly form that, for volume problems, scales favorably compared to the popular low-rank approximation. A detailed description of the algorithm, as implemented in this work, is provided. Validations of the numerical formulation, parameter tuning, and performance study of the fast method for acoustically large objects are presented, in various settings and for a range of examples, representative of biomedical and oceanographic applications.In designing ultra-efficient noise immune nanoscale circuits and systems, Schmitt triggers (STs) are vital components influencing total functionality. This article proposes an ultracompact ST using ferroelectric carbon nanotube field-effect transistors (Fe-CNTFETs) and a robust ST latch. By using the unique electrical futures of the Fe-CNTFETs, the proposed ST has been designed in a particular way to only employ two transistors similar to a conventional binary inverter. Moreover, by utilizing the negative capacitance feature of the Fe-CNTFETs, the proposed ST can perform the backup and restore operation during a scheduled power gating or a sudden power outage without imposing any additional transistors, interconnects, and control signals. The proposed ST latch is hardened to soft errors occurring due to unwanted single-event upsets (SEUs). Our extensive simulations demonstrate that our proposed ST latch offers lower transistors counts (on average 34%) and more energy savings (on average 79%). On the other hand, our design has shown 5.6 times on average higher critical charge tolerance than the previous counterparts due to its soft error hardening circuity and the superior hysteresis behavior of the proposed Fe-CNTFET-based ST. Moreover, the auto-nonvolatility of the proposed ST makes the proposed latches immune to the sudden power outage, which was not devised in the previous ST latches. Our results show new pathways in designing ultracompact and efficient nonvolatile ST latches using the Fe-CNTFET technology.The change of microvasculature is associated with the occurrence and development of many diseases. Ultrafast power Doppler imaging (uPDI) is an emerging technology for the visualization of microvessels due to the development of ultrafast plane wave (PW) imaging and advanced clutter filters. However, the low signal-to-noise ratio (SNR) caused by unfocused transmit of PW imaging deteriorates the subsequent imaging of microvasculature. Nonlocal means (NLM) filtering has been demonstrated to be effective in the denoising of both natural and medical images, including ultrasound power Doppler images. However, the feasibility and performance of applying an NLM filter on the ultrasound radio frequency (RF) data have not been investigated so far. In this study, we propose to apply an NLM filter on the spatiotemporal domain of clutter filtered blood flow RF data (St-NLM) to improve the quality of uPDI. Experiments were conducted to compare the proposed method with three different methods (under various similarity windo6, 201, and [Formula see text] for St-NLM, Non-NLM, and S-NLM, respectively. The proposed St-NLM method can enhance the microvascular visualization in uPDI and has the potential for the diagnosis of many microvessel-change-related diseases.Due to its sensitivity to geometrical and mechanical properties of waveguides, ultrasonic guided waves (UGWs) propagating in cortical bones play an important role in the early diagnosis of osteoporosis. However, as impacts of overlaid soft tissues are complex, it remains challenging to retrieve bone properties accurately. Meta-learning, i.e., learning to learn, is capable of extracting transferable features from a few data and, thus, suitable to capture potential characteristics, leading to accurate bone assessment. In this study, we investigate the feasibility to apply the multichannel identification neural network (MCINN) to estimate the thickness and bulk velocities of coated cortical bone. It minimizes the effects of soft tissue by extracting specific features of UGW, which shares the same cortical properties, while the overlaid soft tissue varies. Distinguished from most reported methods, this work moves from the hand-design inversion scheme to data-driven assessment by automatically mapping features of UGW to the space of bone properties. The MCINN was trained and validated using simulated datasets produced by the finite-difference time-domain (FDTD) method and then applied to experimental data obtained from cortical bovine bone plates overlaid with soft tissue mimics. A good match was found between experimental trajectories and theoretical dispersion curves. The results demonstrated that the proposed method was feasible to assess the thickness of coated cortical bone plates.

Statistical shape models have been successfully used in numerous biomedical image analysis applications where prior shape information is helpful such as organ segmentation or data augmentation when training deep learning models. However, training such models requires large data sets, which are often not available and, hence, shape models frequently fail to represent local details of unseen shapes. This work introduces a kernel-based method to alleviate this problem via so-called model localization. It is specifically designed to be used in large-scale shape modeling scenarios like deep learning data augmentation and fits seamlessly into the classical shape modeling framework.

Relying on recent advances in multi-level shape model localization via distance-based covariance matrix manipulations and Grassmannian-based level fusion, this work proposes a novel and computationally efficient kernel-based localization technique. Moreover, a novel way to improve the specificity of such models via normalizing flow-based density estimation is presented.

The method is evaluated on the publicly available JSRT/SCR chest X-ray and IXI brain data sets. The results confirm the effectiveness of the kernelized formulation and also highlight the models’ improved specificity when utilizing the proposed density estimation method.

This work shows that flexible and specific shape models from few training samples can be generated in a computationally efficient way by combining ideas from kernel theory and normalizing flows.

The proposed method together with its publicly available implementation allows to build shape models from few training samples directly usable for applications like data augmentation.

The proposed method together with its publicly available implementation allows to build shape models from few training samples directly usable for applications like data augmentation.

Microwave imaging has been investigated for medical applications such as stroke and breast imaging. Current systems typically rely on bench-top equipment to scan at a variety of antenna positions. For dynamic imaging of moving structures, such as the cardiovascular system, much higher imaging speeds are required than what has thus far been reported. Recent innovations in radar-on-chip technology allow for simultaneous high speed data collection at multiple antenna positions at a fraction of the cost of conventional microwave equipment, in a small and potentially portable system. The objective of the current work is to provide proof of concept of dynamic microwave imaging in the body, using radar-on-chip technology.

Arrays of body-coupled antennas were used with nine simultaneously operated coherent ultra-wideband radar chips. Data were collected from the chest and thigh of a volunteer, with the objective of imaging the femoral artery and beating heart. In addition, data were collected from a phantom to validate system performance.