Featured Articles

  • An Intracardiac Flow Based Electromagnetic Energy Harvesting Mechanism for Cardiac Pacing

    Contemporary cardiac implantable electronic devices are powered by batteries. Replacement due to battery depletion may cause complications and is costly. To overcome these limitations, we present an energy harvesting device with a lever which is deflected by blood flow within the right heart. The kinetic energy of the lever is converted by an electromagnetic conversion principle. It generated a mean power of 14.39 / 82.64 µW at 60 / 200 bpm in an experimental setup mimicking flow conditions in the heart at 1 m/s peak flow. Therefore, it presents a viable alternative to batteries to power cardiac pacemakers.

  • A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training

    It is very common for individuals to experience a loss of arm function after neurological injury. Robotic devices can assist in recovery; however, current devices are typically too large, bulky, and expensive to be routinely used in the clinic or at home. Here, we developed and validated a low-cost portable planar passive rehabilitation robot (PaRRo) that uses a unique mechanical design and miniature eddy current brakes instead of motors to directly resist the user during reaching motions. Theoretical and experimental results show that this device could potentially serve as a valuable clinical tool to restore arm function after neurological injury.

  • Learning Patterns of Pivoting Neuromuscular Control Training-Towards a Learning Model

    Over the last decades, lower limb therapy using locomotion devices has been used; however, how the learning patterns over the course of a long-term training program change are unknown. The purpose of this study was to investigate the learning patterns in leg pivoting neuromuscular control performance over six-week pivoting neuromuscular control training (POINT) among 20 healthy subjects. Overall, learning patterns could be characterized by learning curve models such as the power law and exponential curve. The findings and models can potentially be used to develop more effective subject-specific therapy scheduling and gait therapy protocol.

  • Model of Impedance Changes in Unmyelinated Nerve Fibres

    Electrical impedance tomography produces cross-sectional images of the compound action potential in peripheral nerve; it has potential to provide images of active fascicles in autonomic nerves. It could be used in electroceuticals for selective stimulation of nerve fascicles and so avoid off-target effects.
    For this, the EIT parameters must be optimised. This has been aided by development of accurate FEM models of unmyelinated nerve fibres bi-directionally coupled with the external space. The models agreed with experiments and could serve as a basis for optimising EIT imaging and consequent selective stimulation.

  • An Automated Method for Multi-DOF Cell Rotational Control Contributing to Orientation-based Cell Surgery Applications

    An autonomous control framework for cell out-of-plane reorientation has been proposed by utilizing a robot-tweezers cell manipulation system. A serious of experiments that include cell in-plane, out-of-plane rotational control and cellular orientation-based enucleation manipulation have been performed, demonstrating the necessity and significance of cell orientation control involved in many cell surgery studies. The outcome of this study represents an advancement in single cell manipulation, and contributes to many cellular orientation-based cell surgery applications, such as cell nucleus biopsy and transplantation, cloning technology, and PGD.

  • A Method for Rapid, Reliable, and Low-Volume Measurement of Lithium in Blood for Use in Bipolar Disorder Treatment Management

    For over 60 years, lithium has proved most effective in the treatment of Bipolar Disorder. Blood lithium levels must be closely monitored to prevent toxicity, but compliance with the regular blood test requirement falls below standard and is associated with high rates of treatment non-adherence with consequent relapse of illness
    The study proposes a novel opto-impedance technique, that combines high specificity and sensitivity attributes of optical and electrical impedance methods into a personal lithium analyser for use by patients. Results demonstrate the accuracy of the proposed technique and its ability to offer a solution for rapid assessment of lithium.

  • Video Magnification Applied in Ultrasound

    Video magnification is a computational method increasing the subtle temporal motion in videos. This visualization method is proposed here for medical ultrasound images, as an alternative to standard vectors and color map illustrations. Small changes are amplified to be easily seen with a naked eye to render in a natural way subtle and fast displacements that are happening in tissue without obscuring any anatomical information. Video magnification could be a new tool for physicians to highlight new pathology indicators or for long-term disease monitoring; it could also be used for rapid qualitative inspection or educational purposes.

  • Wireless thermometry for real-time temperature recording on thousand-cell level

    It is vitally necessary to measure the cellular temperature to fully understand life sciences. A method allowing to measure the cellular temperature for a normal growing state without doing damage to the cells and disturbing their intercellular communication is needed. Here, a wireless, real-time, high-throughput temperature detection method is developed. The acquisition system applies a high-precision reference resistor and a low real-time measurement current (below or equal to 0.14 mA) to reduce self-heating via the intermittent measurement. Cells of a small volume cell medium are cultured on the surface of the platinum thermal resistor and subsequently measured in the incubator.

  • Medical Image Synthesis with Deep Convolutional Adversarial Networks

    Medical Image Synthesis with Deep Convolutional Adversarial Networks

    We propose a generative adversarial approach to address the medical image synthesis problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To produce more realistic image, we propose to use adversarial learning strategy to better model the FCN. Moreover, an image-gradient-difference based loss function is proposed to avoid generating blurry images. Also, a long-term residual unit is explored to help the training of the network. We further apply auto-context model to implement a context-aware framework. Experimental results show the robustness and accuracy of our method in synthesizing various medical images.

  • RF Channel Modeling for Implant to Implant Communication and Implant to Sub-Cutaneous Implant Communication for Future Leadless Cardiac Pacemakers

    RF Channel Modeling for Implant-to-Implant Communication and Implant to Sub-Cutaneous Implant Communication for Future Leadless Cardiac Pacemakers

    The study proposes the radio-frequency channel modeling for in-body to in-body implant communication for next generation multinodal capsule-like leadless cardiac pacemaker technology. The method is based on detailed numerical simulations of complex digital human models. The research also investigates the placement of a subcutaneous implant transceiver and channel behaviour based on ventricular blood volume change to find out the appropriate timing of the signal transmission between the implants. The overall study would facilitate the design of the world’s first complete prototype of the multi-node leadless capsule pacemaker technology in the future.

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