Estimation of Carrying Angle Based on CT Images in Preoperative Surgical Planning for Cubitus Deformities

Conventionally, the carrying angle of the elbow is measured using simple two-dimensional radiography or goniometry, which has questionable reliability. This study proposes a novel method for estimating carrying angles using computed tomography that can enhance the reliability of the angle measurement. Data of CT scans from 25 elbow joints were processed to build segmented three-dimensional models. The cross-sectional centerlines of the ulna and the humerus were traced from the 3D models, and the angle between 2 vectors formed from the centerlines of the humerus and the ulna was defined as the &#34;three-dimensional carrying angle.&#34; These angles were compared with those measured by simple radiograph. Two cases of angular deformity were underwent surgery based on this preoperative surgical planning, and the postoperative 3D carrying angles were evaluated using the proposed method. The mean value of the calculated three-dimensional carrying angle was 20.7 degrees +/-3.61, while it was 16.3 degrees +/-3.21 based on simple radiography without statistical difference. Based on the 3D carrying angle estimations, 2 surgical cases of cubitus deformities were planned by comparison with the normal contra-lateral elbow. Postoperative angle estimations confirmed that the corrected angles were nearly identical to the planned angles for both cases. The results of this study showed that the carrying angle can be accurately estimated using three-dimensional CT and that the proposed method is useful in evaluating deformities of the elbow with high reliability.</p

Estimation of the spatial profile of neuromodulation and the temporal latency in motor responses induced by focused ultrasound brain stimulation

This study investigates the spatial profile and the temporal latency of the brain stimulation induced by the transcranial application of pulsed focused ultrasound (FUS). The site of neuromodulation was detected using 2-deoxy-2-[18F]fluoro-d-glucose PET immediately after FUS sonication on the unilateral thalamic area of Sprague–Dawley rats. The latency of the stimulation was estimated by measuring the time taken from the onset of the stimulation of the appropriate brain motor area to the corresponding tail motor response. The brain area showing elevated glucose uptake from the PET image was much smaller (56±10% in diameter, 24±6% in length) than the size of the acoustic focus, which is conventionally defined by the full-width at half-maximum of the acoustic intensity field. The spatial dimension of the FUS-mediated neuromodulatory area was more localized, approximated to be full-width at 90%-maximum of the acoustic intensity field. In addition, the time delay of motor responses elicited by the FUS sonication was 171±63 (SD) ms from the onset of sonication. When compared with latencies of other nonultrasonic neurostimulation techniques, the longer time delay associated with FUS-mediated motor responses is suggestive of the nonelectrical modes of neuromodulation, making it a distinctive brain stimulation method

Development of Gait Rehabilitation System Capable of Assisting Pelvic Movement of Normal Walking

Gait rehabilitation training with robotic exoskeleton is drawing attention as a method for more advanced gait rehabilitation training. However, most of the rehabilitation robots are mainly focused on locomotion training in the sagittal plane. This study introduces a novel gait rehabilitation system with actuated pelvic motion to generate natural gait motion. The rehabilitation robot developed in this study, COWALK, is a lower-body exoskeleton system with 15 degrees of freedom (DoFs). The COWALK can generate multi-DoF pelvic movement along with leg movements. To produce natural gait patterns, the actuation of pelvic movement is essential. In the COWALK, the pelvic movement mechanism is designed to help hemiplegic patients regain gait balance during gait training. To verify the effectiveness of the developed system, the gait patterns with and without pelvic movement were compared to the normal gait on a treadmill. The experimental results show that the active control of pelvic movement combined with the active control of leg movement can make the gait pattern much more natural

Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer's intention to stimulate a rat's brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer's intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0 +/- 3.0% accuracy, with a time delay of 1.59 +/- 1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.open12

Experimental and Computational Characterization of Biological Liquid Crystals: A Review of Single-Molecule Bioassays

Quantitative understanding of the mechanical behavior of biological liquid crystals such as proteins is essential for gaining insight into their biological functions, since some proteins perform notable mechanical functions. Recently, single-molecule experiments have allowed not only the quantitative characterization of the mechanical behavior of proteins such as protein unfolding mechanics, but also the exploration of the free energy landscape for protein folding. In this work, we have reviewed the current state-of-art in single-molecule bioassays that enable quantitative studies on protein unfolding mechanics and/or various molecular interactions. Specifically, single-molecule pulling experiments based on atomic force microscopy (AFM) have been overviewed. In addition, the computational simulations on single-molecule pulling experiments have been reviewed. We have also reviewed the AFM cantilever-based bioassay that provides insight into various molecular interactions. Our review highlights the AFM-based single-molecule bioassay for quantitative characterization of biological liquid crystals such as proteins

Driver-vehicle interaction in braking

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1999.Includes bibliographical references (leaves 81-86).While antilock brake systems (ABS) have become more and more popular with the public, statistics reports imply that ABS-equipped cars have no advantage over non-ABS-equipped cars in reducing fatal accidents. While the brake pedal needs to be pushed down (full-brake) to activate the ABS, many drivers on ABS-equipped cars fail to do this simple maneuver, reducing the effectiveness of ABS and even contributing to some accidents. Because of such behavior on the driver's part, the major feature of this brake assistance system is often ineffective. The goal of this thesis is to design brake systems that provide intuitive brake control and proper braking performance information for the driver. As a preliminary study in brake system design, the characteristics of human leg motion and its underlying motor control scheme were studied through experiments and simulations. Automotive brake systems were modeled as a type of master-slave tele-manipulator. Human force-displacement interaction at the master end (the brake pedal) has a strong effect on the system's ability to control the operations at the slave end (the braking performance). By providing drivers with "force feel" at the brake pedal, they can obtain information about braking conditions or performance. This thesis developed novel brake systems based on two new aspects. First, the mechanical impedance characteristics of the leg action of the driver were taken into consideration in designing the brake systems. Second, the brake systems provide the driver with kinesthetic feedback of braking conditions or performance. Since the effectiveness of brake systems needs to be examined as a combined driver-vehicle system, driving simulations were used to investigate the performance of the proposed designs.by Shinsuk Park.Ph.D

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics