Search

Humans perform grasping by breaking down the task into a series of action phases, where the transitions between the action phases are based on the comparison between the predicted tactile events and the actual tactile events. The dependency on tactile sensation in grasping allows humans to grasp objects without the need to locate the object precisely, which is a feature desirable in robot grasping to successfully grasp objects when there are uncertainties in localizing the target object. In this paper, we propose a method of implementing a tactile event based grasping algorithm using memorized predicted tactile events as state transition triggers, inspired by the human grasping. First, a simulated robotic manipulator mounted with pressure and vibration sensors on each finger, analogous to the different mechanoreceptors in humans, performed ideal grasping tasks, from which the tactile signals between consecutive states were extracted. The extracted tactile signals were processed and stored as predicted tactile events. Secondly, a grasping algorithm composed of eight discrete states, Reach, Re-Reach, Load, Lift, Hold, Avoid, Place, and Unload was built. The transition between consecutive states is triggered when the actual tactile events match the predicted tactile events, otherwise, triggering the corrective actions. Our algorithm was implemented on an actual robot, equipped with capacitive and piezoelectric transducers on the fingertips. Lastly, grasping experiments were conducted, where the target objects were deliberately misplaced from their expected positions, to investigate the robustness of the tactile event based grasping algorithm to object localization errors.

Despite the advantages of electromyography (EMG), which can grasp intent before actual movement, there have not been many studies on the use of EMG in exoskeletons. In this paper, we conducted an experiment to analyze the characteristics of electromyography when EMG signals are used in exoskeleton robots. We integrated the advantages of EMG sensors and physical sensors to control exoskeleton robots using EMG signals and physical signals. The walking environment was defined using the surface electromyography (sEMG) signal and the physical signal to an accuracy of 88% or more. To compensate for the limitations of physical sensors, we used sEMG to distinguish changes in the load during walking. Moreover, sEMG signals and physical signals were used to distinguish external collisions and to identify other variables that could be distinguished. Finally, to examine the characteristics of muscular fatigue, which is a disadvantage when using electromyography, we conducted a muscle fatigue experiment in the lower limb and summarized how the EMG characteristics change in relation to the degree of force and the muscle fatigue.

We investigated long-term outcomes in patients with pulmonary and extrapulmonary ARDS including all items of PFT, gas exchange, quality of life, and HRCT, which had not been dealt comprehensively in previous studies. We found that, although most abnormalities on PFT were mild, 62.9% and 51.8% of the patients showed abnormality in PFT and exercise PFT, respectively after more than six months of ARDS onset. Multivariate analysis demonstrated that only significant factor to affect the extent of lesions on HRCT and TLC was the subtype of ARDS. These results suggest that patients with ARDSp would remain more severe lung sequelae after recovery than the ARDSexp patients.

Smoking may induce changes in T lymphocyte cell subsets in peripheral blood. Abnormality of T lymphocyte cell subsets in peripheral blood and in bronchoalveolar-lavage fluid, and increased CD8+ T lymphocytes in the airways have been reported in patients with COPD. These findings suggest that T lymphocyte abnormalities might play a role in the pathogenesis of airflow limitation in smokers. To investigate this hypothesis this study was performed. Peripheral blood T lymphocyte cell subsets were measured by flow cytometry using specific monoclonal antibodies in 20 healthy nonsmokers, 20 healthy smokers, and 20 smokers with stable COPD. No significant differences in the peripheral blood T lymphocyte cell subsets were observed between the three groups. Because a previous study had shown peripheral blood T lymphocyte abnormalities in the subgroup of nonsmoking COPD subjects, we wanted to investigate what factors determine the subgroup of COPD with abnormal T lymphocyte cell subsets. We tried to measure the relationship between T lymphocyte cell subsets and physiologic indices of pulmonary function tests in smokers with COPD. The proportion of CD8+ T lymphocytes significantly correlated with DLco (p= 0.012) and DLco/VA (p=0.002), and CD4+/CD8+ ratio correlated with DLco/VA (p=0.01). Therefore, we attempted to divide the smokers with COPD into two subgroups on the basis of DLco/VA. Ten patients with low DLco/VA (DLco/VA