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Realizing an environment that could automatically modify temperature and humidity according to human thermal comfort using an air conditioning system, it is necessary to measure the quantified changes of human thermal comfort continually. While quantifying thermal comfort, human biometric data and environmental data from various environments are needed. Therefore, we proposed a method to estimate human thermal comfort through regression analysis with human biometric data acquired by wearable sensors. Since the PMV model is generally used to evaluate human thermal comfort, the PMV value as the correct thermal comfort could be calculated from the PMV formula. We constructed an experiment environment for acquiring the subject's biometric data and gained the subjects' METs and clo value for calculating the PMV value. The biometric data were analyzed in three regression models to predict PMV value, and MAE and RMSE evaluated each regression model. The fewest number of wearable sensors could be confirmed by gradually reducing the feature values of input data of the regression model. As a result, we acknowledged that the human thermal comfort in an indoor environment could be estimated by heart rate data and left-arm temperature data while applying the proposed method to a daily environment.

Following are two mainstream approaches of video retrieval from large-scale video data using query sentences: (1) an approach to find pre-trained concepts such as objects, persons, scenes, and activities corresponding to a query sentence, and (2) an approach to map a query sentence and images/videos into the same feature space and directly search for images/videos that match the query sentence. In this study, we analyze the advantages and disadvantages of these two approaches using a large-scale video database of TRECVID benchmark and confirm whether the fusion of these approaches can improve video retrieval performance.

Total arch debranching and thoracic endovascular repair of an aortic arch aneurysm with a left aberrant vertebral artery are rare procedures. A small artery is difficult to reconstruct and anastomose in a narrow thoracic space with a large aneurysm. We describe an 85-year-old man with a fusiform aortic arch aneurysm and left aberrant vertebral artery who underwent a hybrid procedure with reconstruction of the left aberrant vertebral artery at a surgical site in the neck. Postoperative computed tomography images confirmed exclusion of the aneurysm and patency of all arch vessels, including the left vertebral artery.

Objective: The objective of this study was to determine the effects of a continuous infusion of low-dose hANP on the lungs during cardiac surgery in patients under cardiopulmonary bypass (CPB). Methods: We analyzed 30 consecutive cases of cardiac surgery performed at our hospital from 2007–2008. The patients were divided into a group that received hANP (hANP group) or a group that received saline and no hANP (N-hANP group). We measured various parameters before and after surgery using a PiCCO monitor. Result: There were no differences in the preoperative characteristics between the groups, although urine volume during the operation was significantly greater in the hANP group. After surgery, there were no significant differences between the groups in cardiac output index (CI), global enddiastolic volume index (GEDVI), intrathoracic blood volume index (ITBI), pulmonary blood volume index (PBI), extravascular lung water index (ELWI) and pulmonary vascular permeability index (PVPI), total protein, and creatine. In contrast, interleukin-6 (IL-6) and renin were significantly lower, and albumin was significantly higher in the hANP group. Conclusion: We found that low-dose hANP during open cardiac surgery inhibited the secretion and plasma activity of IL-6 and renin. Although there were no differences in lung circulatory parameters such as the amount of fluid in the pulmonary blood vessels between the two groups, we believe that the strong diuretic effect of hANP reduced third-space fluid retention caused by CPB.

In this paper, we propose “double admittance control” for a finger & manipulator system, which can realize different admittances of both the finger and the manipulator. The proposed method can realize very natural motion to constrained tasks and can compensate a finger-manipulability under the admittance control. Although our previous method can compensate the finger-manipulability under finger admittance control during the constrained task by using compensate manipulator motion depending on an optimum theory, these results can realize only artificially-made motions. On the other hand, as the new proposed method uses natural dynamic effects, its system is very simple and its motion is very natural. Consequently, this new proposed system has a possibility to extend the manipulator technology to biomechanical control field.

Introduction: Zotepine and chlorprothixene are antipsychotic drugs, and they have a broad therapeutic effect on patients with schizophrenia. Zotepine and chlorprothixene have been reported to decre...

We propose a Tensor Decomposition based algorithm that recognizes the observed action performed by an unknown person and unknown viewpoint not included in the database. Our previous research aimed motion recognition from one single viewpoint. In this paper, we extend our approach for human motion recognition from an arbitrary viewpoint. To achieve this issue, we set tensor database which are multi-dimensional vectors with dimensions corresponding to human models, viewpoint angles, and action classes. The value of a tensor for a given combination of human silhouette model, viewpoint angle, and action class is the series of mesh feature vectors calculated each frame sequence. To recognize human motion, the actions of one of the persons in the tensor are replaced by the synthesized actions. Then, the core tensor for the replaced tensor is computed. This process is repeated for each combination of action, person, and viewpoint. For each iteration, the difference between the replaced and original core tensors is computed. The assumption that gives the minimal difference is the action recognition result. The recognition results show the validity of our proposed method, the method is experimentally compared with Nearest Neighbor rule. Our proposed method is very stable as each action was recognized with over 75% accuracy.

This paper proposes a method that identifies and tracks a walking human across discontinuous fields of views of multiple cameras for the purpose of video surveillance. A typical video surveillance system has multiple cameras, but there are several spaces within the surveillance area that are not within any of the camera's field of view. Also, there are discontinuities between the fields of views of adjacent cameras. In such a system, humans need to be tracked across discontinuous fields of views of multiple cameras. Our proposed model addresses this issue using the concepts of gait pattern, gait model, and motion signature. Each human's gait pattern is constructed and stored in a database. This gait pattern spans a tensor space that consists of three dimensions: person, image feature, and spatio-temporal data. A human's gait model can be constructed from the gait pattern using the "tensor decomposition based approach" described in this paper. When human(s) appears in one of the camera's field of a view (which is often discontinuous from the other camera's field of views), the human's motion signature is calculated and compared to each person in the database's gait model. The person with the gait model that is most similar to the motion signature is identified as same person. After the person is identified, the person is tracked within the field of view of the camera using the mean-shift algorithm based on color parameters. We conducted two experiments; the first experiment was identifying and tracking humans in a single video sequence, and experiments, the percentage of subjects that were correctly identified and tracked was better than that of two currently widely-used methods, PCA and nearest-neighbor. In the second experiment was the same as the first experiment but consisted of multiple-cameras with discontinuous views. The second experiment (human tracking across discontinuous images), shows the potential validity of the proposed method in a typical surveillance system.

Motion control algorithms were proposed for a 9-DOF finger-arm robot by using the finger manipulability obtained in a previous study. However, in the previous study, only methods for achieving unconstrained movement of the finger-arm robot were discussed. In this paper, the authors propose a novel method for having a finger-arm robot complete a constrained task by integrating impedance control with manipulability control of the finger. First, methods of applying the previous heuristic method to the passive impedance control and the active impedance control were developed; and experiments were performed to demonstrate the features. Then, an impedance control combined with the steepest ascent method to modulate the manipulability of the finger was proposed. The proposed method demonstrates a strong performance even when a dynamic external force is applied to the finger. By using the proposed method, the arm actively moves along a direction to effectively maintain the moving potential of the finger during both the unconstrained and the constrained tasks.

This paper proposes a Tensor Decomposition Based method that can recognize an unknown person's action from a video sequence, where the unknown person is not included in the database (tensor) used for the recognition. The tensor consists of persons, actions and time-series image features. For the observed unknown person's action, one of the actions stored in the tensor is assumed. Using the motion signature obtained from the assumption, the unknown person's actions are synthesized. The actions of one of the persons in the tensor are replaced by the synthesized actions. Then, the core tensor for the replaced tensor is computed. This process is repeated for the actions and persons. For each iteration, the difference between the replaced and original core tensors is computed. The assumption that gives the minimal difference is the action recognition result. For the time-series image features to be stored in the tensor and to be extracted from the observed video sequence, the human body silhouette's contour shape based feature is used. To show the validity of our proposed method, our proposed method is experimentally compared with Nearest Neighbor rule and Principal Component analysis based method. Experiments using 33 persons' seven kinds of action show that our proposed method achieves better recognition accuracies for the seven actions than the other methods.