Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data

Object manipulation actions represent an important share of the Activities of Daily Living (ADLs). In this work, we study how to enable service robots to use human multi-modal data to understand object manipulation actions, and how they can recognize such actions when humans perform them during human-robot collaboration tasks. The multi-modal data in this study consists of videos, hand motion data, applied forces as represented by the pressure patterns on the hand, and measurements of the bending of the fingers, collected as human subjects performed manipulation actions. We investigate two different approaches. In the first one, we show that multi-modal signal (motion, finger bending and hand pressure) generated by the action can be decomposed into a set of primitives that can be seen as its building blocks. These primitives are used to define 24 multi-modal primitive features. The primitive features can in turn be used as an abstract representation of the multi-modal signal and employed for action recognition. In the latter approach, the visual features are extracted from the data using a pre-trained image classification deep convolutional neural network. The visual features are subsequently used to train the classifier. We also investigate whether adding data from other modalities produces a statistically significant improvement in the classifier performance. We show that both approaches produce a comparable performance. This implies that image-based methods can successfully recognize human actions during human-robot collaboration. On the other hand, in order to provide training data for the robot so it can learn how to perform object manipulation actions, multi-modal data provides a better alternative

Failures of Retaining Wall Structures Due to Earthquake

This paper describes crack pattern of reinforcement concrete retaining structure due to earthquake. For this reason, the 3-D finite element dynamic analysis of retaining wall structures with consideration of the soil-structure interaction has been used. Purpose of this study is to detect damage zone, due to earthquake in such structures. The analysis data is based on 1995 Kobe and 1994 Northridge earthquake reports, and the results have been verified with some retaining walls were damaged in those earthquakes. To take into account the non-linearity of soil-structure surface, surface to surface contact element is used. One of the most important problems in dynamic analysis is modeling of infinite media. If hinge or sliding support for soil boundary is used , it would not define an acceptable boundary condition, because the transmitted earthquake waves reflect from the boundary and no energy would transmit out. For simulation of the unbounded nature of the soil medium, viscous (dashpot) boundary has been applied. Damping coefficient is given by Lysmer and Kuhlemeyer, and Drucker Prager soil plasticity model is considered for non-linearity of soil. Distributions of the amplitude of stress in the wall, crack pattern in concrete wall are discussed in detail and finally suggested flexural failure diagram for determining damage zone and weak point of cantilever retaining wall

Global variations in diabetes mellitus based on fasting glucose and haemogloblin A1c

Fasting plasma glucose (FPG) and haemoglobin A1c (HbA1c) are both used to diagnose diabetes, but may identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening had elevated FPG, HbA1c, or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardised proportion of diabetes that was previously undiagnosed, and detected in survey screening, ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the agestandardised proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global gap in diabetes diagnosis and surveillance.peer-reviewe

A Framework for Using Object Manipulation Actions in Multimodal Human-Robot Interaction

There is a growing need for service robots that can support independent living of the elderly and people with disabilities, as well as robots that can assist human workers in a warehouse or on a fac- tory floor. However, robots that collaborate with humans should act predictably and ensure that the interaction is safe and effective. Therefore, when humans and robots collaborate for example during Activities of Daily Living (ADLs), robots should be able to recognize human actions and intentions and produce appropriate responses. To do so, it is crucial to understand how two humans interact during a collaborative task and how they perform them. Humans employ multiple communication modalities when engaging in collaborative activities; similarly, service robots require information from multiple sensors to plan their actions based on the interaction and the task states. In particular, it is necessary to collect and analyze data from different sensor modalities when humans engage in the interaction which involves physical manipulation of the environment. Towards this goal, our research focused on three research problems. 1- Human Grasp Analysis: Grasp is an important phase of object manipulation actions and service robots can be trained how to execute a grasp using programming by demonstration (PbD). To control a grasp, one should consider the position of the fingers on the grasped object including the general configuration of the fingers along with their applied forces, similarly for learning by demonstration, these variables provide good candidate features. However considering these multimodal features, the question arises whether existing grasp types presented in literature are appropriate. We endeavor to address this question empirically by studying the human grasp data using the pressure and bend sensors measurements from our developed data glove. We use unsupervised learning methods to extract the human grasp patterns from our collected data. The findings of our work implies that the grasp types that can be perceived from the measured data are a subset of the grasp types presented in taxonomies by literature. These results have direct implications on how PbD should be used for robots that assist with ADLs. 2- Human Manipulation Actions: Object manipulation actions represent an important share of ADLs. We therefore investigated how to enable service robots to use human multimodal data to learn how to perform these actions, and how to recognize them during human-robot collaboration. In mul- timodal human-robot interaction, when the humans and robots physically interact, the physical contact itself, be it between the human and the robot, or with the objects in the environment, becomes an impor- tant channel for communication. In this thesis, we focus on the latter, understanding how manipulation actions enhance the understanding between the human and the robot. Of special interest are so called haptic-ostensive (H-O) actions, manipulation actions that help the communication. In particular, we identify motion primitives from which H-O actions are composed. In this way, H-O actions can be recognized and classified. Our multimodal data consists of videos, and measurements from a data glove (including hand mo- tion, finger joint angle and applied force measured by bend and pressure sensors) collected from human participants implementing object manipulation actions. We explore two different methods, the first of which is primitive-based method while the second is purely data-driven, called visual-data-flow. In the former, we show that the multimodal signal generated by a manipulation action can be decomposed into a set of primitives namely building blocks. The primitives are modeled using physical insights obtained from experimental data. In the latter, the raw measurements are used along with the features extracted from videos using deep convolutional neural networks (DCNN). Both methods present acceptable per- formance which suggests that vision-based method can be adequate for H-O action recognition during multimodal human-robot interaction. In addition, it motivates us to utilize only image for performing human H-O action recognition in real-time. 3- A Manager for Human-Robot Multimodal Interaction: Service robots for the elderly require information from multiple modalities to maintain active interaction with a human during performing interactive tasks. We study in detail the scenario where a human and a service robot collaborate to find an object (Find Task) in the kitchen so it can be used in a subsequent task such as cooking. Based on the data collected during human studies, we develop an Interaction Manager which allows the robot to actively participate in the interaction and plan its next action given human spoken utterances, observed manipulation actions, and gestures. We develop multiple modules for a robot in the Robot Operating System (ROS), including H-O action recognition using vision, gesture recognition using vision, speech recognition using the Google speech recognition API, a dialogue tool which includes a multimodal dialogue act (DA) classifier that determines the intention of the speaker, and the Interaction Manager itself. The proposed system is validated using two different robot platforms: a Baxter robot and a Nao robot. The preliminary user study provides the evidence that by using the developed multimodal Interaction Manager, the robot can successfully interact with the human in the Find Task

Assessment of request, distribution and using of fresh frozen plasma in Hospital’s Emergency Department in Rasool Akram Hospital

Background: Uncontrolled prescribing of blood products can cause reduce blood bank reserves and inappropriate distribution of blood products. Objective: The goal of this study was to determine of fresh frozen plasma (FFP) transfusion indicators in emergency department. Methods: A cross-sectional study was done in patients admitted to the emergency department of Rasoul Akram, Tehran, Iran for one year (September 2016 to 2017). Findings: Seventy patients (47M/23F) were assessed with the mean age 57.96±18.22 years. Totally 294 units of fresh frozen plasma (FFP) were requested for patients and 93 units (31.64%) were used. The most indication for FFP unit request was acute and chronic anemia. Conclusion: Transfusion rate and index were within acceptable limits, while the crossmatch to transfusion ratio (C/T) was undesirable. Keywords: Products blood, Fresh frozen plasma, Transfusion ratio, Transfusion inde

Assessment of request, distribution and using of platelet concentrates in hospital’s emergency department of Rasool Akram in Tehran

Background: Indiscriminate or no indication prescribed blood products can increase health care costs, transmission of infection, infusion-related complications and improper disposal of blood products and inappropriate distribution of blood products can reduce blood bank reserves. Objective: This study was performed to determine the index of platelet concentrate transfusion in an emergency department. Methods: A cross-sectional study was done on 28 patients admitted in the emergency department, Rasool Akram hospital, Tehran for one year (October, 2014-2015). Infusion index include crossmatch / transfusion ratio (C/T Ratio), transfusion ratio (T%) and transfusion index (TI), separately were identified. Also, the non-infusion or incomplete injection units were recorded. For data analysis student T-Test and analysis of variance and chi-square were used. Findings: 17 (60.7%) cases were men and 11 (39.3%) were female with mean age 47.36±21.30 years. The most requests for platelet concentrate were pre-operation preparation (16 cases). In 15 cases, 10 units of platelet concentrate was request for the each patient. A total of 249 units of platelet concentrate were requested for 28 patients, which 141 units were used (56.62%). Transfusion ratio, C/T ratio and TI were 71.42%, 1.76 and 5.03, respectively. Conclusion: Based on the findings, the platelet concentrate transfusion indexes in the emergency department were acceptable and can be used as a suitable approach in the management of blood products in emergency departments of the hospital

A framework of antecedents for the development of rural cooperatives

Context and purpose. This study aimed to present the framework of rural cooperative development antecedents. Methodology/approach. The approach of the present research is mixed, based on the applied purpose and exploratory in terms of data collection. The statistical population in both of qualitative and quantitative stages include experts in the field of agriculture and rural cooperatives, including managers and experts of rural cooperatives as well as managers and active members of rural cooperatives. The sampling method is purposive in qualitative stage and convenient in the quantitative stage. In the qualitative stage, semi-structured interview tools were used and the interviews continued until the theoretical saturation (26 people) was reached and theme analysis technique has also been used to analyze the data. In the quantitative stage, a questionnaire was used to collect data so that 40 questionnaires were distributed among the statistical sample and the weighted average technique was used to prioritize the topics. Findings and conclusions. Findings show that the development background of rural cooperatives can be classified into four general dimensions: individual-cognitive development, inclusive institutional development, multiple capacity building and endogenous synergy. As a result and based on the research findings, the most important preconditions for the development of rural cooperatives include thirteen components: psychological development, collaborative space development, performance management, empowerment, institutional-government development, awareness and education, financial support, infrastructure-technical capability, systems upgrading, marketing and sales, supply chain management, local development And knowledge of the interior and surroundings.Originality. Previous studies mainly in the field of pathology of rural cooperatives