8,284 research outputs found

    Active detection of age groups based on touch interaction

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    This paper studies user classification into children and adults according to their interaction with touchscreen devices. We analyse the performance of two sets of features derived from the Sigma-Lognormal theory of rapid human movements and global characterization of touchscreen interaction. We propose an active detection approach aimed to continuously monitorize the user patterns. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. We have used Support Vector Machines algorithm to classify the resulting features into age groups. The sets of features are fused at score level using data from smartphones and tablets. The results, with correct classification rates over 96%, show the discriminative ability of the proposed neuromotorinspired features to classify age groups according to the interaction with touch devices. In active detection setup, our method is able to identify a child using only 4 gestures in averageThis work was funded by the project CogniMetrics (TEC2015-70627-R) and Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017

    Developing mHealth Solutions for Natural Family Planning

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    Natural Family Planning (NFP) is a method to help couples determine the fertile and infertile times of a woman’s menstrual cycle with natural indicators of fertility. NFP methods have advantages over other methods of family planning. Proper use of NFP methods also ensures high effectiveness (close to 98%) in helping couples avoid pregnancy. However, very few physicians prescribe NFP to their patients due to lack of credibility to the fertility methods and lack of access to NFP knowledge. The Marquette University College of Nursing Institute for Natural Family Planning has been researching for many years to increase knowledge and efficiency of NFP. Their proposed evidencebased Marquette Model (MM) for NFP already showed success as an internet based charting system. It is obvious to have an effective mHealth (mobile health) solution for NFP because of enormous growth of smart phones. We have designed and developed muFertility, a mHealth framework that follows the MM so that couples can chart the menstrual cycles. In this thesis, we have discussed the major human computer interface (HCI) and design issues. We also have also presented how user feedback cycle based approach can be used to incorporate user experiences in the development and deployment of a mHealth solution

    Virtual Reality Interfaces for Product Design: Finding User Interface solutions for design creation within Virtual Reality

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    The focus of Virtual Reality has gone from research to widespread adoption in entertainment and practical directions, like automotive design and architectural visualization. With that, we have to take into consideration the best way to give in-experience control to the user and the interaction within the interface. Recent studies explore the ergonomic considerations and zones of content for VR interfaces. But Virtual Reality interaction design has a long way to go and nowadays is done mainly like a projection of 2D screens, with planar interfaces in the 3D space, almost ignoring the immersive potential of the Virtual Reality medium (Alger 2015; Google Developers 2017). Designers that work with 3D objects might find it difficult to make design decisions and validate their concepts based on context and empathy. To help with this, they often prototype, which can take a great deal of time and effort. Virtual reality can be a tool that improves the process and gives the designer an unconstrained and flexible canvas. By reimagining interactions for Virtual Reality, this thesis aims to create interface tools that help designers explore shape and manipulate their designs

    Active contour following to explore object shape with robot touch

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    In this work, we present an active tactile perception approach for contour following based on a probabilistic framework. Tactile data were collected using a biomimetic fingertip sensor. We propose a control architecture that implements a perception-action cycle for the exploratory procedure, which allows the fingertip to react to tactile contact whilst regulating the applied contact force. In addition' the fingertip is actively repositioned to an optimal position to ensure accurate perception. The method is trained off-line and then the testing performed on-line based on contour following around several different test shapes. We then implement object recognition based on the extracted shapes. Our active approach is compared with a passive approach, demonstrating that active perception is necessary for successful contour following and hence shape recognition

    High-resolution CBV-fMRI allows mapping of laminar activity and connectivity of cortical input and output in human M1

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    Layer-dependent fMRI allows measurements of information flow in cortical circuits, as afferent and efferent connections terminate in different cortical layers. However, it is unknown to what level human fMRI is specific and sensitive enough to reveal directional functional activity across layers. To answer this question, we developed acquisition and analysis methods for blood-oxygen-level-dependent (BOLD) and cerebral-blood-volume (CBV)-based laminar fMRI and used these to discriminate four different tasks in the human motor cortex (M1). In agreement with anatomical data from animal studies, we found evidence for somatosensory and premotor input in superficial layers of M1 and for cortico-spinal motor output in deep layers. Laminar resting-state fMRI showed directional functional connectivity of M1 with somatosensory and premotor areas. Our findings demonstrate that CBV-fMRI can be used to investigate cortical activity in humans with unprecedented detail, allowing investigations of information flow between brain regions and outperforming conventional BOLD results that are often buried under vascular biases

    Beta: Bioprinting engineering technology for academia

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    Higher STEM education is a field of growing potential, but too many middle school and high school students are not testing proficiently in STEM subjects. The BETA team worked to improve biology classroom engagement through the development of technologies for high school biology experiments. The BETA project team expanded functionality of an existing product line to allow for better student and teacher user experience and the execution of more interesting experiments. The BETA project’s first goal was to create a modular incubating Box for the high school classroom. This Box, called the BETA Box was designed with a variety of sensors to allow for custom temperature and lighting environments for each experiment. It was completed with a clear interface to control the settings and an automatic image capture system. The team also conducted a feasibility study on auto calibration and dual-extrusion for SE3D’s existing 3D bioprinter. The findings of this study led to the incorporation of a force sensor for auto calibration and the evidence to support the feasibility of dual extrusion, although further work is needed. These additions to the current SE3D educational product line will increase effectiveness in the classroom and allow the target audience, high school students, to better engage in STEM education activities

    Cost effective and Non-intrusive occupancy detection in residential building through machine learning algorithm

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    Residential and commercial buildings consume more than 40% of energy and 76% of electricity in the U.S. Buildings also emit more than one-third of U.S. greenhouse gas emissions, which is the largest sector. A significant portion of the energy is wasted by unnecessary operations on heating, ventilation, and air conditioning (HVAC) systems, such as overheating/overcooling or operation without occupants. Wasteful behaviors consume twice the amount of energy compared to energy-conscious behaviors. Many commercial buildings utilize a building management system (BMS) and occupancy sensors to better control and monitor the HVAC and lighting system based on occupancy information. However, the complicated installation process of occupancy sensors and their long payback period have prevented consumers from adopting this technology in the residential sector. Hence, I explored a method to detect the presence of an occupant and utilize it to reduce energy wasting in residential buildings. Existing methods of occupancy detection often focus on directly measure occupancy information from environmental sensors. The validity of such a sensor network highly depends on the room configurations, so the approach is not readily transferrable to other residential buildings. Instead of direct measurement, the proposed scheme detects the change of occupancy in a building. The new scheme implements machine learning methods based on a sequence of human activities that happens in a short period. Since human activities are similar regardless of house floorplan, such an approach may lead to readily transferrable to other residential buildings. I explored three types of human activity sensor to detect door handle touch, water usage, and motion near the entrance, which are highly correlated with the change of occupancy. The occupancy change is not only based on one single human activity, it also depends on a series of human activities that happen in a short period, called event. As the events have different durations and cannot be readily applicable to existing machine learning models due to varying input matrix sizes. Hence, I devised a fixed format to summarize the event regardless of the total duration of the event. Then I used a machine learning model to identify the occupancy change based on the event data. The saving potential of occupancy driven thermostat is about 20 % of energy in residential buildings. However, the actual saving impact in any given house can vary significantly from the average value due to the large variety of residential buildings. Existing building simulation tools did not readily consider the random nature of occupancy and users’ comfort. For this reason, I explored a co-simulation platform that integrates an occupancy simulator, a cooling/heating setpoint control algorithm, a comfort level evaluator, and a building simulator together. I explored the annual energy saving impact of an occupancy-driven thermostat compare with a conventional thermostat. The simulation had been repeated in five U.S. cities (Fairbanks, New York City, San Francisco, Miami, and Phoenix) with distinctive climate zones

    Index to 1981 NASA Tech Briefs, volume 6, numbers 1-4

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    Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1981 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences
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