3,145 research outputs found

    ANGELAH: A Framework for Assisting Elders At Home

    Get PDF
    The ever growing percentage of elderly people within modern societies poses welfare systems under relevant stress. In fact, partial and progressive loss of motor, sensorial, and/or cognitive skills renders elders unable to live autonomously, eventually leading to their hospitalization. This results in both relevant emotional and economic costs. Ubiquitous computing technologies can offer interesting opportunities for in-house safety and autonomy. However, existing systems partially address in-house safety requirements and typically focus on only elder monitoring and emergency detection. The paper presents ANGELAH, a middleware-level solution integrating both ”elder monitoring and emergency detection” solutions and networking solutions. ANGELAH has two main features: i) it enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and ii) provides a solid framework for creating and managing rescue teams composed of individuals willing to promptly assist elders in case of emergency situations. A prototype of ANGELAH, designed for a case study for helping elders with vision impairments, is developed and interesting results are obtained from both computer simulations and a real-network testbed

    Would adults with autism be less likely to bury the survivors? An eye movement study of anomalous text reading

    Get PDF
    In a single eye movement experiment we investigated the effects of context on the time course of local and global anomaly processing during reading in adults with Autism Spectrum Disorder (ASD). In one condition short paragraph texts contained anomalous target words. Detection of the anomaly was only possible through evaluation of word meaning in relation to the global context of the whole paragraph (Passage Level Anomalies). In another condition the anomaly could be detected via computation of a local thematic violation within a single sentence embedded in the paragraph (Sentence Level Anomalies).For the sentence level anomalies the ASD group, in contrast with the typically developing (TD) group, showed early detection of the anomaly as indexed by regressive eye movements from the critical target word upon fixation. Conversely, for the passage level anomalies, and in contrast with the ASD group, the TD group showed early detection of the anomaly, with increased regressive eye movements once the critical word had been fixated.The reversal of the pattern of regression path data for the two groups, for the sentence and passage level anomalies, is discussed in relation to cognitive accounts of ASD

    Developmental disorders of vision

    Get PDF
    This review of developmental disorders of vision focuses on a few of the many disorders that disrupt visual development. Given the enormity of the human visual system in the primate brain and complexity of visual development, however, there are likely hundreds or thousands of potential types of disorders affecting high-level vision. The rapid progress seen in developmental dyslexia and Williams syndrome demonstrates the possibilities and difficulties inherent in researching such disorders, and the authors hope that similar progress will be made for congenital prosopagnosia and other disorders in the near future

    The neurocognition of syntactic processing

    Get PDF

    Schizotypal personality models

    Get PDF

    Locomotion Traces Data Mining for Supporting Frail People with Cognitive Impairment

    Get PDF
    The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of older people. Thus, this thesis at first focused on providing a systematic literature review of locomotion data mining systems for supporting Neuro-Degenerative Diseases (NDD) diagnosis, identifying locomotion anomaly indicators and movement patterns for discovering low-level locomotion indicators, sensor data acquisition, and processing methods, as well as NDD detection algorithms considering their pros and cons. Then, we investigated the use of sensor data and Deep Learning (DL) to recognize abnormal movement patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduced novel visual feature extraction methods for locomotion data. Our solutions rely on locomotion traces segmentation, image-based extraction of salient features from locomotion segments, and vision-based DL. Furthermore, we proposed a data augmentation strategy to increase the volume of collected data and generalize the solution to different smart-homes with different layouts. We carried out extensive experiments with a large real-world dataset acquired in a smart-home test-bed from older people, including people with cognitive diseases. Experimental comparisons show that our system outperforms state-of-the-art methods

    A Framework for Discovery and Diagnosis of Behavioral Transitions in Event-streams

    Get PDF
    Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant behavioral change. Decision tree, sequence pattern and Hidden Markov modeling being used in this study. These three types of modeling can expose different aspect of user’s behavior. In case of decision tree modeling, the specific changes in user behavior can automatically characterized by differencing the data-mined decision-tree models. The sequence pattern modeling can shed light on how the user changes his sequence of actions and Hidden Markov modeling can identifies the learning transition points. This research describes how model-quality monitoring and these three types of modeling as a generic framework can aid recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The date stream mining techniques mentioned are used to monitor patient goals as part of a clinical plan to aid cognitive rehabilitation. In this context, real time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. This generic framework can be widely applicable to other real-time data-intensive analysis problems. In order to illustrate this fact, the similar Hidden Markov modeling is being used for analyzing the transactional behavior of a telecommunication company for fraud detection. Fraud similarly can be considered as a potentially significant transaction behavioral change

    Preservation of long-term memory and synaptic plasticity despite short-term impairments in the Tc1 mouse model of Down syndrome

    Get PDF
    Down syndrome (DS) is a genetic disorder arising from the presence of a third copy of the human chromosome 21 (Hsa21). Recently, O’Doherty and colleagues in an earlier study generated a new genetic mouse model of DS (Tc1) that carries an almost complete Hsa21. Since DS is the most common genetic cause of mental retardation, we have undertaken a detailed analysis of cognitive function and synaptic plasticity in Tc1 mice. Here we show that Tc1 mice have impaired spatial working memory (WM) but spared long-term spatial reference memory (RM) in the Morris watermaze. Similarly, Tc1 mice are selectively impaired in short-term memory (STM) but have intact long-term memory (LTM) in the novel object recognition task. The pattern of impaired STM and normal LTM is paralleled by a corresponding phenotype in long-term potentiation (LTP). Freely-moving Tc1 mice exhibit reduced LTP 1 h after induction but normal maintenance over days in the dentate gyrus of the hippocampal formation. Biochemical analysis revealed a reduction in membrane surface expression of the AMPAR (α-amino-3-hydroxy-5-methyl-4-propionic acid receptor) subunit GluR1 in the hippocampus of Tc1 mice, suggesting a potential mechanism for the impairment in early LTP. Our observations also provide further evidence that STM and LTM for hippocampus-dependent tasks are subserved by parallel processing streams
    corecore