1,721,043 research outputs found
Fostering Human Activity Recognition Workflows: An Open-Source Baseline Framework
No full textThe application of machine and deep learning algorithms in Human Activity Recognition (HAR) has shown great potential for monitoring various professional and daily life activities, benefiting different research areas such as healthcare, well-being and industrial automation. HAR can enable the development of various services and applications to empower technical performance and enable risk prevention in working places, to support education and training, and, more in general, to monitor the biopsychosocial status of people. However, we still lack a baseline framework for easily implementing the data processing pipeline that must be designed to setup and configure HAR workflows. This makes challenging to estimate the effectiveness, efficiency, and the overall quality of HAR solutions, thus hindering the comparison among different approaches. This also increases the likelihood that researchers introduce errors, which negatively affect the accuracy of the obtained results. To fill in the gap, this paper introduces B-HAR, an open-source framework to automatically implement baseline HAR workflows
Trace elements and REE geochemistry of Middle Devonian carbonate mounds (Maïder Basin, Eastern Anti-Atlas, Morocco): Implications for early diagenetic processes
No full textTrace and rare earth elements (REEs) have proven their utility as tools for assessing the genesis and early diagenesis of widespread geological bodies such as carbonate mounds, whose genetic processes are not yet fully understood. Carbonates from the Middle Devonian conical mud mounds of the Maïder Basin (eastern Anti-Atlas, Morocco) have been analysed for their REE and trace element distribution. Collectively, the carbonates from the Maïder Basin mud mounds appear to display coherent REE patterns. Three different geochemical patterns, possibly related with three different diagenetic events, include: i) dyke fills with a normal marine REE pattern probably precipitated in equilibrium with seawater, ii) mound micrite with a particular enrichment of overall REE contents and variable Ce anomaly probably related to variation of pH, increase of alkalinity or dissolution/remineralization of organic matter during early diagenesis, and iii) haematite-rich vein fills precipitated from venting fluids of probable hydrothermal origin. Our results reinforce the hypothesis that these mounds were probably affected by an early diagenesis induced by microbial activity and triggered by abundance of dispersed organic matter, whilst venting may have affected the mounds during a later diagenetic phase
Estimating Indoor Occupancy Through Low-Cost BLE Devices
Full text linkDetecting the presence of persons and estimating their quantity in an indoor environment has grown in importance recently. For example, the information if a room is unoccupied can be used for automatically switching off the light, air conditioning, and ventilation, thereby saving significant amounts of energy in public buildings. Most existing solutions rely on dedicated hardware installations, which involve presence sensors, video cameras, and carbon dioxide sensors. Unfortunately, such approaches are costly, subject to privacy concerns, have high computational requirements, and lack ubiquitousness. The work presented in this article addresses these limitations by proposing a low-cost occupancy detection system. Our approach builds upon detecting variations in Bluetooth Low Energy (BLE) signals related to the presence of humans. The effectiveness of this approach is evaluated by performing comprehensive tests on five different datasets. We apply several pattern recognition models and compare our methodology with systems building upon IEEE 802.11 (WiFi). On average, in multifarious environments, we can correctly classify the occupancy with an accuracy of 97.97%. When estimating the number of people in a room, on average, the estimated number of subjects differs from the actual one by 0.32 persons. We conclude that our system's performance is comparable to that of existing ones based on WiFi, while significantly reducing cost and installation effort. Hence, our approach makes occupancy detection practical for real-world deployments
Non-invasive monitoring of Alzheimer's patients through WiFi channel state information
No full textThe design of noninvasive systems for monitoring people's activities is becoming of central interest in recent years. Such systems are essential for those affected by diseases that modify their cognitive status and are not collaborative in using wearable or interactive systems (e.g., mobile apps to communicate). This is particularly true regarding neurodegenerative diseases that involve memory loss, cognitive decline, communication difficulties, behavioral changes, loss of independence, and physical complications. In response to the need of healthcare structures and caregivers to monitor this category of people during their in-home daily life, this paper proposes a nonintrusive system capable of detecting whether or not a person is in his/her room and if he/she is lying on the bed. Checking these conditions is of utmost importance, in particular, during the night to support the monitoring activity of caregivers and social-health operators taking care of people with Dementia and Alzheimer's disease. The proposed system exploits WiFi's Channel State Information (CSI) gathered by common access points installed in the room. CSI data are then used to train a Convolutional Neural Network (CNN) and a fine-tuning technique is applied to increase the generalization capabilities of the CNN model on new environments. In our experimental analysis, we trained the CNN model by collecting CSI data in four different rooms, from two subjects performing three distinct activities. Promising results have been achieved (accuracy > 97.5%) in recognizing the target activities
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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