1,720,972 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
A Minimal and Multi-Source Recording Setup for Ankle Joint Kinematics Estimation During Walking Using Only Proximal Information From Lower Limb
Get PDFIn this study, a minimal setup for the ankle joint kinematics estimation is proposed relying only on proximal information of the lower-limb, i.e. thigh muscles activity and joint kinematics. To this purpose, myoelectric activity of Rectus Femoris (RF), Biceps Femoris (BF), and Vastus Medialis (VM) were recorded by surface electromyography (sEMG) from six healthy subjects during unconstrained walking task. For each subject, the angular kinematics of hip and ankle joints were synchronously recorded with sEMG signal for a total of 288 gait cycles. Two feature sets were extracted from sEMG signals, i.e. time domain (TD) and wavelet (WT) and compared to have a compromise between the reliability and computational capacity, they were used for feeding three regression models, i.e. Artificial Neural Networks, Random Forest, and Least Squares - Support Vector Machine (LS-SVM). BF together with LS-SVM provided the best ankle angle estimation in both TD and WT domains (RMSE < 5.6 deg). The inclusion of Hip joint trajectory significantly enhanced the regression performances of the model (RMSE < 4.5 deg). Results showed the feasibility of estimating the ankle trajectory using only proximal and limited information from the lower limb which would maximize a potential transfemoral amputee user's comfortability while facing the challenge of having a small amount of information thus requiring robust data-driven models. These findings represent a significant step towards the development of a minimal setup useful for the control design of ankle active prosthetics and rehabilitative solutions
Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model
Get PDFThe volitional control of powered assistive devices is commonly performed by mapping the electromyographic (EMG) activity of the lower limb to joints’ angular kinematics, which are then used as the input for regulation. However, during walking, the ground reaction force (GRF) plays a central role in the modulation of the gait, providing dynamic stability and propulsion during the stance phase. Including this information within the control loop of prosthetic devices can improve the quality of the final output, providing more physiological walking dynamics that enhances the usability and patient comfort. In this work, we explored the feasibility of the estimation of the ground reaction force vertical component (VGRF) by using only the EMG activities of the thigh and shank muscles. We compared two deep learning models in three experiments that involved different muscular configurations. Overall, the outcomes show that the EMG signals could be leveraged to obtain a reliable estimation of the VGRF during walking, and the shank muscles alone represent a viable solution if a reduced recording setup is needed. On the other hand, the thigh muscles failed in providing performance enhancements, either when used alone or together with the shank muscles. The results outline the feasibility of including GRF information within an EMG-driven control scheme for prosthetic and assistive devices
Impact of sampling frequency and signal quantization on myoelectric-based hand gesture recognition
No full textThe rapid advancement of wearable technologies has facilitated the acquisition of myoelectric signals, which are increasingly used as input for machine learning (ML) architectures to recognize human motion. However, the technical specifications of sensors and the experimental setup can significantly affect signal quality, potentially reducing the reliability of motor command recognition. This study investigates how signal quantization (ADC resolution) and sampling frequency influence the performance of myoelectric hand gesture recognition. Surface EMG was recorded with an armband during 20 gestures performed by 10 healthy subjects. Three acquisition settings were tested: 8-bit/500 Hz, 8-bit/1000 Hz, and 12-bit/500 Hz. A time-domain feature set was extracted and used to train three classifiers: linear discriminant analysis (LDA), linear support vector machine (SVM), and quadratic SVM (SVMQ). Results show that higher sampling frequency consistently improved classification accuracy, both with the full armband configuration and with a reduced sensor setup (4 channels). The linear SVM trained with the complete feature set achieved the best performance, with accuracy up to 90% using all sensors and around 80% with the minimal configuration. Even when trained with a single feature, such as mean absolute value or waveform length, the full configuration yielded accuracy above 80% across conditions. In contrast, ADC resolution had only a marginal impact on performance. Overall, the findings indicate that appropriate feature selection and sensor configuration can mitigate the effects of lower sampling rates, offering practical trade-offs between recognition accuracy and computational efficiency in wearable EMG-based systems
Impact of sampling frequency and signal quantization on myoelectric-based hand gesture recognition
No full textThe rapid advancement of wearable technologies has facilitated the acquisition of myoelectric signals, which are increasingly used as input for machine learning (ML) architectures to recognize human motion. However, the technical specifications of sensors and the experimental setup can significantly affect signal quality, potentially reducing the reliability of motor command recognition. This study investigates how signal quantization (ADC resolution) and sampling frequency influence the performance of myoelectric hand gesture recognition. Surface EMG was recorded with an armband during 20 gestures performed by 10 healthy subjects. Three acquisition settings were tested: 8-bit/500 Hz, 8-bit/1000 Hz, and 12-bit/500 Hz. A time-domain feature set was extracted and used to train three classifiers: linear discriminant analysis (LDA), linear support vector machine (SVM), and quadratic SVM (SVMQ). Results show that higher sampling frequency consistently improved classification accuracy, both with the full armband configuration and with a reduced sensor setup (4 channels). The linear SVM trained with the complete feature set achieved the best performance, with accuracy up to 90% using all sensors and around 80% with the minimal configuration. Even when trained with a single feature, such as mean absolute value or waveform length, the full configuration yielded accuracy above 80% across conditions. In contrast, ADC resolution had only a marginal impact on performance. Overall, the findings indicate that appropriate feature selection and sensor configuration can mitigate the effects of lower sampling rates, offering practical trade-offs between recognition accuracy and computational efficiency in wearable EMG-based systems
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