1,720,970 research outputs found
Human-Machine Interfaces using Distributed Sensing and Stimulation Systems
No full textAs the technology moves towards more natural human-machine interfaces (e.g. bionic limbs, teleoperation, virtual reality), it is necessary to develop a sensory feedback system in order to foster embodiment and achieve better immersion in the control system. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing a wide bandwidth of information. To provide this type of feedback, it is necessary to develop a distributed sensing system that could extract a wide range of information during the interaction between the robot and the environment. In addition, a distributed feedback interface is needed to deliver such information to the user. This thesis proposes the development of a distributed sensing system (e-skin) to acquire tactile sensation, a first integration of distributed sensing system on a robotic hand, the development of a sensory feedback system that compromises the distributed sensing system and a distributed stimulation system, and finally the implementation of deep learning methods for the classification of tactile data. It’s core focus addresses the development and testing of a sensory feedback system, based on the latest distributed sensing and stimulation techniques. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives, and the used methodology and contributions; as well as six studies that tackled the development of human-machine interfaces
Hardness Discrimination Using Piezoelectric-Based Biomimetic Tactile Sensor and Machine Learning
No full textIn this letter, we present a tactile sensing system based on piezoelectric sensors, embedded electronics, and a machine learning (ML)-based approach for hardness discrimination. Various statistical features were extracted and evaluated through machine learning algorithms including support vector machines (SVM), single-layer feed-forward neural networks, and k-nearest neighbor (KNN). Five hardness objects were examined by performing indentation experiments using a Cartesian robot equipped with the sensing system while varying the indentation speed and load. Results showed that the SVM classifier trained on features ranked using principal component analysis (PCA) achieves a discrimination accuracy of 96% while utilizing a single sensor. Furthermore, results demonstrated that fixing the indentation speed and load increases the discrimination accuracy to 100%. This study demonstrated the capability of the tactile sensing system in extracting tactile information opening up interesting perspectives for wearable sensing and soft robots
Investigating Cutaneous Mechanoreceptors for Neuromorphic Tactile Texture Classification
No full textThis paper investigates the computational cost of modeling the response of the Type-I and Type-II cutaneous human mechanoreceptors for neuromorphic texture classification. We examined both the number of floating operations for modeling the receptors, and the number of synaptic operations for recurrent spiking neural networks (RSNNs) used in classification. Results show that deeper receptors (Type-II) require a greater computational cost to be modeled than those close to the surface (Type-I). However, RSNNs linked with deeper receptors exhibit a lower cost. We evaluated the energy consumption of the modeling and classification parts, each on its dedicated hardware device. The results suggest that pairing Type-I receptors with their corresponding RSNNs offers the best trade-off between energy consumption and classification accuracy
Biomimetic Tactile Sensing for Hannes Anthropomorphic Prosthetic Hand
No full textA prosthetic device replicating the human hand capabilities is very challenging to achieve, not only for the intrinsic nature of the very complex movements, anthropomorphism, and aesthetics but also for the sophisticated capabilities that its net of receptors offers from the somatosensory perspective. Therefore, providing seamless human hand capabilities in a single device is still an open topic. Hannes Hand prosthesis exemplifies advancements in prosthetic technology, and, for this research activity, a preliminary integration of P(VDF-TrFE) piezoelectric sensors enhances novel tactile sensing capabilities. This study focuses on the sensorization of the Hannes Hand, aiming to bridge the gap between human hand functionalities and prosthetic performance. We present a preliminary implementation of sensor arrays embedded within the prosthetic glove, ensuring high sensitivity and responsiveness. Our approach aims at emphasizing sensor fusion toward the development of comprehensive feedback and intuitive control. Through a preliminary comparison analysis with human mechanoreceptors, we highlight the effectiveness of our piezoelectric sensors in replicating rapid adaptive behaviours, crucial for dynamic interaction with the environment
Novel Wearable Tactile Feedback System for post-stroke Rehabilitation
No full textThis paper presents a novel system for tactile feedback integrating advanced sensorized glove and non- invasive stimulation interface. The system comprises a textile glove that integrates 64 sensors capable of capturing distributed tactile signals during usual manual activities, embedded interface electronics, multichannel simulator and non-invasive stimulation interface. Several experiments have been performed to test the capability of the system to capture and to deliver to the user static or dynamic patterns during manual interactions. The experiments demonstrated that the system successfully translated the mechanical interaction into electrotactile profiles within a delay of 32 ms, opening up interesting perspectives for wearable feedback systems for post-stroke rehabilitation
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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