1,720,968 research outputs found
Activity Recognition in Smart Homes via Feature-Rich Visual Extraction of Locomotion Traces
Full text linkThe proliferation of sensors in smart homes makes it possible to monitor human activities, routines, and complex behaviors in an unprecedented way. Hence, human activity recognition has gained increasing attention over the last few years as a tool to improve healthcare and well-being in several applications. However, most existing activity recognition systems rely on cameras or wearable sensors, which may be obtrusive and may invade the user's privacy, especially at home. Moreover, extracting expressive features from a stream of data provided by heterogeneous smart-home sensors is still an open challenge. In this paper, we investigate a novel method to detect activities of daily living by exploiting unobtrusive smart-home sensors (i.e., passive infrared position sensors and sensors attached to everyday objects) and vision-based deep learning algorithms, without the use of cameras or wearable sensors. Our method relies on depicting the locomotion traces of the user and visual clues about their interaction with objects on a floor plan map of the home, and utilizes pre-trained deep convolutional neural networks to extract features for recognizing ongoing activity. One additional advantage of our method is its seamless extendibility with additional features based on the available sensor data. Extensive experiments with a real-world dataset and a comparison with state-of-the-art approaches demonstrate the effectiveness of our method
Effect of normalizations on detecting differentially expressed genes with cDNA microarray experiments.
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Explainable AI-powered Graph Neural Networks for HD EMG-Based Gesture Intention Recognition
Full text linkThe ability to recognize fine-grained gestures enables several applications in different domains, including healthcare, robotics, remote control, and human-computer interaction. Traditional gesture recognition systems rely on data acquired from cameras, depth sensors, or smart gloves. More recently, techniques for recognizing gestures based on signals acquired by high-density (HD) EMG electrodes worn on the forearm have been proposed. An advantage of these techniques is that they do not rely on the use of external devices, and they are feasible also to people who underwent amputation. Unfortunately, the extraction of complex features from raw HD EMG signals may introduce delays that deter the real-time requirements of the system. To address this issue, in a preliminary investigation we proposed to use graph neural networks for gesture recognition from raw HD EMG data. In this paper, we extend our previous work by exploiting Explainable AI algorithms to automatically refine the graph topology based on the data in order to improve recognition rates and reduce the computational cost. We performed extensive experiments with a large dataset collected from 20 volunteers regarding the execution of 65 fine-grained gestures, comparing our technique with state-of-the-art methods based on handcrafted features and different machine learning algorithms. Experimental results show that our technique outperforms the state of the art in terms of recognition performance while incurring significantly lower computational cost at run-time
Towards EEG-based Performance Assessment in Dataset Annotation Tasks
No full textArtificial intelligence is more and more adopted to complement human activity in solving complex tasks in several domains, including healthcare, security, finance, and automation. In order to be effective, several artificial intelligence tools rely on large training sets of carefully annotated data. Since labeling is mostly performed manually, it is a costly and error-prone process. Hence, there is increasing interest in devising innovative tools to support the annotation task. In this paper, we report an initial investigation on the application of EEG data mining for evaluating the performance of humans carrying out image annotation tasks. Our approach relies on a cheap portable EEG sensor and on supervised learning methods. We collected a dataset from five volunteers, and performed an initial evaluation of our technique. The achieved results are promising, and pave the way to several research directions. To the best of our knowledge, our work is the first one applying EEG data mining for assessing the performance of labelers
Monitoring Human Attention with a Portable EEG Sensor and Supervised Machine Learning
Full text linkFor several healthcare applications, it is important to monitor the attention level of people, especially in the fields of rehabilitation and psychology. The recent availability of cheap and portable EEG readers has enabled continuous and unobtrusive acquisition of EEG signals. Those signals may be preprocessed and analysed with machine learning algorithms to estimate the attention level of people without interfering with their current activities. In this paper, we report our experience with attention level estimation using two kinds of devices: an off-the-shelf portable EEG headset, and a more sophisticated EEG device
Toward a brain-controlled prosthetic arm through advanced machine learning methods
No full textThe disability associated with limb amputation makes it difficult to perform the simplest everyday activities. Robotic prostheses can be used to address this complication. These prostheses apply machine learning methods to the EMG/ENG signals to understand the amputee's intention. The use of ENG signals compared to EMG signals is very recent, and allows not only the amputee to perform gestures, but also to mitigate the symptoms of the phantom limb and to restore the sense of touch, since the robotic arm can provide tactile feedback to the peripheral nervous system. In this work, a technique to classify ENG signals, recorded from individuals with limb amputation, is described. All the steps that compose the technique are illustrated in detail. In the last part of this article, some innovative deep learning techniques are suggested in order to improve the state-of-the-art
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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