32,716 research outputs found
Chronic-Pain Protective Behavior Detection with Deep Learning
In chronic pain rehabilitation, physiotherapists adapt physical activity to
patients' performance based on their expression of protective behavior,
gradually exposing them to feared but harmless and essential everyday
activities. As rehabilitation moves outside the clinic, technology should
automatically detect such behavior to provide similar support. Previous works
have shown the feasibility of automatic protective behavior detection (PBD)
within a specific activity. In this paper, we investigate the use of deep
learning for PBD across activity types, using wearable motion capture and
surface electromyography data collected from healthy participants and people
with chronic pain. We approach the problem by continuously detecting protective
behavior within an activity rather than estimating its overall presence. The
best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross
validation. When protective behavior is modelled per activity type, performance
is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for
sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This
performance reaches excellent level of agreement with the average experts'
rating performance suggesting potential for personalized chronic pain
management at home. We analyze various parameters characterizing our approach
to understand how the results could generalize to other PBD datasets and
different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on
Computing for Healthcar
Learning Visual Importance for Graphic Designs and Data Visualizations
Knowing where people look and click on visual designs can provide clues about
how the designs are perceived, and where the most important or relevant content
lies. The most important content of a visual design can be used for effective
summarization or to facilitate retrieval from a database. We present automated
models that predict the relative importance of different elements in data
visualizations and graphic designs. Our models are neural networks trained on
human clicks and importance annotations on hundreds of designs. We collected a
new dataset of crowdsourced importance, and analyzed the predictions of our
models with respect to ground truth importance and human eye movements. We
demonstrate how such predictions of importance can be used for automatic design
retargeting and thumbnailing. User studies with hundreds of MTurk participants
validate that, with limited post-processing, our importance-driven applications
are on par with, or outperform, current state-of-the-art methods, including
natural image saliency. We also provide a demonstration of how our importance
predictions can be built into interactive design tools to offer immediate
feedback during the design process
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Training of neural networks for automated diagnosis of pigmented skin lesions
is hampered by the small size and lack of diversity of available datasets of
dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human
Against Machine with 10000 training images") dataset. We collected
dermatoscopic images from different populations acquired and stored by
different modalities. Given this diversity we had to apply different
acquisition and cleaning methods and developed semi-automatic workflows
utilizing specifically trained neural networks. The final dataset consists of
10015 dermatoscopic images which are released as a training set for academic
machine learning purposes and are publicly available through the ISIC archive.
This benchmark dataset can be used for machine learning and for comparisons
with human experts. Cases include a representative collection of all important
diagnostic categories in the realm of pigmented lesions. More than 50% of
lesions have been confirmed by pathology, while the ground truth for the rest
of the cases was either follow-up, expert consensus, or confirmation by in-vivo
confocal microscopy
A framework for interrogating social media images to reveal an emergent archive of war
The visual image has long been central to how war is seen, contested and legitimised, remembered and forgotten. Archives are pivotal to these ends as is their ownership and access, from state and other official repositories through to the countless photographs scattered and hidden from a collective understanding of what war looks like in individual collections and dusty attics. With the advent and rapid development of social media, however, the amateur and the professional, the illicit and the sanctioned, the personal and the official, and the past and the present, all seem to inhabit the same connected and chaotic space.However, to even begin to render intelligible the complexity, scale and volume of what war looks like in social media archives is a considerable task, given the limitations of any traditional human-based method of collection and analysis. We thus propose the production of a series of ‘snapshots’, using computer-aided extraction and identification techniques to try to offer an experimental way in to conceiving a new imaginary of war. We were particularly interested in testing to see if twentieth century wars, obviously initially captured via pre-digital means, had become more ‘settled’ over time in terms of their remediated presence today through their visual representations and connections on social media, compared with wars fought in digital media ecologies (i.e. those fought and initially represented amidst the volume and pervasiveness of social media images).To this end, we developed a framework for automatically extracting and analysing war images that appear in social media, using both the features of the images themselves, and the text and metadata associated with each image. The framework utilises a workflow comprising four core stages: (1) information retrieval, (2) data pre-processing, (3) feature extraction, and (4) machine learning. Our corpus was drawn from the social media platforms Facebook and Flickr
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