24,214 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
Socially Constrained Structural Learning for Groups Detection in Crowd
Modern crowd theories agree that collective behavior is the result of the
underlying interactions among small groups of individuals. In this work, we
propose a novel algorithm for detecting social groups in crowds by means of a
Correlation Clustering procedure on people trajectories. The affinity between
crowd members is learned through an online formulation of the Structural SVM
framework and a set of specifically designed features characterizing both their
physical and social identity, inspired by Proxemic theory, Granger causality,
DTW and Heat-maps. To adhere to sociological observations, we introduce a loss
function (G-MITRE) able to deal with the complexity of evaluating group
detection performances. We show our algorithm achieves state-of-the-art results
when relying on both ground truth trajectories and tracklets previously
extracted by available detector/tracker systems
What Twitter Profile and Posted Images Reveal About Depression and Anxiety
Previous work has found strong links between the choice of social media
images and users' emotions, demographics and personality traits. In this study,
we examine which attributes of profile and posted images are associated with
depression and anxiety of Twitter users. We used a sample of 28,749 Facebook
users to build a language prediction model of survey-reported depression and
anxiety, and validated it on Twitter on a sample of 887 users who had taken
anxiety and depression surveys. We then applied it to a different set of 4,132
Twitter users to impute language-based depression and anxiety labels, and
extracted interpretable features of posted and profile pictures to uncover the
associations with users' depression and anxiety, controlling for demographics.
For depression, we find that profile pictures suppress positive emotions rather
than display more negative emotions, likely because of social media
self-presentation biases. They also tend to show the single face of the user
(rather than show her in groups of friends), marking increased focus on the
self, emblematic for depression. Posted images are dominated by grayscale and
low aesthetic cohesion across a variety of image features. Profile images of
anxious users are similarly marked by grayscale and low aesthetic cohesion, but
less so than those of depressed users. Finally, we show that image features can
be used to predict depression and anxiety, and that multitask learning that
includes a joint modeling of demographics improves prediction performance.
Overall, we find that the image attributes that mark depression and anxiety
offer a rich lens into these conditions largely congruent with the
psychological literature, and that images on Twitter allow inferences about the
mental health status of users.Comment: ICWSM 201
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