447 research outputs found
Protective Behavior Detection in Chronic Pain Rehabilitation: From Data Preprocessing to Learning Model
Chronic pain (CP) rehabilitation extends beyond physiotherapist-directed clinical sessions and primarily functions in people's everyday lives. Unfortunately, self-directed rehabilitation is difficult because patients need to deal with both their pain and the mental barriers that pain imposes on routine functional activities. Physiotherapists adjust patients' exercise plans and advice in clinical sessions based on the amount of protective behavior (i.e., a sign of anxiety about movement) displayed by the patient. The goal of such modifications is to assist patients in overcoming their fears and maintaining physical functioning. Unfortunately, physiotherapists' support is absent during self-directed rehabilitation or also called self-management that people conduct in their daily life.
To be effective, technology for chronic-pain self-management should be able to detect protective behavior to facilitate personalized support. Thereon, this thesis addresses the key challenges of ubiquitous automatic protective behavior detection (PBD). Our investigation takes advantage of an available dataset (EmoPain) containing movement and muscle activity data of healthy people and people with CP engaged in typical everyday activities. To begin, we examine the data augmentation methods and segmentation parameters using various vanilla neural networks in order to enable activity-independent PBD within pre-segmented activity instances. Second, by incorporating temporal and bodily attention mechanisms, we improve PBD performance and support theoretical/clinical understanding of protective behavior that the attention of a person with CP shifts between body parts perceived as risky during feared movements. Third, we use human activity recognition (HAR) to improve continuous PBD in data of various activity types. The approaches proposed above are validated against the ground truth established by majority voting from expert annotators. Unfortunately, using such majority-voted ground truth causes information loss, whereas direct learning from all annotators is vulnerable to noise from disagreements. As the final study, we improve the learning from multiple annotators by leveraging the agreement information for regularization
Characterizing HCI Research in China: Streams, Methodologies and Future Directions
This position paper takes the first step to attempt to present the initial
characterization of HCI research in China. We discuss the current streams and
methodologies of Chinese HCI research based on two well-known HCI theories:
Micro/Marco-HCI and the Three Paradigms of HCI. We evaluate the discussion with
a survey of Chinese publications at CHI 2019, which shows HCI research in China
has less attention to Macro-HCI topics and the third paradigms of HCI
(Phenomenologically situated Interaction). We then propose future HCI research
directions such as paying more attention to Macro-HCI topics and third paradigm
of HCI, combining research methodologies from multiple HCI paradigms, including
emergent users who have less access to technology, and addressing the cultural
dimensions in order to provide better technical solutions and support
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
The automatic recognition of micro-expression has been boosted ever since the
successful introduction of deep learning approaches. As researchers working on
such topics are moving to learn from the nature of micro-expression, the
practice of using deep learning techniques has evolved from processing the
entire video clip of micro-expression to the recognition on apex frame. Using
the apex frame is able to get rid of redundant video frames, but the relevant
temporal evidence of micro-expression would be thereby left out. This paper
proposes a novel Apex-Time Network (ATNet) to recognize micro-expression based
on spatial information from the apex frame as well as on temporal information
from the respective-adjacent frames. Through extensive experiments on three
benchmarks, we demonstrate the improvement achieved by learning such temporal
information. Specially, the model with such temporal information is more robust
in cross-dataset validations.Comment: 6 pages, 3 figures, 3 tables, code available, accepted in ACII 201
Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
For people with chronic pain, the assessment of protective behavior during
physical functioning is essential to understand their subjective pain-related
experiences (e.g., fear and anxiety toward pain and injury) and how they deal
with such experiences (avoidance or reliance on specific body joints), with the
ultimate goal of guiding intervention. Advances in deep learning (DL) can
enable the development of such intervention. Using the EmoPain MoCap dataset,
we investigate how attention-based DL architectures can be used to improve the
detection of protective behavior by capturing the most informative temporal and
body configurational cues characterizing specific movements and the strategies
used to perform them. We propose an end-to-end deep learning architecture named
BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts
that are more informative to the detection of protective behavior. The approach
addresses the variety of ways people execute a movement (including healthy
people) independently of the type of movement analyzed. Through extensive
comparison experiments with other state-of-the-art machine learning techniques
used with motion capture data, we show statistically significant improvements
achieved by using these attention mechanisms. In addition, the BANet
architecture requires a much lower number of parameters than the state of the
art for comparable if not higher performances.Comment: 7 pages, 3 figures, 2 tables, code available, accepted in ACII 201
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
Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering
Video question answering (VideoQA) is challenging given its multimodal
combination of visual understanding and natural language processing. While most
existing approaches ignore the visual appearance-motion information at
different temporal scales, it is unknown how to incorporate the multilevel
processing capacity of a deep learning model with such multiscale information.
Targeting these issues, this paper proposes a novel Multilevel Hierarchical
Network (MHN) with multiscale sampling for VideoQA. MHN comprises two modules,
namely Recurrent Multimodal Interaction (RMI) and Parallel Visual Reasoning
(PVR). With a multiscale sampling, RMI iterates the interaction of
appearance-motion information at each scale and the question embeddings to
build the multilevel question-guided visual representations. Thereon, with a
shared transformer encoder, PVR infers the visual cues at each level in
parallel to fit with answering different question types that may rely on the
visual information at relevant levels. Through extensive experiments on three
VideoQA datasets, we demonstrate improved performances than previous
state-of-the-arts and justify the effectiveness of each part of our method
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
Networks found in the real-world are numerous and varied. A common type of
network is the heterogeneous network, where the nodes (and edges) can be of
different types. Accordingly, there have been efforts at learning
representations of these heterogeneous networks in low-dimensional space.
However, most of the existing heterogeneous network embedding methods suffer
from the following two drawbacks: (1) The target space is usually Euclidean.
Conversely, many recent works have shown that complex networks may have
hyperbolic latent anatomy, which is non-Euclidean. (2) These methods usually
rely on meta-paths, which require domain-specific prior knowledge for meta-path
selection. Additionally, different down-streaming tasks on the same network
might require different meta-paths in order to generate task-specific
embeddings. In this paper, we propose a novel self-guided random walk method
that does not require meta-path for embedding heterogeneous networks into
hyperbolic space. We conduct thorough experiments for the tasks of network
reconstruction and link prediction on two public datasets, showing that our
model outperforms a variety of well-known baselines across all tasks.Comment: In proceedings of the 35th AAAI Conference on Artificial Intelligenc
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