8,569 research outputs found
Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints
Physical exercise is an essential component of rehabilitation programs that
improve quality of life and reduce mortality and re-hospitalization rates. In
AI-driven virtual rehabilitation programs, patients complete their exercises
independently at home, while AI algorithms analyze the exercise data to provide
feedback to patients and report their progress to clinicians. To analyze
exercise data, the first step is to segment it into consecutive repetitions.
There has been a significant amount of research performed on segmenting and
counting the repetitive activities of healthy individuals using raw video data,
which raises concerns regarding privacy and is computationally intensive.
Previous research on patients' rehabilitation exercise segmentation relied on
data collected by multiple wearable sensors, which are difficult to use at home
by rehabilitation patients. Compared to healthy individuals, segmenting and
counting exercise repetitions in patients is more challenging because of the
irregular repetition duration and the variation between repetitions. This paper
presents a novel approach for segmenting and counting the repetitions of
rehabilitation exercises performed by patients, based on their skeletal body
joints. Skeletal body joints can be acquired through depth cameras or computer
vision techniques applied to RGB videos of patients. Various sequential neural
networks are designed to analyze the sequences of skeletal body joints and
perform repetition segmentation and counting. Extensive experiments on three
publicly available rehabilitation exercise datasets, KIMORE, UI-PRMD, and
IntelliRehabDS, demonstrate the superiority of the proposed method compared to
previous methods. The proposed method enables accurate exercise analysis while
preserving privacy, facilitating the effective delivery of virtual
rehabilitation programs.Comment: 8 pages, 1 figure, 2 table
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Sign Language Translation from Instructional Videos
The advances in automatic sign language translation (SLT) to spoken languages
have been mostly benchmarked with datasets of limited size and restricted
domains. Our work advances the state of the art by providing the first baseline
results on How2Sign, a large and broad dataset.
We train a Transformer over I3D video features, using the reduced BLEU as a
reference metric for validation, instead of the widely used BLEU score. We
report a result of 8.03 on the BLEU score, and publish the first open-source
implementation of its kind to promote further advances.Comment: Paper accepted at WiCV @CVPR2
Comedians without a Cause: The Politics and Aesthetics of Humour in Dutch Cabaret (1966-2020)
Comedians play an important role in society and public debate. While comedians have been considered important cultural critics for quite some time, comedy has acquired a new social and political significance in recent years, with humour taking centre stage in political and social debates around issues of identity, social justice, and freedom of speech. To understand the shifting meanings and political implications of humour within a Dutch context, this PhD thesis examines the political and aesthetic workings of humour in the highly popular Dutch cabaret genre, focusing on cabaret performances from the 1960s to the present. The central questions of the thesis are: how do comedians use humour to deliver social critique, and how does their humour resonate with political ideologies? These questions are answered by adopting a cultural studies approach to humour, which is used to analyse Dutch cabaret performances, and by studying related materials such as reviews and media interviews with comedians. This thesis shows that, from the 1960s onwards, Dutch comedians have been considered ‘progressive rebels’ – politically engaged, subversive, and carrying a left-wing political agenda – but that this image is in need of correction. While we tend to look for progressive political messages in the work of comedians who present themselves as being anti-establishment rebels – such as Youp van ‘t Hek, Hans Teeuwen, and Theo Maassen – this thesis demonstrates that their transgressive and provocative humour tends to protect social hierarchies and relationships of power. Moreover, it shows that, paradoxically, both the deliberately moderate and nuanced humour of Wim Kan and Claudia de Breij, and the seemingly past-oriented nostalgia of Alex Klaasen, are more radical and progressive than the transgressive humour of van ‘t Hek, Teeuwen and Maassen. Finally, comedians who present absurdist or deconstructionist forms of humour, such as the early student cabarets, Freek de Jonge, and Micha Wertheim, tend to disassociate themselves from an explicit political engagement. By challenging the dominant image of the Dutch comedian as a ‘progressive rebel,’ this thesis contributes to a better understanding of humour in the present cultural moment, in which humour is often either not taken seriously, or one-sidedly celebrated as being merely pleasurable, innocent, or progressively liberating. In so doing, this thesis concludes, the ‘dark’ and more conservative sides of humour tend to get obscured
Hi4D: 4D Instance Segmentation of Close Human Interaction
We propose Hi4D, a method and dataset for the automatic analysis of
physically close human-human interaction under prolonged contact. Robustly
disentangling several in-contact subjects is a challenging task due to
occlusions and complex shapes. Hence, existing multi-view systems typically
fuse 3D surfaces of close subjects into a single, connected mesh. To address
this issue we leverage i) individually fitted neural implicit avatars; ii) an
alternating optimization scheme that refines pose and surface through periods
of close proximity; and iii) thus segment the fused raw scans into individual
instances. From these instances we compile Hi4D dataset of 4D textured scans of
20 subject pairs, 100 sequences, and a total of more than 11K frames. Hi4D
contains rich interaction-centric annotations in 2D and 3D alongside accurately
registered parametric body models. We define varied human pose and shape
estimation tasks on this dataset and provide results from state-of-the-art
methods on these benchmarks.Comment: Project page: https://yifeiyin04.github.io/Hi4D
Procedure-Aware Pretraining for Instructional Video Understanding
Our goal is to learn a video representation that is useful for downstream
procedure understanding tasks in instructional videos. Due to the small amount
of available annotations, a key challenge in procedure understanding is to be
able to extract from unlabeled videos the procedural knowledge such as the
identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or
the potential next steps given partial progress in its execution. Our main
insight is that instructional videos depict sequences of steps that repeat
between instances of the same or different tasks, and that this structure can
be well represented by a Procedural Knowledge Graph (PKG), where nodes are
discrete steps and edges connect steps that occur sequentially in the
instructional activities. This graph can then be used to generate pseudo labels
to train a video representation that encodes the procedural knowledge in a more
accessible form to generalize to multiple procedure understanding tasks. We
build a PKG by combining information from a text-based procedural knowledge
database and an unlabeled instructional video corpus and then use it to
generate training pseudo labels with four novel pre-training objectives. We
call this PKG-based pre-training procedure and the resulting model Paprika,
Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We
evaluate Paprika on COIN and CrossTask for procedure understanding tasks such
as task recognition, step recognition, and step forecasting. Paprika yields a
video representation that improves over the state of the art: up to 11.23%
gains in accuracy in 12 evaluation settings. Implementation is available at
https://github.com/salesforce/paprika.Comment: CVPR 202
The Psychology of Trust from Relational Messages
A fundamental underpinning of all social relationships is trust. Trust can be established through implicit forms of communication called relational messages. A multidisciplinary, multi-university, cross-cultural investigation addressed how these message themes are expressed and whether they are moderated by culture and veracity. A multi-round decision-making game with 695 international participants assessed the nonverbal and verbal behaviors that express such meanings as affection, dominance, and composure, from which people ultimately determine who can be trusted and who not. Analysis of subjective judgments showed that trust was most predicted by dominance, then affection, and lastly, composure. Behaviorally, several nonverbal and verbal behaviors associated with these message themes were combined to predict trust. Results were similar across cultures but moderated by veracity. Methodologically, automated software extracted facial features, vocal features, and linguistic metrics associated with these message themes. A new attentional computer vision method retrospectively identified specific meaningful segments where relational messages were expressed. The new software tools and attentional model hold promise for identifying nuanced, implicit meanings that together predict trust and that can, in combination, serve as proxies for trust
The determinants of value addition: a crtitical analysis of global software engineering industry in Sri Lanka
It was evident through the literature that the perceived value delivery of the global software
engineering industry is low due to various facts. Therefore, this research concerns global
software product companies in Sri Lanka to explore the software engineering methods and
practices in increasing the value addition. The overall aim of the study is to identify the key
determinants for value addition in the global software engineering industry and critically
evaluate the impact of them for the software product companies to help maximise the value
addition to ultimately assure the sustainability of the industry.
An exploratory research approach was used initially since findings would emerge while the
study unfolds. Mixed method was employed as the literature itself was inadequate to
investigate the problem effectively to formulate the research framework. Twenty-three face-to-face online interviews were conducted with the subject matter experts covering all the
disciplines from the targeted organisations which was combined with the literature findings as
well as the outcomes of the market research outcomes conducted by both government and nongovernment institutes. Data from the interviews were analysed using NVivo 12. The findings
of the existing literature were verified through the exploratory study and the outcomes were
used to formulate the questionnaire for the public survey. 371 responses were considered after
cleansing the total responses received for the data analysis through SPSS 21 with alpha level
0.05. Internal consistency test was done before the descriptive analysis. After assuring the
reliability of the dataset, the correlation test, multiple regression test and analysis of variance
(ANOVA) test were carried out to fulfil the requirements of meeting the research objectives.
Five determinants for value addition were identified along with the key themes for each area.
They are staffing, delivery process, use of tools, governance, and technology infrastructure.
The cross-functional and self-organised teams built around the value streams, employing a
properly interconnected software delivery process with the right governance in the delivery
pipelines, selection of tools and providing the right infrastructure increases the value delivery.
Moreover, the constraints for value addition are poor interconnection in the internal processes,
rigid functional hierarchies, inaccurate selections and uses of tools, inflexible team
arrangements and inadequate focus for the technology infrastructure. The findings add to the
existing body of knowledge on increasing the value addition by employing effective processes,
practices and tools and the impacts of inaccurate applications the same in the global software
engineering industry
TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH
Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)
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