152 research outputs found
StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation
Anticipation problem has been studied considering different aspects such as
predicting humans' locations, predicting hands and objects trajectories, and
forecasting actions and human-object interactions. In this paper, we studied
the short-term object interaction anticipation problem from the egocentric
point of view, proposing a new end-to-end architecture named StillFast. Our
approach simultaneously processes a still image and a video detecting and
localizing next-active objects, predicting the verb which describes the future
interaction and determining when the interaction will start. Experiments on the
large-scale egocentric dataset EGO4D show that our method outperformed
state-of-the-art approaches on the considered task. Our method is ranked first
in the public leaderboard of the EGO4D short term object interaction
anticipation challenge 2022. Please see the project web page for code and
additional details: https://iplab.dmi.unict.it/stillfast/
Loss of a sense of aliveness, bodily unhomeliness and radical estrangement: A phenomenological inquiry into service users’ experiences of psychiatric medication use in the treatment of early psychosis
Quantitative research drawing on the disease-centred model of psychiatric drug action dominates research on psychiatric medication, while little is known about service users’ subjective, embodied experiences of taking psychiatric medication. This research explored service users’ felt, embodied and relational experiences of psychiatric medication use in the
treatment of early psychosis using a multimodal, longitudinal research design. A more in-depth understanding of what it is like and what it means to take psychiatric medication from
service users’ idiographic perspectives is needed to improve the clinical care and support service users receive and better understand the treatment choices they make. Ten participants between the age of 18 and 30 years were recruited from London-based NHS Early Intervention in Psychosis services and participated in in-depth idiographic interviews. Eight participants took part in a follow-up interview between six and nine months later. Visual methods were used to explore the verbal as well as the pre-reflective, embodied aspects of participants’ medication experiences. The data was analysed using a combination of interpretative phenomenological analysis and framework analysis. While taking psychiatric medication, participants reported the loss of a sense of aliveness, feelings of radical estrangement from themselves, the world and other people and a sense of being suspended in a liminal, time-locked dimension in which they felt unable to transition from past
experiences of psychosis to future recovery. The findings of this study highlight the highly distressing and adverse iatrogenic effects of psychiatric medication use, including medication-induced coporealisation, disembodiment, estrangement and a loss of belonging. More holistic, human rights-based, recovery-oriented and body-centred ways of treating psychosis are needed
Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design
Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data
An Outlook into the Future of Egocentric Vision
What will the future be? We wonder! In this survey, we explore the gap
between current research in egocentric vision and the ever-anticipated future,
where wearable computing, with outward facing cameras and digital overlays, is
expected to be integrated in our every day lives. To understand this gap, the
article starts by envisaging the future through character-based stories,
showcasing through examples the limitations of current technology. We then
provide a mapping between this future and previously defined research tasks.
For each task, we survey its seminal works, current state-of-the-art
methodologies and available datasets, then reflect on shortcomings that limit
its applicability to future research. Note that this survey focuses on software
models for egocentric vision, independent of any specific hardware. The paper
concludes with recommendations for areas of immediate explorations so as to
unlock our path to the future always-on, personalised and life-enhancing
egocentric vision.Comment: We invite comments, suggestions and corrections here:
https://openreview.net/forum?id=V3974SUk1
Improving Outcomes for Shell and Shucking By-Products in Australian Abalone Fisheries – A Supply Chain Perspective
This research responds to global targets to halve food waste by 2030 and the Australian abalone industry’s need to maximise returns on catch by utilising shucking by-products. Both these exigencies are addressed by quantifying food waste and understanding its drivers. Analysis revealed that Australia’s wild-harvest abalone industry faces several barriers to recovering and valorising commercially-viable volumes of waste arising from heavily-regulated supply and vast geographical distances. Supply chain collaboration is necessary to overcome these challenges
Detecting Human-Object Contact in Images
Humans constantly contact objects to move and perform tasks. Thus, detecting human-object contact is important for building human-centered artificial intelligence. However, there exists no robust method to detect contact between the body and the scene from an image, and there exists no dataset to learn such a detector. We fill this gap with HOT ("Human-Object conTact"), a new dataset of human-object contacts for images. To build HOT, we use two data sources: (1) We use the PROX dataset of 3D human meshes moving in 3D scenes, and automatically annotate 2D image areas for contact via 3D mesh proximity and projection. (2) We use the V-COCO, HAKE and Watch-n-Patch datasets, and ask trained annotators to draw polygons for the 2D image areas where contact takes place. We also annotate the involved body part of the human body. We use our HOT dataset to train a new contact detector, which takes a single color image as input, and outputs 2D contact heatmaps as well as the body-part labels that are in contact. This is a new and challenging task that extends current foot-ground or hand-object contact detectors to the full generality of the whole body. The detector uses a part-attention branch to guide contact estimation through the context of the surrounding body parts and scene. We evaluate our detector extensively, and quantitative results show that our model outperforms baselines, and that all components contribute to better performance. Results on images from an online repository show reasonable detections and generalizability
Egocentric Action Understanding by Learning Embodied Attention
Videos captured from wearable cameras, known as egocentric videos, create a continuous record of human daily visual experience, and thereby offer a new perspective for human activity understanding. Importantly, egocentric video aligns gaze, embodied movement, and action in the same “first-person” coordinate system. The rich egocentric cues reflect the attended scene context of an action, and thereby provide novel means for reasoning human daily routines.
In my thesis work, I describe my efforts on developing novel computational models that learn the embodied egocentric attention for the automatic analysis of egocentric actions. First, I introduce a probabilistic model for learning gaze and actions in egocentric video and further demonstrate that attention can serve as a robust tool for learning motion-aware video representation. Second, I develop a novel deep model to address the challenging problem of jointly recognizing and localizing actions of a mobile user on a known 3D map from egocentric videos. Third, I present a novel deep latent variable model that makes use of human intentional body movement (motor attention) as a key representation for forecasting human-object interaction in egocentric video. Finally, I propose a novel task of future hand segmentation from egocentric videos, and show how explicitly modeling the future head motion can facilitate future hand movement forecasting.Ph.D
Design and Effect of Continuous Wearable Tactile Displays
Our sense of touch is one of our core senses and while not as information rich as sight and hearing, it tethers us to reality.
Our skin is the largest sensory organ in our body and we rely on it so much that we don\u27t think about it most of the time.
Tactile displays - with the exception of actuators for notifications on smartphones and smartwatches - are currently understudied and underused.
Currently tactile cues are mostly used in smartphones and smartwatches to notify the user of an incoming call or text message.
Specifically continuous displays - displays that do not just send one notification but stay active for an extended period of time and continuously communicate information - are rarely studied.
This thesis aims at exploring the utilization of our vibration perception to create continuous tactile displays.
Transmitting a continuous stream of tactile information to a user in a wearable format can help elevate tactile displays from being mostly used for notifications to becoming more like additional senses enabling us to perceive our environment in new ways.
This work provides a serious step forward in design, effect and use of continuous tactile displays and their use in human-computer interaction.
The main contributions include:
Exploration of Continuous Wearable Tactile Interfaces
This thesis explores continuous tactile displays in different contexts and with different types of tactile information systems. The use-cases were explored in various domains for tactile displays - Sports, Gaming and Business applications. The different types of continuous tactile displays feature one- or multidimensional tactile patterns, temporal patterns and discrete tactile patterns.
Automatic Generation of Personalized Vibration Patterns
In this thesis a novel approach of designing vibrotactile patterns without expert knowledge by leveraging evolutionary algorithms to create personalized vibration patterns - is described. This thesis presents the design of an evolutionary algorithm with a human centered design generating abstract vibration patterns. The evolutionary algorithm was tested in a user study which offered evidence that interactive generation of abstract vibration patterns is possible and generates diverse sets of vibration patterns that can be recognized with high accuracy.
Passive Haptic Learning for Vibration Patterns
Previous studies in passive haptic learning have shown surprisingly strong results for learning Morse Code. If these findings could be confirmed and generalized, it would mean that learning a new tactile alphabet could be made easier and learned in passing. Therefore this claim was investigated in this thesis and needed to be corrected and contextualized. A user study was conducted to study the effects of the interaction design and distraction tasks on the capability to learn stimulus-stimulus-associations with Passive Haptic Learning. This thesis presents evidence that Passive Haptic Learning of vibration patterns induces only a marginal learning effect and is not a feasible and efficient way to learn vibration patterns that include more than two vibrations.
Influence of Reference Frames for Spatial Tactile Stimuli
Designing wearable tactile stimuli that contain spatial information can be a challenge due to the natural body movement of the wearer. An important consideration therefore is what reference frame to use for spatial cues. This thesis investigated allocentric versus egocentric reference frames on the wrist and compared them for induced cognitive load, reaction time and accuracy in a user study. This thesis presents evidence that using an allocentric reference frame drastically lowers cognitive load and slightly lowers reaction time while keeping the same accuracy as an egocentric reference frame, making a strong case for the utilization of allocentric reference frames in tactile bracelets with several tactile actuators
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