In-Place Gestures Classification via Long-term Memory Augmented Network

In-place gesture-based virtual locomotion techniques enable users to control their viewpoint and intuitively move in the 3D virtual environment. A key research problem is to accurately and quickly recognize in-place gestures, since they can trigger specific movements of virtual viewpoints and enhance user experience. However, to achieve real-time experience, only short-term sensor sequence data (up to about 300ms, 6 to 10 frames) can be taken as input, which actually affects the classification performance due to limited spatio-temporal information. In this paper, we propose a novel long-term memory augmented network for in-place gestures classification. It takes as input both short-term gesture sequence samples and their corresponding long-term sequence samples that provide extra relevant spatio-temporal information in the training phase. We store long-term sequence features with an external memory queue. In addition, we design a memory augmented loss to help cluster features of the same class and push apart features from different classes, thus enabling our memory queue to memorize more relevant long-term sequence features. In the inference phase, we input only short-term sequence samples to recall the stored features accordingly, and fuse them together to predict the gesture class. We create a large-scale in-place gestures dataset from 25 participants with 11 gestures. Our method achieves a promising accuracy of 95.1% with a latency of 192ms, and an accuracy of 97.3% with a latency of 312ms, and is demonstrated to be superior to recent in-place gesture classification techniques. User study also validates our approach. Our source code and dataset will be made available to the community.Comment: This paper is accepted to IEEE ISMAR202

Combining motion matching and orientation prediction to animate avatars for consumer-grade VR devices

The animation of user avatars plays a crucial role in conveying their pose, gestures, and relative distances to virtual objects or other users. Self-avatar animation in immersive VR helps improve the user experience and provides a Sense of Embodiment. However, consumer-grade VR devices typically include at most three trackers, one at the Head Mounted Display (HMD), and two at the handheld VR controllers. Since the problem of reconstructing the user pose from such sparse data is ill-defined, especially for the lower body, the approach adopted by most VR games consists of assuming the body orientation matches that of the HMD, and applying animation blending and time-warping from a reduced set of animations. Unfortunately, this approach produces noticeable mismatches between user and avatar movements. In this work we present a new approach to animate user avatars that is suitable for current mainstream VR devices. First, we use a neural network to estimate the user's body orientation based on the tracking information from the HMD and the hand controllers. Then we use this orientation together with the velocity and rotation of the HMD to build a feature vector that feeds a Motion Matching algorithm. We built a MoCap database with animations of VR users wearing a HMD and used it to test our approach on both self-avatars and other users’ avatars. Our results show that our system can provide a large variety of lower body animations while correctly matching the user orientation, which in turn allows us to represent not only forward movements but also stepping in any direction.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 860768 (CLIPE project) and the Spanish Ministry of Science and Innovation (PID2021-122136OB-C21).Peer ReviewedPostprint (published version

Landscapes of Helping: Kindliness in Neighbourhoods and Communities

Increasing geographical mobility, economic change and the rise of an individualist culture in the UK have contributed to the loosening of close ties in communities. Communities need to evolve, to reconnect, so that people cultivate the ‘background hum’ of sociability that has been associated with neighbourliness. This ‘background hum’ is characterised by people’s awareness of each other, by a respect for each other’s privacy and by a readiness to take action if help is needed. In this research we define kindliness as ‘neighbourliness enacted’ and describe the process of reconnection within communities as the ‘reinvention of sociality’. Hebden Bridge’s relative success in melding traditional and more contemporary forms of sociality helps to identify some broader lessons about fostering kindliness in neighbourhoods and communities

The Rise of iWar: Identity, Information, and the Individualization of Modern Warfare

During a decade of global counterterrorism operations and two extended counterinsurgency campaigns, the United States was confronted with a new kind of adversary. Without uniforms, flags, and formations, the task of identifying and targeting these combatants represented an unprecedented operational challenge for which Cold War era doctrinal methods were largely unsuited. This monograph examines the doctrinal, technical, and bureaucratic innovations that evolved in response to these new operational challenges. It discusses the transition from a conventionally focused, Cold War-era targeting process to one optimized for combating networks and conducting identity-based targeting. It analyzes the policy decisions and strategic choices that were the catalysts of this change and concludes with an in depth examination of emerging technologies that are likely to shape how this mode of warfare will be waged in the future.https://press.armywarcollege.edu/monographs/1436/thumbnail.jp

Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation

Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few patients (i.e., limited training data). Treatments for SCIs have natural groupings, so we propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding technique. We then use Fitted Q Iteration to train an agent that learns optimal treatments. Through a simulation study designed to reflect the properties of SCI rehabilitation, we find that both methods can help improve the treatment decisions of physiotherapists, but the approach based on domain knowledge offers better performance

Designing wearables for use in the workplace: the role of solution developers

Wearables (such as data glasses and smartwatches) are a particularly visible element of Industrie 4.0 applications. They aim at providing situation-specific information to workers, but at the same time they can also be used for surveillance and control because they generate data on the work process and sometimes even on movement patterns and vital data of the employees. Wearables technology is at an early stage of development, in which the interests and perspectives of relevant stakeholders, especially technology developers and the management, are of particular importance. This article explores the role of solution developers and their understanding of work processes in which wearables are to be used. It is based on expert interviews with solution developers, academic and company experts. The analysis shows an ambivalent understanding of work: On the one hand, it is characterized by the perception of workers as potential sources of error. It focuses on the optimization of individual workplaces and their ergonomics, while broader questions of work design and work organization are ignored. On the other hand, the technology developers see and discuss the potentials and dangers of wearables technologies with regard to individualization, data protection and control in a differentiated manner

Integrating passive ubiquitous surfaces into human-computer interaction

Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll

Moving on? Experiences of social mobility in a mixed-class North London neighbourhood

This qualitative study investigates subjective experiences of social mobility amongst parents whose children attend the same London state primary school, at a historical moment when the Conservative-led Coalition government claims social mobility as the principal goal of its social policies. I argue that the government’s understanding of social mobility is founded on a neoliberal discourse that holds individuals responsible for their own life trajectories. This individualist view aligns with individualization theory’s emphasises on reflexive selves, understood as disembedded from class groups. By examining how participants’ experiences are shaped by class processes I interrogate this dominant perspective, and consider alternative conceptions of social mobilities that expand the existing discourse. I take a case-study approach that utilises a range of qualitative methods, enabling crossclass comparisons as well as examining parents’ intersectional identities. I draw embodied and emotional geographies into the analysis, including everyday distinctionmaking and face-to-face interactions. I relate subjective experiences to class structures across a range of social fields, inter-weaving material and cultural analyses to examine the impacts of economic and political processes on lived experiences. The thesis demonstrates how class processes significantly impact on social mobility experiences, and thus argues that the individualist social mobility discourse is flawed. However, whilst the individualist model denies the role of class structures, I argue that it constructs class identities by attaching stigma and status to individuals, who are held responsible for their own social trajectories. This narrative is implicated in processes of dominance and hegemony, and works to justify the current welfare cuts. I also argue, however, that by attending to participants’ experiences and using a class analysis it is possible to reframe social mobility within an equality agenda based on the redistribution of resources. This study therefore makes a significant academic contribution because it expands the understanding of how class impacts on social mobility experiences, it explicitly addresses the individualist discourse of social mobility, and it suggests an alternative more equitable model

From a Tweet to the Street: The Effect of Social Media on Social Movement Theory

This thesis studies the role of social media within social movements and social movement theory and focuses on applying these theories to the Ni Una Menos movement of Argentina. I focus on three social movement theories: the resource mobilization theory, the political process theory, and the cultural approach. I also analyze the rise of social media in the 21st century and how they fit within the framework of the three theories. I then apply these theories to the Ni Una Menos movement. I argue that the Ni Una Menos movement emerged as a cultural movement, and shifted towards a resource mobilization movement and has only recently begun to fit the political process theory due to new laws and governmental plans. Social media is the main resource for this movement and is responsible for the formation and success of Ni Una Menos