ModDrop: adaptive multi-modal gesture recognition

We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning. Each visual modality captures spatial information at a particular spatial scale (such as motion of the upper body or a hand), and the whole system operates at three temporal scales. Key to our technique is a training strategy which exploits: i) careful initialization of individual modalities; and ii) gradual fusion involving random dropping of separate channels (dubbed ModDrop) for learning cross-modality correlations while preserving uniqueness of each modality-specific representation. We present experiments on the ChaLearn 2014 Looking at People Challenge gesture recognition track, in which we placed first out of 17 teams. Fusing multiple modalities at several spatial and temporal scales leads to a significant increase in recognition rates, allowing the model to compensate for errors of the individual classifiers as well as noise in the separate channels. Futhermore, the proposed ModDrop training technique ensures robustness of the classifier to missing signals in one or several channels to produce meaningful predictions from any number of available modalities. In addition, we demonstrate the applicability of the proposed fusion scheme to modalities of arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure

Deep Learning for Head Pose Estimation: A Survey

Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic

Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems

Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300 GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including security sensing, industrial packaging, medical imaging, and non-destructive testing. Traditional methods for perception and imaging are challenged by novel data-driven algorithms that offer improved resolution, localization, and detection rates. Over the past decade, deep learning technology has garnered substantial popularity, particularly in perception and computer vision applications. Whereas conventional signal processing techniques are more easily generalized to various applications, hybrid approaches where signal processing and learning-based algorithms are interleaved pose a promising compromise between performance and generalizability. Furthermore, such hybrid algorithms improve model training by leveraging the known characteristics of radio frequency (RF) waveforms, thus yielding more efficiently trained deep learning algorithms and offering higher performance than conventional methods. This dissertation introduces novel hybrid-learning algorithms for improved mmWave imaging systems applicable to a host of problems in perception and sensing. Various problem spaces are explored, including static and dynamic gesture classification; precise hand localization for human computer interaction; high-resolution near-field mmWave imaging using forward synthetic aperture radar (SAR); SAR under irregular scanning geometries; mmWave image super-resolution using deep neural network (DNN) and Vision Transformer (ViT) architectures; and data-level multiband radar fusion using a novel hybrid-learning architecture. Furthermore, we introduce several novel approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen

Proficiency-aware systems

In an increasingly digital world, technological developments such as data-driven algorithms and context-aware applications create opportunities for novel human-computer interaction (HCI). We argue that these systems have the latent potential to stimulate users and encourage personal growth. However, users increasingly rely on the intelligence of interactive systems. Thus, it remains a challenge to design for proficiency awareness, essentially demanding increased user attention whilst preserving user engagement. Designing and implementing systems that allow users to become aware of their own proficiency and encourage them to recognize learning benefits is the primary goal of this research. In this thesis, we introduce the concept of proficiency-aware systems as one solution. In our definition, proficiency-aware systems use estimates of the user's proficiency to tailor the interaction in a domain and facilitate a reflective understanding for this proficiency. We envision that proficiency-aware systems leverage collected data for learning benefit. Here, we see self-reflection as a key for users to become aware of necessary efforts to advance their proficiency. A key challenge for proficiency-aware systems is the fact that users often have a different self-perception of their proficiency. The benefits of personal growth and advancing one's repertoire might not necessarily be apparent to users, alienating them, and possibly leading to abandoning the system. To tackle this challenge, this work does not rely on learning strategies but rather focuses on the capabilities of interactive systems to provide users with the necessary means to reflect on their proficiency, such as showing calculated text difficulty to a newspaper editor or visualizing muscle activity to a passionate sportsperson. We first elaborate on how proficiency can be detected and quantified in the context of interactive systems using physiological sensing technologies. Through developing interaction scenarios, we demonstrate the feasibility of gaze- and electromyography-based proficiency-aware systems by utilizing machine learning algorithms that can estimate users' proficiency levels for stationary vision-dominant tasks (reading, information intake) and dynamic manual tasks (playing instruments, fitness exercises). Secondly, we show how to facilitate proficiency awareness for users, including design challenges on when and how to communicate proficiency. We complement this second part by highlighting the necessity of toolkits for sensing modalities to enable the implementation of proficiency-aware systems for a wide audience. In this thesis, we contribute a definition of proficiency-aware systems, which we illustrate by designing and implementing interactive systems. We derive technical requirements for real-time, objective proficiency assessment and identify design qualities of communicating proficiency through user reflection. We summarize our findings in a set of design and engineering guidelines for proficiency awareness in interactive systems, highlighting that proficiency feedback makes performance interpretable for the user.In einer zunehmend digitalen Welt schaffen technologische Entwicklungen - wie datengesteuerte Algorithmen und kontextabhängige Anwendungen - neuartige Interaktionsmöglichkeiten mit digitalen Geräten. Jedoch verlassen sich Nutzer oftmals auf die Intelligenz dieser Systeme, ohne dabei selbst auf eine persönliche Weiterentwicklung hinzuwirken. Wird ein solches Vorgehen angestrebt, verlangt dies seitens der Anwender eine erhöhte Aufmerksamkeit. Es ist daher herausfordernd, ein entsprechendes Design für Kompetenzbewusstsein (Proficiency Awareness) zu etablieren. Das primäre Ziel dieser Arbeit ist es, eine Methodik für das Design und die Implementierung von interaktiven Systemen aufzustellen, die Nutzer dabei unterstützen über ihre eigene Kompetenz zu reflektieren, um dadurch Lerneffekte implizit wahrnehmen können. Diese Arbeit stellt ein Konzept für fähigkeitsbewusste Systeme (proficiency-aware systems) vor, welche die Fähigkeiten von Nutzern abschätzen, die Interaktion entsprechend anpassen sowie das Bewusstsein der Nutzer über deren Fähigkeiten fördern. Hierzu sollten die Systeme gesammelte Daten von Nutzern einsetzen, um Lerneffekte sichtbar zu machen. Die Möglichkeit der Anwender zur Selbstreflexion ist hierbei als entscheidend anzusehen, um als Motivation zur Verbesserung der eigenen Fähigkeiten zu dienen. Eine zentrale Herausforderung solcher Systeme ist die Tatsache, dass Nutzer - im Vergleich zur Abschätzung des Systems - oft eine divergierende Selbstwahrnehmung ihrer Kompetenz haben. Im ersten Moment sind daher die Vorteile einer persönlichen Weiterentwicklung nicht unbedingt ersichtlich. Daher baut diese Forschungsarbeit nicht darauf auf, Nutzer über vorgegebene Lernstrategien zu unterrichten, sondern sie bedient sich der Möglichkeiten interaktiver Systeme, die Anwendern die notwendigen Hilfsmittel zur Verfügung stellen, damit diese selbst über ihre Fähigkeiten reflektieren können. Einem Zeitungseditor könnte beispielsweise die aktuelle Textschwierigkeit angezeigt werden, während einem passionierten Sportler dessen Muskelaktivität veranschaulicht wird. Zunächst wird herausgearbeitet, wie sich die Fähigkeiten der Nutzer mittels physiologischer Sensortechnologien erkennen und quantifizieren lassen. Die Evaluation von Interaktionsszenarien demonstriert die Umsetzbarkeit fähigkeitsbewusster Systeme, basierend auf der Analyse von Blickbewegungen und Muskelaktivität. Hierbei kommen Algorithmen des maschinellen Lernens zum Einsatz, die das Leistungsniveau der Anwender für verschiedene Tätigkeiten berechnen. Im Besonderen analysieren wir stationäre Aktivitäten, die hauptsächlich den Sehsinn ansprechen (Lesen, Aufnahme von Informationen), sowie dynamische Betätigungen, die die Motorik der Nutzer fordern (Spielen von Instrumenten, Fitnessübungen). Der zweite Teil zeigt auf, wie Systeme das Bewusstsein der Anwender für deren eigene Fähigkeiten fördern können, einschließlich der Designherausforderungen , wann und wie das System erkannte Fähigkeiten kommunizieren sollte. Abschließend wird die Notwendigkeit von Toolkits für Sensortechnologien hervorgehoben, um die Implementierung derartiger Systeme für ein breites Publikum zu ermöglichen. Die Forschungsarbeit beinhaltet eine Definition für fähigkeitsbewusste Systeme und veranschaulicht dieses Konzept durch den Entwurf und die Implementierung interaktiver Systeme. Ferner werden technische Anforderungen objektiver Echtzeitabschätzung von Nutzerfähigkeiten erforscht und Designqualitäten für die Kommunikation dieser Abschätzungen mittels Selbstreflexion identifiziert. Zusammengefasst sind die Erkenntnisse in einer Reihe von Design- und Entwicklungsrichtlinien für derartige Systeme. Insbesondere die Kommunikation, der vom System erkannten Kompetenz, hilft Anwendern, die eigene Leistung zu interpretieren

Tailoring Interaction. Sensing Social Signals with Textiles.

Nonverbal behaviour is an important part of conversation and can reveal much about the nature of an interaction. It includes phenomena ranging from large-scale posture shifts to small scale nods. Capturing these often spontaneous phenomena requires unobtrusive sensing techniques that do not interfere with the interaction. We propose an underexploited sensing modality for sensing nonverbal behaviours: textiles. As a material in close contact with the body, they provide ubiquitous, large surfaces that make them a suitable soft interface. Although the literature on nonverbal communication focuses on upper body movements such as gestures, observations of multi-party, seated conversations suggest that sitting postures, leg and foot movements are also systematically related to patterns of social interaction. This thesis addressees the following questions: Can the textiles surrounding us measure social engagement? Can they tell who is speaking, and who, if anyone, is listening? Furthermore, how should wearable textile sensing systems be designed and what behavioural signals could textiles reveal? To address these questions, we have designed and manufactured bespoke chairs and trousers with integrated textile pressure sensors, that are introduced here. The designs are evaluated in three user studies that produce multi-modal datasets for the exploration of fine-grained interactional signals. Two approaches to using these bespoke textile sensors are explored. First, hand crafted sensor patches in chair covers serve to distinguish speakers and listeners. Second, a pressure sensitive matrix in custom-made smart trousers is developed to detect static sitting postures, dynamic bodily movement, as well as basic conversational states. Statistical analyses, machine learning approaches, and ethnographic methods show that by moni- toring patterns of pressure change alone it is possible to not only classify postures with high accuracy, but also to identify a wide range of behaviours reliably in individuals and groups. These findings es- tablish textiles as a novel, wearable sensing system for applications in social sciences, and contribute towards a better understanding of nonverbal communication, especially the significance of posture shifts when seated. If chairs know who is speaking, if our trousers can capture our social engagement, what role can smart textiles have in the future of human interaction? How can we build new ways to map social ecologies and tailor interactions

Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration.

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs' facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network's attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications