21 research outputs found

    Imagining & Sensing: Understanding and Extending the Vocalist-Voice Relationship Through Biosignal Feedback

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    The voice is body and instrument. Third-person interpretation of the voice by listeners, vocal teachers, and digital agents is centred largely around audio feedback. For a vocalist, physical feedback from within the body provides an additional interaction. The vocalist’s understanding of their multi-sensory experiences is through tacit knowledge of the body. This knowledge is difficult to articulate, yet awareness and control of the body are innate. In the ever-increasing emergence of technology which quantifies or interprets physiological processes, we must remain conscious also of embodiment and human perception of these processes. Focusing on the vocalist-voice relationship, this thesis expands knowledge of human interaction and how technology influences our perception of our bodies. To unite these different perspectives in the vocal context, I draw on mixed methods from cog- nitive science, psychology, music information retrieval, and interactive system design. Objective methods such as vocal audio analysis provide a third-person observation. Subjective practices such as micro-phenomenology capture the experiential, first-person perspectives of the vocalists them- selves. Quantitative-qualitative blend provides details not only on novel interaction, but also an understanding of how technology influences existing understanding of the body. I worked with vocalists to understand how they use their voice through abstract representations, use mental imagery to adapt to altered auditory feedback, and teach fundamental practice to others. Vocalists use multi-modal imagery, for instance understanding physical sensations through auditory sensations. The understanding of the voice exists in a pre-linguistic representation which draws on embodied knowledge and lived experience from outside contexts. I developed a novel vocal interaction method which uses measurement of laryngeal muscular activations through surface electromyography. Biofeedback was presented to vocalists through soni- fication. Acting as an indicator of vocal activity for both conscious and unconscious gestures, this feedback allowed vocalists to explore their movement through sound. This formed new perceptions but also questioned existing understanding of the body. The thesis also uncovers ways in which vocalists are in control and controlled by, work with and against their bodies, and feel as a single entity at times and totally separate entities at others. I conclude this thesis by demonstrating a nuanced account of human interaction and perception of the body through vocal practice, as an example of how technological intervention enables exploration and influence over embodied understanding. This further highlights the need for understanding of the human experience in embodied interaction, rather than solely on digital interpretation, when introducing technology into these relationships

    Deep Multi Temporal Scale Networks for Human Motion Analysis

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    The movement of human beings appears to respond to a complex motor system that contains signals at different hierarchical levels. For example, an action such as ``grasping a glass on a table'' represents a high-level action, but to perform this task, the body needs several motor inputs that include the activation of different joints of the body (shoulder, arm, hand, fingers, etc.). Each of these different joints/muscles have a different size, responsiveness, and precision with a complex non-linearly stratified temporal dimension where every muscle has its temporal scale. Parts such as the fingers responds much faster to brain input than more voluminous body parts such as the shoulder. The cooperation we have when we perform an action produces smooth, effective, and expressive movement in a complex multiple temporal scale cognitive task. Following this layered structure, the human body can be described as a kinematic tree, consisting of joints connected. Although it is nowadays well known that human movement and its perception are characterised by multiple temporal scales, very few works in the literature are focused on studying this particular property. In this thesis, we will focus on the analysis of human movement using data-driven techniques. In particular, we will focus on the non-verbal aspects of human movement, with an emphasis on full-body movements. The data-driven methods can interpret the information in the data by searching for rules, associations or patterns that can represent the relationships between input (e.g. the human action acquired with sensors) and output (e.g. the type of action performed). Furthermore, these models may represent a new research frontier as they can analyse large masses of data and focus on aspects that even an expert user might miss. The literature on data-driven models proposes two families of methods that can process time series and human movement. The first family, called shallow models, extract features from the time series that can help the learning algorithm find associations in the data. These features are identified and designed by domain experts who can identify the best ones for the problem faced. On the other hand, the second family avoids this phase of extraction by the human expert since the models themselves can identify the best set of features to optimise the learning of the model. In this thesis, we will provide a method that can apply the multi-temporal scales property of the human motion domain to deep learning models, the only data-driven models that can be extended to handle this property. We will ask ourselves two questions: what happens if we apply knowledge about how human movements are performed to deep learning models? Can this knowledge improve current automatic recognition standards? In order to prove the validity of our study, we collected data and tested our hypothesis in specially designed experiments. Results support both the proposal and the need for the use of deep multi-scale models as a tool to better understand human movement and its multiple time-scale nature

    The "Federica" hand: a simple, very efficient prothesis

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    Hand prostheses partially restore hand appearance and functionalities. Not everyone can afford expensive prostheses and many low-cost prostheses have been proposed. In particular, 3D printers have provided great opportunities by simplifying the manufacturing process and reducing costs. Generally, active prostheses use multiple motors for fingers movement and are controlled by electromyographic (EMG) signals. The "Federica" hand is a single motor prosthesis, equipped with an adaptive grasp and controlled by a force-myographic signal. The "Federica" hand is 3D printed and has an anthropomorphic morphology with five fingers, each consisting of three phalanges. The movement generated by a single servomotor is transmitted to the fingers by inextensible tendons that form a closed chain; practically, no springs are used for passive hand opening. A differential mechanical system simultaneously distributes the motor force in predefined portions on each finger, regardless of their actual positions. Proportional control of hand closure is achieved by measuring the contraction of residual limb muscles by means of a force sensor, replacing the EMG. The electrical current of the servomotor is monitored to provide the user with a sensory feedback of the grip force, through a small vibration motor. A simple Arduino board was adopted as processing unit. The differential mechanism guarantees an efficient transfer of mechanical energy from the motor to the fingers and a secure grasp of any object, regardless of its shape and deformability. The force sensor, being extremely thin, can be easily embedded into the prosthesis socket and positioned on both muscles and tendons; it offers some advantages over the EMG as it does not require any electrical contact or signal processing to extract information about the muscle contraction intensity. The grip speed is high enough to allow the user to grab objects on the fly: from the muscle trigger until to the complete hand closure, "Federica" takes about half a second. The cost of the device is about 100 US$. Preliminary tests carried out on a patient with transcarpal amputation, showed high performances in controlling the prosthesis, after a very rapid training session. The "Federica" hand turned out to be a lightweight, low-cost and extremely efficient prosthesis. The project is intended to be open-source: all the information needed to produce the prosthesis (e.g. CAD files, circuit schematics, software) can be downloaded from a public repository. Thus, allowing everyone to use the "Federica" hand and customize or improve it

    Proficiency-aware systems

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    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

    Applications of EMG in Clinical and Sports Medicine

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    This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

    Surface Electromyography for Direct Vocal Control

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    This paper introduces a new method for direct control using the voice via measurement of vocal muscular activation with surface electromyography (sEMG). Digital musical interfaces based on the voice have typically used indirect control, in which features extracted from audio signals control the parameters of sound generation, for example in audio to MIDI controllers. By contrast, focusing on the musculature of the singing voice allows direct muscular control, or alternatively, combined direct and indirect control in an augmented vocal instrument. In this way we aim to both preserve the intimate relationship a vocalist has with their instrument and key timbral and stylistic characteristics of the voice while expanding its sonic capabilities. This paper discusses other digital instruments which effectively utilise a combination of indirect and direct control as well as a history of controllers involving the voice. Subsequently, a new method of direct control from physiological aspects of singing through sEMG and its capabilities are discussed. Future developments of the system are further outlined along with usage in performance studies, interactive live vocal performance, and educational and practice tools

    A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

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    The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations

    EAVI EMG board

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    [First paragraph] Electromyography (EMG) has been widely adopted to build new interfaces for musical expression by the community [10,4]. Muscular activity is inherently noisy, making EMG signals potentially difficult to map to audio parameters, and work with when designing interactions with audiovisual systems. For decades, musicians and technologists have explored different solutions – from costly medical devices to do-it-yourself (DIY)packages – to find reliable hardware for capturing the best EMG signal in order to facilitate the music and instrument making process.</p
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