3 research outputs found

    Activity Recognition With Machine Learning in Manual Grinding

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    Activity, context, and plan recognition with computational causal behavior models

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    Objective of this thesis is to answer the question "how to achieve efficient sensor-based reconstruction of causal structures of human behaviour in order to provide assistance?". To answer this question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. Furthermore, a novel approximative inference algorithm – the Marginal Filter – is introduced

    Computer-supported movement guidance: investigating visual/visuotactile guidance and informing the design of vibrotactile body-worn interfaces

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    This dissertation explores the use of interactive systems to support movement guidance, with applications in various fields such as sports, dance, physiotherapy, and immersive sketching. The research focuses on visual, haptic, and visuohaptic approaches and aims to overcome the limitations of traditional guidance methods, such as dependence on an expert and high costs for the novice. The main contributions of the thesis are (1) an evaluation of the suitability of various types of displays and visualizations of the human body for posture guidance, (2) an investigation into the influence of different viewpoints/perspectives, the addition of haptic feedback, and various movement properties on movement guidance in virtual environments, (3) an investigation into the effectiveness of visuotactile guidance for hand movements in a virtual environment, (4) two in-depth studies of haptic perception on the body to inform the design of wearable and handheld interfaces that leverage tactile output technologies, and (5) an investigation into new interaction techniques for tactile guidance of arm movements. The results of this research advance the state of the art in the field, provide design and implementation insights, and pave the way for new investigations in computer-supported movement guidance
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