Humanizing robot dance movements

Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 201

Tri-Modal Motion Retrieval by Learning a Joint Embedding Space

Information retrieval is an ever-evolving and crucial research domain. The substantial demand for high-quality human motion data especially in online acquirement has led to a surge in human motion research works. Prior works have mainly concentrated on dual-modality learning, such as text and motion tasks, but three-modality learning has been rarely explored. Intuitively, an extra introduced modality can enrich a model's application scenario, and more importantly, an adequate choice of the extra modality can also act as an intermediary and enhance the alignment between the other two disparate modalities. In this work, we introduce LAVIMO (LAnguage-VIdeo-MOtion alignment), a novel framework for three-modality learning integrating human-centric videos as an additional modality, thereby effectively bridging the gap between text and motion. Moreover, our approach leverages a specially designed attention mechanism to foster enhanced alignment and synergistic effects among text, video, and motion modalities. Empirically, our results on the HumanML3D and KIT-ML datasets show that LAVIMO achieves state-of-the-art performance in various motion-related cross-modal retrieval tasks, including text-to-motion, motion-to-text, video-to-motion and motion-to-video

Sonification of Samba dance using periodic pattern analysis

In this study we focus on the sonification of Samba dance, using a multi-modal analysis-by-synthesis approach. In the analysis we use periodic pattern analysis to decompose the Samba dance movements into basic movement gestures along the music’s metric layers. In the synthesis we start from the basic movement gestures and extract peaks and valleys, which we use as basic material for the sonification. This leads to a matrix of repetitive dance gestures from which we select the proper cues that trigger samples of a Samba ensemble. The straightforward sonification procedure suggests that Samba rhythms may be mirrored in choreographic forms or vice-versa

Dance Sport Movement Therapy in the Metaverse: a new frontier for alternative mental health therapies

This paper delves into the innovative integration of Dance Movement Therapy (DMT) within extended reality (XR) environments, exploring its potential as a non- pharmacological intervention for mental health conditions. The study employed a blend of qualitative evidence synthesis and meta-analyses of primary quantitative data, focusing on the therapeutic implications of dance in virtual, augmented, and mixed realities. Utilising wearables and sensors, real-time data on participants' movements, physiological responses, and emotional feedback were collected and analysed using AI/ML algorithms, including Random Forest, SVM, CNNs, and RNNs. The research highlighted the importance of data privacy and ethical considerations, emphasising the need for securely storing metadata to ensure user trust and legal compliance. Findings underscored the potential of XR environments like the Metaverse in transforming mental health practices, offering efficient, engaging, and effective therapeutic interventions. The study also introduced the novel concept of Physical Intensity Matching and the significance of personalised exercise selection. Despite its ground-breaking insights, the research acknowledged potential biases introduced by wearables and the challenges of ensuring data accuracy. This paper is a foundational exploration into the convergence of DMT, XR, and AI, paving the way for future interdisciplinary research in mental health and technology

Research Report - October 2009. Elaboration of the Module: Definition of the Programme

Alternative innovative didactic methodology is needed to reduce premature school drop out,particularly of young people at risk of exclusion, such as migrants, ethnic groups and children/teenagers from difficult socio-economic background). The key point is to modify the way to deliver learning. Cultural enrichment through young interestas such as music and art, use of technologies, social competencies, problem-solving skills incomputer science, autonomy and sense of purpose may help childhood and adolescence to achieve an improved engagement in school and a sense of educational accomplishmen

Dance sport movement therapy in the metaverse: A new frontier for alternative mental health therapies

This paper investigates the integration of Dance Movement Therapy (DMT) within extended reality (XR) environments, exploring its potential as a non-pharmacological intervention for mental health conditions. The study employed a blend of qualitative evidence synthesis and analyses of primary quantitative data, focusing on the therapeutic implications of Dance in virtual, augmented and mixed realities. Data from wearables and sensors on movements, physiological responses and emotional feedback are analysed using AI/ML algorithms, including Random Forest, SVM, CNNs and RNNs. The research highlighted the importance of data privacy and ethical considerations, emphasising the need for securely storing metadata to ensure user trust and legal compliance. Findings underscored the potential of XR environments like the Metaverse in transforming mental health practices, offering efficient, engaging and effective therapeutic interventions. The study also introduced the novel concept of Physical Intensity Matching and the significance of personalised exercise selection. Despite its ground-breaking insights, the research acknowledged potential biases introduced by wearables and the challenges of ensuring data accuracy. This paper is a foundational exploration into the convergence of DMT, XR and AI, paving the way for future interdisciplinary research in mental health and technology