Mova

Alemi, Omid; Pasquier, Philippe; Shaw, Chris

doi:10.1145/2617995.2618002

Cited by 20 publications

(5 citation statements)

References 12 publications

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“…Recent advancements in application interfaces for human movement analysis include OpenCap [16], which performs motion analysis through synchronized smartphone videos; PhysioU's Gait Movement Analysis Application, offering insights into walking patterns and gait analysis for various health conditions [4]; Kemtai, leveraging computer vision to transform any camera into a personal exercise guide [2]; and the Mova platform, providing a suite of tools for comprehensive movement visualization and analysis [1]. Although these platforms offer improved movement visualization and fundamental analysis, they do not feature the approaches such as explainable AI for motion analysis and data augmentation capabilities highlighted in the proposed web-based platform.…”

Section: Related Workmentioning

confidence: 99%

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Interactive Visualization and Dexterity Analysis of Human Movement: AIMove Platform

Olivas-Padilla,

Manitsaris,

Glushkova

2024

2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)

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This paper introduces an innovative web-based platform for visualizing, analyzing, and generating human movement, leveraging explainable artificial intelligence (AI). It addresses the challenge of limited data by offering one-shot and data-intensive training approaches, facilitates data augmentation, and provides insights into human dexterity through user interaction with the learned movement models. The paper outlines the prototype's implementation, highlighting its tools for analyzing and visualizing complex movements, and discusses its application in analyzing professional movements within the heritage crafts and manufacturing industry. Future work aims to expand its motion capture technology compatibility and improve analytical functionalities. This enhancement seeks to extend the platform's reach, making it a valuable tool for researchers and professionals across various disciplines.

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Section: Related Workmentioning

confidence: 99%

“…H3 or H4 (1) In this equation, the motion representation is characterized by time-varying coefficients A t ∈ R 1×2 and B t ∈ R 1×N , but they can also be constant A ∈ R 1×2 and B ∈ R 1×N . Examining these learned coefficients enables the assessment of their significance in the modeling and generation.…”

Section: A Dexterity Analysismentioning

confidence: 99%

Interactive Visualization and Dexterity Analysis of Human Movement: AIMove Platform

Olivas-Padilla,

Manitsaris,

Glushkova

2024

2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)

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“…In sports, Stein et al [53] integrated movement data including possible player movement directly with the original video, "enabling analysts to draw on the advantages of both". Mova [1] used small multiples of extracted body keypoints that were annotated with color to indicate features of movement. A design prototype we considered included these enhanced keypoints.…”

Section: Human Physical Behavior Data Visualizationmentioning

confidence: 99%

SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness

Huang,

He,

Maher

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement.

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“…Through the web front-end, researchers can both access and upload movement data. Each file or group of files can also be viewed in the accompanying "MoVa" movement visualizer [1]. MoDa provides programmatic access through the use of a standardized RESTful API that allows communication using HTTP message passing.…”

Section: Integration Of the Movement Database "Moda"mentioning

confidence: 99%

m+m

Bernardet

Adhia²,

Jaffe³

et al. 2016

Proceedings of the 3rd International Symposium on Movement and Computing

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Embodied interaction has the potential to provide users with uniquely engaging and meaningful experiences. m+m: Movement + Meaning middleware is an open source software framework that enables users to construct real-time, interactive systems that are based on movement data. The acquisition, processing, and rendering of movement data can be local or distributed, real-time or off-line. Key features of the m+m middleware are a small footprint in terms of computational resources, portability between different platforms, and high performance in terms of reduced latency and increased bandwidth. Examples of systems that can be built with m+m as the internal communication middleware include those for the semantic interpretation of human movement data, machine-learning models for movement recognition, and the mapping of movement data as a controller for online navigation, collaboration, and distributed performance.

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Mova

Cited by 20 publications

References 12 publications

Interactive Visualization and Dexterity Analysis of Human Movement: AIMove Platform

Interactive Visualization and Dexterity Analysis of Human Movement: AIMove Platform

SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness

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