Coleção de Artigos Acadêmicos

URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/3227

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20223

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Autor: search.filters.author.TIAGO FERNANDES TAVARESÁrea do Conhecimento: search.filters.subjectcnpq.CIENCIA DA COMPUTACAO

Resultados da Pesquisa

Agora exibindo 1 - 3 de 3
  • N/D
    Artigo Científico
    Audiovisual interactive artwork via web-deployed software: Motus composes Homino-idea
    (2022) Amstalden, Augusto Piato; TIAGO FERNANDES TAVARES; Costa Neto, Anésio Azevedo; Camarini, Giovana Cardi
    Many art installations rely on camera-based audiovisual interactions, and this commonly requires specialized hardware and software. Consequently, audiovisual installations are usually restricted to wealthier areas, in which the specialized equipment can be afforded and properly hosted. In countries with an evident income unbalance linked to location, the geographic restriction leads to an audience restriction. In this work, we present the development of a web-deployed composition tool for audiovisual interactions that runs on the client side and does not require installing any additional software. Simultaneously, it provides visual feedback that can aid the audience to understand the experience. Consequently, the tool can be used to compose audiovisual interactions that reach a large audience via web. We further explore the tool by composing the audiovisual installation Homino-idea. The installation is inspired by the interactions between humans and the environment, and can be either shown in art venues or used online.
  • N/D
    Artigo Científico
    A multi-sensor human gait dataset captured through an optical system and inertial measurement units
    (2022) Santos, Geise; Wanderley, Marcelo; TIAGO FERNANDES TAVARES; Rocha, Anderson
    Diferent technologies can acquire data for gait analysis, such as optical systems and inertial measurement units (IMUs). Each technology has its drawbacks and advantages, ftting best to particular applications. The presented multi-sensor human gait dataset comprises synchronized inertial and optical motion data from 25 participants free of lower-limb injuries, aged between 18 and 47 years. A smartphone and a custom micro-controlled device with an IMU were attached to one of the participant’s legs to capture accelerometer and gyroscope data, and 42 refexive markers were taped over the whole body to record three-dimensional trajectories. The trajectories and inertial measurements were simultaneously recorded and synchronized. Participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and pre processed in each of two sessions, performed on diferent days. This dataset supports the comparison of gait parameters and properties of inertial and optical capture systems, whereas allows the study of gait characteristics specifc for each system.
  • N/D
    Artigo Científico
    Reliability and generalization of gait biometrics using 3D inertial sensor data and 3D optical system trajectories
    (2022) Santos, Geise; TIAGO FERNANDES TAVARES; Rocha, Anderson
    Particularities in the individuals’ style of walking have been explored for at least three decades as a biometric trait, empowering the automatic gait recognition feld. Whereas gait recognition works usually focus on improving end-to-end performance measures, this work aims at understanding which individuals’ traces are more relevant to improve subjects’ separability. For such, a manifold projection technique and a multi-sensor gait dataset were adopted to investigate the impact of each data source characteristics on this separability. Assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject-based identifcation scenario. In this setup, the subjects’ separability is more related to their physical characteristics than their movements related to gait cycles and biomechanical events. However, this study’s results also points to the feasibility of learning identity characteristics from individuals’ walking patterns learned from similarities and diferences between subjects in a verifcation setup. The explorations concluded that periodic components occurring in frequencies between 6 and 10 Hz are more signifcant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.