Topple-free foot strategy applied to real-time motion capture data using kinect sensor

Silva, Danilo B. da; Vidal, Creto Augusto; Cavalcante-Neto, Joaquim Bento; Pessoa, Italo Ney Silva; Nunes, Rubens Fernandes

doi:10.1145/3412841.3441890

Proceedings of the 36th Annual ACM Symposium on Applied Computing 2021

DOI: 10.1145/3412841.3441890

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Topple-free foot strategy applied to real-time motion capture data using kinect sensor

Danilo B. da Silva

Creto Augusto Vidal

Joaquim Bento Cavalcante-Neto

et al.

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2022

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Cited by 2 publications

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Shoes++

Yan

Zhou

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Feet are the foundation of our bodies that not only perform locomotion but also participate in intent and emotion expression. Thus, foot gestures are an intuitive and natural form of expression for interpersonal interaction. Recent studies have mostly introduced smart shoes as personal gadgets, while foot gestures used in multi-person foot interactions in social scenarios remain largely unexplored. We present Shoes++, which includes an inertial measurement unit (IMU)-mounted sole and an input vocabulary of social foot-to-foot gestures to support foot-based interaction. The gesture vocabulary is derived and condensed by a set of gestures elicited from a participatory design session with 12 users. We implement a machine learning model in Shoes++ which can recognize two-person and three-person social foot-to-foot gestures with 94.3% and 96.6% accuracies (N=18). In addition, the sole is designed to easily attach to and detach from various daily shoes to support comfortable social foot interaction without taking off the shoes. Based on users' qualitative feedback, we also found that Shoes++ can support team collaboration and enhance emotion expression, thus making social interactions or interpersonal dynamics more engaging in an expanded design space.

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Shoes++

Yan

Zhou

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Real-Time Stylized Humanoid Behavior Control through Interaction and Synchronization

Cao

Bao

Ren

et al. 2022

Sensors

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Restricted by the diversity and complexity of human behaviors, simulating a character to achieve human-level perception and motion control is still an active as well as a challenging area. We present a style-based teleoperation framework with the help of human perceptions and analyses to understand the tasks being handled and the unknown environment to control the character. In this framework, the motion optimization and body controller with center-of-mass and root virtual control (CR-VC) method are designed to achieve motion synchronization and style mimicking while maintaining the balance of the character. The motion optimization synthesizes the human high-level style features with the balance strategy to create a feasible, stylized, and stable pose for the character. The CR-VC method including the model-based torque compensation synchronizes the motion rhythm of the human and character. Without any inverse dynamics knowledge or offline preprocessing, our framework is generalized to various scenarios and robust to human behavior changes in real-time. We demonstrate the effectiveness of this framework through the teleoperation experiments with different tasks, motion styles, and operators. This study is a step toward building a human-robot interaction that uses humans to help characters understand and achieve the tasks.

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Topple-free foot strategy applied to real-time motion capture data using kinect sensor

Cited by 2 publications

References 20 publications

Shoes++

Shoes++

Real-Time Stylized Humanoid Behavior Control through Interaction and Synchronization

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