使用软聚合物声波导阵列的可佩戴实时手部运动跟踪系统的设计。

Soft robotics Pub Date : 2024-04-01 Epub Date: 2023-10-23 DOI:10.1089/soro.2022.0091
Yuan Lin, Peter B Shull, Jean-Baptiste Chossat
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引用次数: 0

摘要

强大的手部运动跟踪有望在不同领域改善人机交互,包括虚拟现实和自动手语翻译。然而,当前的可穿戴手部运动跟踪方法通常在检测性能、可穿戴性和耐用性方面受到限制。本文介绍了一种使用多个软聚合物声波导(SPAW)的手部运动跟踪系统。SPAW作为应变传感器的创新使用提供了几个优点来解决这些局限性。SPAW可以通过铸造形状为软声波导的软聚合物并包含商业上可买到的小型陶瓷压电换能器来容易地制造。当用作应变传感器时,SPAW表现出高拉伸性(高达100%)、高线性(R2 > 在所有准静态、动态和耐久性拉伸试验中均为0.996),磁滞可忽略不计(
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of a Wearable Real-Time Hand Motion Tracking System Using an Array of Soft Polymer Acoustic Waveguides.

Robust hand motion tracking holds promise for improved human-machine interaction in diverse fields, including virtual reality, and automated sign language translation. However, current wearable hand motion tracking approaches are typically limited in detection performance, wearability, and durability. This article presents a hand motion tracking system using multiple soft polymer acoustic waveguides (SPAWs). The innovative use of SPAWs as strain sensors offers several advantages that address the limitations. SPAWs are easily manufactured by casting a soft polymer shaped as a soft acoustic waveguide and containing a commercially available small ceramic piezoelectric transducer. When used as strain sensors, SPAWs demonstrate high stretchability (up to 100%), high linearity (R2 > 0.996 in all quasi-static, dynamic, and durability tensile tests), negligible hysteresis (<0.7410% under strain of up to 100%), excellent repeatability, and outstanding durability (up to 100,000 cycles). SPAWs also show high accuracy for continuous finger angle estimation (average root-mean-square errors [RMSE] <2.00°) at various flexion-extension speeds. Finally, a hand-tracking system is designed based on a SPAW array. An example application is developed to demonstrate the performance of SPAWs in real-time hand motion tracking in a three-dimensional (3D) virtual environment. To our knowledge, the system detailed in this article is the first to use soft acoustic waveguides to capture human motion. This work is part of an ongoing effort to develop soft sensors using both time and frequency domains, with the goal of extracting decoupled signals from simple sensing structures. As such, it represents a novel and promising path toward soft, simple, and wearable multimodal sensors.

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