uKnit: A Position-Aware Reconfigurable Machine-Knitted Wearable for Gestural Interaction and Passive Sensing using Electrical Impedance Tomography

uKnit: A Position-Aware Reconfigurable Machine-Knitted Wearable for Gestural Interaction and Passive Sensing using Electrical Impedance Tomography
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DOI:
10.1145/3544548.3580692
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发表时间:
2023-04
期刊:
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
影响因子:
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通讯作者:
T. Yu;Riku Arakawa;James McCann;Mayank Goel
T. Yu;Riku Arakawa;James McCann;Mayank Goel
中科院分区:
其他
文献类型:
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作者:
T. Yu;Riku Arakawa;James McCann;Mayank Goel

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围巾本身具有可重构性:佩戴者经常将其用作围脖、披肩、头带、腕带等等。我们开发了uKnit,一种具有围巾般可重构性的类似围巾的软传感器,它是通过机器编织和电阻抗断层成像传感技术制成的。柔软的可穿戴设备很舒适,因此在许多人机交互场景中颇具吸引力。虽然先前的研究已经展示了各种柔软的可穿戴能力,但每种能力都是针对特定设备和位置的,无法用单一设备满足用户的各种需求。相比之下,uKnit探索了一种“一软适所有”的可能性。我们描述了uKnit背后的制作和传感原理,展示了几个示例应用,并通过10名参与者的用户研究和可洗涤性测试对其进行了评估。对于5类佩戴位置检测,uKnit使用每个用户的模型/通用模型分别达到了88.0%/78.2%的准确率,对于7类手势识别分别达到了80.4%/75.4%的准确率。此外,它能以1.25次/分钟的误差率识别呼吸频率,并以86.2%的平均准确率检测两种坐姿。
A scarf is inherently reconfigurable: wearers often use it as a neck wrap, a shawl, a headband, a wristband, and more. We developed uKnit, a scarf-like soft sensor with scarf-like reconfigurability, built with machine knitting and electrical impedance tomography sensing. Soft wearable devices are comfortable and thus attractive for many human-computer interaction scenarios. While prior work has demonstrated various soft wearable capabilities, each capability is device- and location-specific, being incapable of meeting users’ various needs with a single device. In contrast, uKnit explores the possibility of one-soft-wearable-for-all. We describe the fabrication and sensing principles behind uKnit, demonstrate several example applications, and evaluate it with 10-participant user studies and a washability test. uKnit achieves 88.0%/78.2% accuracy for 5-class worn-location detection and 80.4%/75.4% accuracy for 7-class gesture recognition with a per-user/universal model. Moreover, it identifies respiratory rate with an error rate of 1.25 bpm and detects binary sitting postures with an average accuracy of 86.2%.