Intra-/inter-user adaptation framework for wearable gesture sensing device
Intra-/inter-user adaptation framework for wearable gesture sensing device
复制标题
可穿戴手势传感设备的用户内/用户间适应框架
DOI：
10.1145/3267242.3267256
复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Sugimoto
中科院分区：
文献类型：
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作者：
Kosuke Kikui;Yuta Itoh;M. Yamada;Yuta Sugiura;M. Sugimoto
光反射传感器（PRS）是一种微型测距模块，是一种广泛应用于可穿戴用户界面的电子元件。这种可穿戴PRS设备在实际使用中的不可避免的问题是需要独立于用户的训练以具有高姿势识别准确度。每个新用户都必须通过提供新的训练数据来重新训练设备（我们称之为用户间设置）。更糟糕的是，理想情况下，每次同一用户重新佩戴设备时，都需要重新训练（我们称之为用户内设置）。在本文中，我们提出了一个域自适应框架，以减少这种培训成本的用户。具体来说，我们将预训练的卷积神经网络（CNN）用于用户间和用户内设置，以保持较高的识别精度。我们证明，与实际的PRS设备，我们的框架显着提高了用户内和用户间设置的平均分类准确度高达87.43%和80.06%对基线（非适应）设置的准确度分别为68.96%和63.26%。
The photo reflective sensor (PRS), a tiny distant-measurement module, is a popular electronic component widely used in wearable user-interfaces. An unavoidable issue of such wearable PRS devices in practical use is the need of user-independent training to have high gesture recognition accuracy. Each new user has to re-train a device by providing new training data (we call the inter-user setup). Even worse, re-training is also necessary ideally every time when the same user re-wears the device (we call the intra-user setup). In this paper, we propose a domain adaptation framework to reduce this training cost of users. Specifically, we adapt a pre-trained convolutional neural network (CNN) for both inter-user and intra-user setups to maintain the recognition accuracy high. We demonstrate, with an actual PRS device, that our framework significantly improves the average classification accuracy of the intra-user and inter-user setups up to 87.43% and 80.06% against the baseline (non-adapted) setups with the accuracy 68.96% and 63.26% respectively.