User-Centric and Real-Time Activity Recognition Using Smart Glasses
User-Centric and Real-Time Activity Recognition Using Smart Glasses
复制标题
使用智能眼镜进行以用户为中心的实时活动识别
DOI：
10.1007/978-3-319-39077-2_13
复制
发表时间：
2016
影响因子：
27.4
通讯作者：
Chien
中科院分区：
文献类型：
--
作者：
Joshua Ho;Chien
在本文中，我们提出了一个个性化和实时原型解决方案的智能眼镜针对活动识别。我们的工作是基于对传感器数据的分析来研究用户的运动和活动，同时利用捆绑有各种传感器的可穿戴眼镜。该软件系统收集、训练数据并构建快速分类模型，重点是特定特征如何注释和提取头戴式行为。实验结果表明，基于本文提出的特征选择算法，系统达到了较高的准确率和较低的计算代价。与以往在智能手机或智能眼镜传感器上的数据挖掘工作以及智能手机上的活动识别相关工作相比，本文的结果表明，准确率达到87%，响应时间小于3 s。该系统可以为眼镜佩戴者提供更有洞察力和更强大的服务。它可能会被期望在未来进行更多的以用户为中心和上下文感知的可穿戴应用。
In this paper, we present a personalized and real-time prototyping solution on smart glasses targeting activity recognition. Our work is based on the analysis of sensor data to study user’s motions and activities, while utilizing wearable glasses bundled with various sensors. The software system collects, trains data, and builds the model for fast classification, which emphasizes on how specific features annotate and extract head-mounted behavior. Based on our feature selection algorithm, the system reaches high accuracy and low computation cost in the experiments. Other than some previous works in data mining on sensors of smart phones or smart glasses, and related works of activity recognition on smartphones, our results show the accuracy achieves 87 %, and the responsive time is less than 3 s. The proposed system can provide more insightful and powerful services for the glass wearers. It would be possibly expected to carry out more user-centric and context-aware wearable applications in the future.