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Your Knock Is My Command: Binary Hand Gesture Recognition on Smartphone with Accelerometer
Mobile Information Systems ( IF 1.863 ) Pub Date : 2020-07-26 , DOI: 10.1155/2020/8864627
Huixiang Zhang 1 , Wenteng Xu 1 , Chunlei Chen 2 , Liang Bai 3 , Yonghui Zhang 2
Affiliation  

Motion-based hand gesture is an important scheme to allow users to invoke commands on their smartphones in an eyes-free manner. However, the existing scheme is facing some problems. On the one hand, the expression ability of one single gesture is limited. As a result, a gesture set consisting of multiple gestures is typically adopted to represent different commands. Users must memorize all gestures in order to make interaction successfully. On the other hand, the design of gestures needs to be complicated to express diverse intensions. However, complex gestures are difficult to learn and remember. In addition, complex gestures set a high recognition barrier to smart APPs. This leads to an imbalance problem. Different gestures have different recognition accuracy levels, which may result in instability of recognition precision in practical applications. To address these problems, this paper proposes a novel scheme using binary motion gestures. Only two simple gestures are required to express bit “0” and “1,” and rich information can be expressed through the permutation and combination of the two binary gestures. Firstly, four kinds of candidate binary gestures are evaluated for eyes-free interactions. Then, an online signal cutting and merging algorithm is designed to split accelerometer signals sequence into multiple separate gesture signal segments. Next, five algorithms, including Dynamic Time Warping (DTW), Naive Bayes, Decision Tree, Support Vector Machine (SVM), and Bidirectional Long Short-Term Memory (BLSTM) Network, are adopted to recognize these segments of knock gestures. The BLSTM achieves the top performance in terms of both recognition accuracy and recognition imbalance. Finally, an Android application is developed to illustrate the usability of the proposed binary gestures. As binary gestures are much simpler than traditional hand gestures, they are more efficient and user-friendly. Our scheme eliminates the imbalance problem and achieves high recognition accuracy.

中文翻译:

敲门是我的命令:带加速度计的智能手机上的二进制手势识别

基于动作的手势是一种重要的方案,允许用户以无视的方式调用其智能手机上的命令。但是,现有方案面临一些问题。一方面,单个手势的表达能力受到限制。结果,通常采用由多个手势组成的手势集来表示不同的命令。用户必须记住所有手势才能成功进行交互。另一方面,手势的设计需要复杂以表达不同的意图。但是,复杂的手势很难学习和记忆。此外,复杂的手势为智能APP设置了较高的识别障碍。这导致不平衡的问题。不同的手势具有不同的识别精度级别,在实际应用中可能会导致识别精度不稳定。为了解决这些问题,本文提出了一种使用二进制运动手势的新颖方案。只需两个简单手势即可表示位“ 0”和“ 1”,并且可以通过两个二进制手势的置换和组合来表示丰富的信息。首先,评估了四种候选二进制手势的无眼交互。然后,设计了一种在线信号切割和合并算法,以将加速度计信号序列分为多个单独的手势信号段。接下来,采用五种算法,包括动态时间规整(DTW),朴素贝叶斯,决策树,支持向量机(SVM)和双向长期短期记忆(BLSTM)网络,来识别敲打手势的这些片段。BLSTM在识别精度和识别不平衡方面均达到了最高的性能。最后,开发了一个Android应用程序来说明所提出的二进制手势的可用性。由于二进制手势比传统手势简单得多,因此它们更加高效且用户友好。我们的方案消除了不平衡问题,并实现了高识别精度。
更新日期:2020-07-26
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