TY - JOUR A2 - Bashir, Ali Kashif AU - Zhang, Huixiang AU - Xu, Wenteng AU - Chen, Chunlei AU - Bai, Liang AU - Zhang, Yonghui PY - 2020 DA - 2020/07/26 TI - Your Knock Is My Command:基于加速度计SP - 8864627 VL - 2020 AB -based手势的智能手机二进制手势识别是一种重要的方案，允许用户在智能手机上以一种无需眼睛的方式调用命令。然而，现有的方案也面临着一些问题。一方面，单一手势的表达能力是有限的。因此，一个由多个手势组成的手势集通常被用来表示不同的命令。用户必须记住所有的手势才能成功进行交互。另一方面，手势的设计需要复杂，以表达多样的内涵。然而，复杂的手势很难学习和记忆。此外，复杂的手势为智能应用程序设置了很高的识别障碍。这就导致了不平衡的问题。 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. SN - 1574-017X UR - https://doi.org/10.1155/2020/8864627 DO - 10.1155/2020/8864627 JF - Mobile Information Systems PB - Hindawi KW - ER -