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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 \u201c0\u201d and \u201c1,\u201d 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.<\/jats:p>","DOI":"10.1155\/2020\/8864627","type":"journal-article","created":{"date-parts":[[2020,7,26]],"date-time":"2020-07-26T23:31:40Z","timestamp":1595806300000},"page":"1-16","source":"Crossref","is-referenced-by-count":2,"title":["Your Knock Is My Command: Binary Hand Gesture Recognition on Smartphone with Accelerometer"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4823-1436","authenticated-orcid":true,"given":"Huixiang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenteng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Weifang University, Weifang 261061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Bai","sequence":"additional","affiliation":[{"name":"No. 203 Research Institute of China Ordnance, Xi\u2019an 710065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Weifang University, Weifang 261061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1145\/3181672"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2019.03.011"},{"issue":"6","key":"20","first-page":"1771","volume":"4","year":"2017","journal-title":"International Research Journal. 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