本文選題：智能手表 + 手勢(shì)識(shí)別��； 參考：《北京郵電大學(xué)》2016年碩士論文

【摘要】：近年來(lái)隨著可穿戴設(shè)備的普及,智能手表這一新興產(chǎn)品迅速搶占了市場(chǎng)并持續(xù)受到大眾的廣泛關(guān)注。作為便攜和易用的象征,人們對(duì)智能手表的交互體驗(yàn)提出了新的要求。手勢(shì)具有簡(jiǎn)單、高效、不受環(huán)境影響的特點(diǎn),是最適合智能手表的交互方式。由于目前手勢(shì)識(shí)別在智能手表上的應(yīng)用程度還很有限,大多數(shù)都是使用基于固有規(guī)則匹配的識(shí)別方式。本文嘗試對(duì)于成熟智能手表產(chǎn)品提出通用手勢(shì)識(shí)別解決方案,并通過實(shí)驗(yàn)驗(yàn)證其有效性,從而為手表的功能擴(kuò)展提供可行性依據(jù)。本文首先研究了當(dāng)前常用的手勢(shì)識(shí)別技術(shù),對(duì)比其優(yōu)劣之后最終確定了基于加速度的識(shí)別解決方案。根據(jù)實(shí)際使用需求對(duì)時(shí)間序列數(shù)據(jù)建模,并依據(jù)平臺(tái)限制采用了基于動(dòng)態(tài)時(shí)間規(guī)整和隱馬爾可夫模型的算法框架。隨后,考慮到手勢(shì)數(shù)據(jù)的特殊性有針對(duì)地設(shè)計(jì)相應(yīng)的輔助算法,并提出了基于最長(zhǎng)連續(xù)子序列和回溯法的數(shù)據(jù)截取算法,有效地完成了數(shù)據(jù)的處理。本文隨后按照上面提出的算法模型,基于Ticwear操作系統(tǒng)實(shí)現(xiàn)了智能手表端的手勢(shì)識(shí)別系統(tǒng)。最后,結(jié)合研究需求設(shè)計(jì)了相應(yīng)的實(shí)驗(yàn),并使用該手勢(shì)識(shí)別系統(tǒng)對(duì)假設(shè)模型進(jìn)行了全面的驗(yàn)證和評(píng)測(cè)。結(jié)果表明,兩種模型均能在300毫秒延遲之內(nèi)給出預(yù)測(cè)結(jié)果,并且達(dá)到平均80%以上的識(shí)別正確率。從而證明了解決方案的可行性。
[Abstract]:With the popularity of wearable devices in recent years, smart watches, such a new product, quickly seize the market and continue to receive widespread public attention. As a portable and easy to use symbol, people have put forward new requirements for the interactive experience of smart watches. Gestures are simple, efficient, and are not affected by the environment. They are the most suitable for smart watches. Interactive mode. Because of the limited application of gesture recognition on smart watches, most of them use the recognition method based on the inherent rules matching. This paper tries to put forward a general gesture recognition solution for the mature intelligent watch products, and verifies its effectiveness by experiments, thus providing a feasible extension of the watch's function. First, this paper studies the current common gesture recognition technology. After comparing its advantages and disadvantages, the recognition solution based on acceleration is finally determined. The time series data is modeled according to the actual use requirement, and the algorithm framework based on dynamic time warping and hidden Markov model is adopted according to the platform constraints. Then, consideration is given. According to the particularity of the gesture data, the corresponding auxiliary algorithms are designed and the data interception algorithm based on the longest continuous subsequence and backtracking method is proposed, and the data processing is completed effectively. Then, based on the algorithm model proposed above, the gesture recognition system of the intelligent watch end is realized based on the Ticwear operating system. Finally, According to the research needs, the corresponding experiments are designed and the gesture recognition system is used to verify and evaluate the hypothesis. The results show that the two models can give the prediction results within 300 millisecond delay and reach an average of more than 80% recognition accuracy, which proves the feasibility of the solution.

【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41;TP368.33

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本文編號(hào)：1891393