本文選題：可穿戴式設(shè)備 + 手勢識別　； 參考：《中國海洋大學》2015年碩士論文

【摘要】：近年來,隨著人們生活品質(zhì)的提高,智能家電系統(tǒng)的控制與研究倍受關(guān)注,成為最活躍的研究方向之一。而手勢是人類日常生活中必不可少的一部分,是人與人之間一種廣泛的交流形式,在人機交互中,它逐步成為新興的交互方式。基于現(xiàn)有的理論和研究,本文經(jīng)過分析和總結(jié),實現(xiàn)了一種基于隱馬爾可夫模型的手勢識別,通過對手勢的識別達到控制家用電器的目的。本文以手勢識別作為研究對象,對相關(guān)理論方法展開了系統(tǒng)的分析,從用戶的實際需求出發(fā),設(shè)計完成了基于可穿戴式設(shè)備的智能家電系統(tǒng)。本文主要從以下幾個方面展開工作：(1)特征提取。特征提取主要包括時間域特征和頻率域特征。由于時間域特征比較直觀,而且容易提取,其性價比比提取頻率域特征要高,因此本文選擇提取時間域特征。最終提取出的時間域特征包括：均值、合成加速度、方差、振幅、加速度最大軸、波峰數(shù)、峰值距離、均方根特征和信號幅度區(qū)域。(2)基于隱馬爾可夫模型的手勢識別過程。本文首先通過實驗采集提取特征數(shù)據(jù),將特征數(shù)據(jù)分為六大組,包括上、下、左、右、前、后,然后通過HMM學習得出隱馬爾可夫模型。擁有隱馬爾可夫模型后,使用序列后向特征選擇方法進行后項選擇特征,選擇出實驗需要的特征,并建立出完善的隱馬爾可夫模型。最終本文選擇出的特征有五個,分別是：均值,振幅、加速度最大軸、波峰數(shù)以及均方根特征。最后,用預留出的測試數(shù)據(jù)對HMM模型進行測試,得到結(jié)果。以后使用此隱馬爾可夫模型即可識別手勢。本文通過進行實驗模擬,驗證了算法的有效性,對實現(xiàn)智能家電系統(tǒng)的控制提供了強有力的證據(jù),可以為今后智能家電行業(yè)的發(fā)展提供一定的理論支持,使得人與電器交互更加的人性化、智能化、舒適化,讓家電更加地認識用戶、懂得用戶的需求、了解用戶的肢體語言。
[Abstract]:In recent years, with the improvement of people's quality of life, the control and research of intelligent home appliance system have attracted much attention, and become one of the most active research directions. Gesture is an indispensable part of human daily life, is a kind of extensive communication between people, in human-computer interaction, it has gradually become a new way of interaction. Based on the existing theory and research, this paper analyzes and summarizes a kind of gesture recognition based on hidden Markov model, through which the purpose of controlling household appliances is achieved. In this paper, gesture recognition is taken as the research object, the related theories and methods are systematically analyzed, and the intelligent appliance system based on wearable devices is designed and completed according to the actual needs of users. This paper mainly works on the following aspects: 1) feature extraction. Feature extraction mainly includes time domain feature and frequency domain feature. Because the feature of time domain is more intuitive and easy to extract, and its ratio of performance to price is higher than that of frequency domain, this paper chooses to extract the feature of time domain. The extracted time domain features include: mean, synthetic acceleration, variance, amplitude, maximum axis of acceleration, peak number, peak distance, root mean square feature and signal amplitude region. 2) gesture recognition process based on hidden Markov model. In this paper, the feature data are collected and extracted through experiments, and the feature data are divided into six groups: top, bottom, left, right, front and back. Then the hidden Markov model is obtained by HMM learning. After possessing the hidden Markov model, the sequential backward feature selection method is used to select the features needed in the experiment, and a perfect hidden Markov model is established. Finally, there are five features selected in this paper: mean, amplitude, maximum axis of acceleration, number of peaks and root mean square (RMS). Finally, the HMM model is tested with the reserved test data and the results are obtained. This hidden Markov model can be used to recognize gestures. Through the experimental simulation, the validity of the algorithm is verified, which provides a strong evidence for the realization of intelligent home appliance system control, and can provide certain theoretical support for the development of intelligent home appliance industry in the future. It makes the interaction between people and electrical appliances more humanized, intelligent and comfortable. It makes household appliances more aware of users, understand their needs, and understand their body language.
【學位授予單位】：中國海洋大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM925.0

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