本文選題：行為識別　切入點：機器學習　出處：《浙江大學》2017年碩士論文　論文類型：學位論文

【摘要】：人體行為識別是機器學習領(lǐng)域中一個重要的研究方向。隨著傳感器技術(shù)、移動互聯(lián)網(wǎng)的快速發(fā)展和機器學習理論的成熟,基于移動設(shè)備傳感器的人體行為識別技術(shù)越來越得到研究人員的關(guān)注。移動設(shè)備具有便攜性、實時性、靈活性等優(yōu)勢,在人體行為識別領(lǐng)域得到廣泛應(yīng)用。人體行為識別技術(shù)具有研究價值和商業(yè)價值,具體應(yīng)用場景有:人機交互、運動輔助、增強現(xiàn)實、智能監(jiān)控等。人體行為識別是一個分類問題,因此,分類結(jié)果的準確率顯得尤為重要。如果分類準確率不能達到應(yīng)用可接受的范圍,將會對用戶產(chǎn)生負面的影響。分類準確率是由模型得到,一個模型的好壞對最終結(jié)果有很大影響。因此,本文首先通過實驗分析,得到數(shù)據(jù)集非線性可分和難識別樣本的問題會影響識別準確率的結(jié)論。然后,本文從模型方面進行優(yōu)化,研究模型融合和模型參數(shù)選擇來提高識別準確率。本文采集了移動智能設(shè)備下多種人體行為的加速度傳感器數(shù)據(jù),并對裸數(shù)據(jù)進行預(yù)處理和特征提取,得到可用于機器學習模型建模的數(shù)據(jù)樣本。相較于DTW模板匹配算法,機器學習的分類模型具有更高的識別準確率。本文采用常用的機器學習模型對數(shù)據(jù)集進行建模分析。結(jié)果顯示,常用的機器學習模型能夠獲得不錯的識別準確率。盡管如此,常用的機器學習模型還有很大的提升空間。本文通過對實驗結(jié)果分析發(fā)現(xiàn),數(shù)據(jù)雖然經(jīng)過預(yù)處理,但依然存在非線性可分和難識別樣本的問題。單個模型往往很難處理這些問題。通過分析常用機器學習模型的優(yōu)缺點,本文基于特征空間和模型預(yù)測兩個角度進行優(yōu)化,提出兩種模型融合的方案。新模型相對于單個模型揚長避短,有效解決上述兩個問題,提高識別準確率。最后,本文通過實驗驗證模型的正確性,并且針對模型的參數(shù)進行研究。
[Abstract]:Human behavior recognition is an important research direction in the field of machine learning. With the rapid development of sensor technology, mobile Internet and the maturity of machine learning theory, Human behavior recognition technology based on mobile device sensors has attracted more and more attention of researchers. Mobile devices have the advantages of portability, real-time, flexibility and so on. Human behavior recognition is widely used in the field of human behavior recognition. Human behavior recognition technology has research value and commercial value, the specific application scenes are: man-machine interaction, motion assistance, augmented reality, Intelligent monitoring and so on. Human behavior recognition is a classification problem, so the accuracy of classification results is particularly important. The accuracy of classification is obtained from the model, and the quality of a model has a great impact on the final result. It is concluded that the problem of nonlinear separable data sets and difficult to identify samples will affect the recognition accuracy. Then, this paper optimizes the model from the point of view of the model. Model fusion and model parameter selection are studied to improve the recognition accuracy. In this paper, the acceleration sensor data of various human behaviors under mobile intelligent devices are collected, and the naked data are preprocessed and feature extraction. Data samples can be used to model machine learning model. Compared with DTW template matching algorithm, The classification model of machine learning has higher recognition accuracy. In this paper, the commonly used machine learning model is used to model and analyze the data set. The results show that the commonly used machine learning model can achieve good recognition accuracy. There is still a lot of room for improvement in the commonly used machine learning models. Through the analysis of the experimental results, it is found that although the data is preprocessed, However, there are still problems of nonlinear separability and difficulty in identifying samples. These problems are often difficult to deal with by single model. By analyzing the advantages and disadvantages of commonly used machine learning models, this paper optimizes them from the perspectives of feature space and model prediction. Two models fusion scheme is proposed. Compared with a single model, the new model can effectively solve the above two problems and improve the recognition accuracy. Finally, the correctness of the model is verified by experiments. And the parameters of the model are studied.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP181

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