【摘要】：人工智能技術(shù)的發(fā)展和支持慣性傳感器的可穿戴設(shè)備的不斷普及,促進(jìn)了行為識別相關(guān)研究和應(yīng)用的快速發(fā)展。作為行為識別研究的一個重要分支,行人位移檢測在設(shè)備定位、節(jié)能、醫(yī)療看護(hù)等方面都具有十分重要的作用。目前研究與應(yīng)用主要面臨兩個問題:依賴專用的慣性傳感器設(shè)備,系統(tǒng)成本較高;傳感器設(shè)備位置固定,系統(tǒng)靈活性低。與此同時,日漸普及的智能手機已普遍嵌入了加速度計、陀螺儀和磁力計等慣性傳感器,通過智能手機檢測行人位移顯然將具有更加廣泛的應(yīng)用價值。本文提出了一種基于智能手機內(nèi)嵌陀螺儀的行人位移檢測方法。首先,通過使用快速傅立葉變換(Fast Fourier Transform,FFT)方法,有效提取陀螺儀信號的頻域特征。其次,根據(jù)非約束條件下行人步行的頻率特征,建立了一個基于閾值的檢測模型,實現(xiàn)了位移的準(zhǔn)確檢測。最后,在真實場景中設(shè)計并實現(xiàn)大量的實驗,通過與現(xiàn)有的標(biāo)準(zhǔn)差閾值(Standard Deviation Threshold,STD__TH)方法和短時傅里葉變換(Short Term Fourier Transform,STFT)方法進(jìn)行比較,驗證了本文提出的行人位移檢測方法的有效性和可行性。實驗結(jié)果表明,本文提出方法的識別精度保持在83%以上,最高可達(dá)92.66%;而STFT方法和STD_TH方法的精度較差,最高精度可達(dá)79.40%和67.80%。綜上所述,本文提出的位移檢測方法在位置約束、檢測精度等方面都展現(xiàn)了一定的優(yōu)勢,將對基于行為識別的應(yīng)用和研究起到積極的作用。
[Abstract]:The development of artificial intelligence technology and the popularization of wearable devices which support inertial sensors promote the rapid development of research and application related to behavior recognition. As an important branch of behavior recognition, pedestrian displacement detection plays an important role in equipment location, energy saving, medical care and so on. At present, the research and application mainly face two problems: depending on the special inertial sensor equipment, the system cost is high, the sensor equipment is fixed in position, and the system flexibility is low. At the same time, inertial sensors such as accelerometer, gyroscope and magnetometer are widely embedded in the increasingly popular smart phones. It is obvious that detecting pedestrian displacement through smart phones will have a wider application value. In this paper, a pedestrian displacement detection method based on smart phone embedded gyroscope is proposed. Firstly, by using Fast Fourier transform (Fast Fourier Transform,FFT) method, the frequency domain features of gyroscope signal are extracted effectively. Secondly, according to the frequency characteristics of pedestrian walking under unconstrained conditions, a threshold-based detection model is established, and the accurate detection of displacement is realized. Finally, a large number of experiments are designed and implemented in real-world, and compared with the standard difference threshold (Standard Deviation Threshold,STD__TH) method and the short-time Fourier transform (Short Term Fourier Transform,STFT) method. The validity and feasibility of the proposed pedestrian displacement detection method are verified. The experimental results show that the recognition accuracy of the proposed method is above 83% with a maximum of 92.66%, while the accuracy of the STFT method and the STD_TH method is poor, with the highest accuracy of 79.40% and 67.80%. In summary, the displacement detection method presented in this paper shows some advantages in position constraint, detection accuracy and so on, which will play an active role in the application and research of behavior-based recognition.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9

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本文編號：2455028