本文選題：用戶調(diào)研 + 牙科治療聲音�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：牙科焦慮癥是牙科治療中常見心理狀態(tài),嚴(yán)重影響患者治療體驗(yàn)、就醫(yī)及時(shí)性和治療效果。調(diào)查表明,牙科治療過程中產(chǎn)生的噪聲是造成牙科焦慮癥的重要因素之一。本文從牙科治療聲音入手,通過挖掘出患者對牙科治療聲音的感受評價(jià)與理解,選定研究范圍,設(shè)計(jì)實(shí)驗(yàn)采集牙科治療過程中的患者實(shí)際聽到的聲音,對采集到的聲音進(jìn)行客觀分析和主觀評價(jià),最終建立牙科治療聲品質(zhì)評價(jià)的BP神經(jīng)網(wǎng)絡(luò)模型,以預(yù)測牙科治療聲音所致焦慮程度。首先,基于UGC平臺文本信息挖掘及語料分析,了解到有過牙科治療經(jīng)驗(yàn)的患者對牙科治療聲音的焦慮及恐懼的痛點(diǎn)所在;然后對口腔科相關(guān)從業(yè)人員的訪談,從專業(yè)角度了解這些導(dǎo)致焦慮的聲音來源;根據(jù)所得到的信息,設(shè)計(jì)調(diào)查問卷,確定了患者對牙科治療聲音不適感的來源及程度,并分析了其他因素是否相關(guān)。針對上述調(diào)查結(jié)果確定研究的內(nèi)容。其次,以對振動(dòng)作用于牙齒時(shí)以空氣、骨骼、血液、肌肉為介質(zhì)的聲音傳播而導(dǎo)致的聽覺特性為依據(jù),設(shè)計(jì)了用羊頭代替人頭并在頭內(nèi)嵌入聲音采集設(shè)備的聲音采集實(shí)驗(yàn),采集了專業(yè)牙醫(yī)以專業(yè)的治療手法用三種牙科治療器械打磨該羊頭的牙齒時(shí)的聲音數(shù)據(jù),將聲音數(shù)據(jù)制作成40個(gè)聲音樣本并對其進(jìn)行了響度、尖銳度、粗糙度、抖動(dòng)強(qiáng)度、語言清晰度五個(gè)心理聲學(xué)屬性的客觀分析,并選擇了合適的心理聲學(xué)屬性算法。接著,利用聲音樣本進(jìn)行主觀評價(jià)實(shí)驗(yàn)。選擇26名有牙科治療經(jīng)驗(yàn)并對治療聲音存在不適感的被測者進(jìn)行聲音樣本聽音實(shí)驗(yàn),根據(jù)感受對聲音樣本進(jìn)行牙科焦慮度評價(jià)。對評價(jià)結(jié)果進(jìn)行篩選后,考察牙科焦慮度與各心理聲學(xué)屬性參數(shù)的相關(guān)性,確定主觀評價(jià)群體以及導(dǎo)致牙科焦慮的心理聲學(xué)屬性。最后,建立牙科治療聲品質(zhì)的神經(jīng)網(wǎng)絡(luò)評價(jià)模型,以導(dǎo)致牙科焦慮的三個(gè)心理聲學(xué)屬性抖動(dòng)強(qiáng)度、響度、AI指數(shù)為輸入,主觀評價(jià)牙科焦慮度為輸出,通過不同算法在建立牙科治療聲音品質(zhì)評價(jià)模型的誤差比較,確定利用BP神經(jīng)網(wǎng)絡(luò)建模,并確定神經(jīng)網(wǎng)絡(luò)的主要參數(shù)和結(jié)構(gòu)。最終建立具有預(yù)測功能的牙科治療聲音所致焦慮度的神經(jīng)網(wǎng)絡(luò)模型,從聲音角度提出緩解牙科焦慮癥狀的方法建議,并闡述該神經(jīng)網(wǎng)絡(luò)模型如何應(yīng)用于優(yōu)化牙科治療聲品質(zhì)。
[Abstract]:Dental anxiety disorder is a common psychological state in dental treatment, which seriously affects the experience, timeliness and effect of treatment. The investigation shows that the noise produced during dental treatment is one of the important factors causing dental anxiety. This article starts with the dental treatment sound, through excavates the patient to the dental treatment sound feeling appraisal and the understanding, selects the research scope, designs the experiment to collect the dental treatment process the patient actually hears the sound, The objective analysis and subjective evaluation of the collected sound were carried out, and the BP neural network model of sound quality evaluation for dental treatment was established to predict the degree of anxiety caused by the sound in dental treatment. First of all, based on the UGC platform text information mining and corpus analysis, we know the pain point of anxiety and fear of dental treatment voice in patients with dental treatment experience, and then interview the relevant practitioners of stomatology. According to the information obtained, a questionnaire was designed to determine the source and extent of patients' voice discomfort in dental treatment, and to analyze whether other factors were relevant. According to the above investigation results, the content of the study is determined. Secondly, based on the auditory characteristics caused by the sound propagation of air, bone, blood and muscle when the vibration acts on the teeth, a sound acquisition experiment is designed, in which the sheep head replaces the human head and the sound acquisition equipment is embedded in the head. The sound data of professional dentists who used three kinds of dental instruments to grind the teeth of the sheep head were collected. The sound data were made into 40 sound samples and were made into loudness, acuity, roughness and shaking intensity. Objective analysis of five psychoacoustics attributes of language articulation and selection of appropriate psychoacoustic attributes algorithm. Then, the subjective evaluation experiment is carried out with sound samples. 26 subjects with dental treatment experience were selected to conduct sound sample listening experiments and to evaluate dental anxiety of sound samples according to their feelings. After the evaluation results were screened, the correlation between dental anxiety and the parameters of each psychoacoustical attribute was investigated, and the subjective evaluation group and the psychoacoustic attributes that led to dental anxiety were determined. Finally, a neural network evaluation model of dental treatment sound quality was established. The three psychoacoustic attributes of dental anxiety, such as the intensity of jitter, the loudness and AI index, and the subjective evaluation of dental anxiety were taken as the input, and the subjective evaluation of dental anxiety was taken as the output. By comparing the errors of different algorithms in establishing a sound quality evaluation model for dental treatment, the BP neural network is used to model the model, and the main parameters and structure of the neural network are determined. Finally, a neural network model with predictive function for anxiety degree caused by sound in dental treatment was established, and the methods to alleviate dental anxiety symptoms were proposed from the sound point of view, and how the neural network model was applied to optimize the sound quality of dental treatment was expounded.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP183;R78
，

本文編號：2067075