本文選題：膝骨性關(guān)節(jié)炎　切入點(diǎn)：ZigBee　出處：《福州大學(xué)》2014年碩士論文

【摘要】：隨著社會(huì)的老齡化,膝骨性關(guān)節(jié)炎也越來越多的發(fā)病于中老年人群,發(fā)病率也逐年遞增。此類疾病的癥狀一般表現(xiàn)為下肢關(guān)節(jié)的劇烈疼痛和下肢活動(dòng)的運(yùn)動(dòng)功能障礙。目前雖然對膝骨性關(guān)節(jié)炎的治療方法較多,但是都不能解決膝骨性關(guān)節(jié)炎對患者預(yù)后的功能活動(dòng)帶來的影響。患者在完成適量的運(yùn)動(dòng)療法訓(xùn)練后,能使其下肢的耐力和肌力得到增強(qiáng),對患者的血液循環(huán)和骨關(guān)節(jié)的活動(dòng)性都有促進(jìn)的作用,最為重要的是能夠保持骨關(guān)節(jié)的穩(wěn)定性。因此運(yùn)動(dòng)療法對膝骨性關(guān)節(jié)炎具有巨大的臨床應(yīng)用價(jià)值。而在訓(xùn)練的過程中若不按一定的規(guī)范性進(jìn)行運(yùn)動(dòng),運(yùn)動(dòng)過于劇烈或者運(yùn)動(dòng)過輕,將對加劇患者的病情或者達(dá)不到訓(xùn)練的效果。因此本文將設(shè)計(jì)一基于ZigBee的膝骨性關(guān)節(jié)炎運(yùn)動(dòng)療法的監(jiān)護(hù)系統(tǒng),對患者的動(dòng)作進(jìn)行規(guī)范性判別。本文的主要工作如下：(1)對人體的兩種運(yùn)動(dòng)模型進(jìn)行了分析和討論,并針對膝骨性關(guān)節(jié)炎運(yùn)動(dòng)療法的下肢運(yùn)動(dòng)特點(diǎn)提出了新的運(yùn)動(dòng)信息采集方案。該方案采用多個(gè)三維加速度傳感器,分別固定放置在下肢小腿和大腿的質(zhì)點(diǎn)位置。通過運(yùn)動(dòng)信息采集系統(tǒng)能有效的完成對人體下肢運(yùn)動(dòng)加速度信息的獲取。(2)針對人體的多質(zhì)點(diǎn)運(yùn)動(dòng)模型,設(shè)計(jì)了基于ZigBee的數(shù)據(jù)采集系統(tǒng),對加速度信息進(jìn)行采集,并依據(jù)ZigBee的終端子節(jié)點(diǎn)和協(xié)調(diào)器節(jié)點(diǎn)所需實(shí)現(xiàn)的具體功能進(jìn)行了電路設(shè)計(jì)。(3)設(shè)計(jì)了一套針對膝骨性關(guān)節(jié)炎常見的動(dòng)作規(guī)范性識別算法。該算法的主要步驟為：首先將采集的加速度信息進(jìn)行去噪和濾波的預(yù)處理,對下肢運(yùn)動(dòng)的有效運(yùn)動(dòng)片段進(jìn)行提取,利用小波變換將加速度的時(shí)域和頻域信息作為分類識別的特征量,再利用粒子群算法進(jìn)行特征選擇得到分類能力最強(qiáng)的特征向量,最后利用特征向量對動(dòng)作的規(guī)范性進(jìn)行判別。(4)通過實(shí)驗(yàn)平臺對系統(tǒng)性能進(jìn)行測試。系統(tǒng)的分類結(jié)果顯示,系統(tǒng)對抬腿、保持和收腿階段中出現(xiàn)的四類動(dòng)作(規(guī)范動(dòng)作、曲腿、側(cè)腿和抖動(dòng))的平均識別率都在95.9±1.2%以上。本文設(shè)計(jì)了一種基于ZigBee的膝骨性關(guān)節(jié)炎運(yùn)動(dòng)療法監(jiān)護(hù)系統(tǒng),系統(tǒng)能有效的對動(dòng)作的不規(guī)范性進(jìn)行判別,滿足監(jiān)護(hù)系統(tǒng)的應(yīng)用需求。該系統(tǒng)性價(jià)比高、體積小、功耗低,在針對膝骨性關(guān)節(jié)炎的運(yùn)動(dòng)監(jiān)護(hù)領(lǐng)域具有實(shí)際應(yīng)用的價(jià)值。
[Abstract]:With the aging of society, knee osteoarthritis is more and more common in middle-aged and elderly people. The incidence of these diseases is also increasing year by year. The symptoms of these diseases are generally characterized by severe pain in the joints of the lower extremities and motor dysfunction of the lower extremities. Although there are many treatments for knee osteoarthritis, However, none of them can solve the impact of knee osteoarthritis on the prognosis of patients. Patients can increase their lower limb endurance and muscle strength after proper exercise therapy training. It can promote the blood circulation and the activity of bone and joint in patients. The most important thing is to maintain the stability of osteoarthritis. Therefore, exercise therapy has great clinical application value in knee osteoarthritis. Too much exercise or too little exercise will aggravate the patient's condition or fail to achieve the effect of training. Therefore, this paper will design a monitoring system for exercise therapy of knee osteoarthritis based on ZigBee. The main work of this paper is as follows: 1) analyze and discuss the two kinds of motion models of human body. According to the motion characteristics of knee osteoarthritis, a new motion information collection scheme is proposed, which uses several three dimensional accelerometers. Fixed in the lower extremities and legs of the particle position respectively. Through the motion information collection system can effectively complete the acquisition of human lower limb motion acceleration information. 2) for the human body multi-particle motion model, A data acquisition system based on ZigBee is designed to collect acceleration information. According to the specific function of terminal sub-node and coordinator node of ZigBee, the circuit design is carried out. (3) A set of common action normative recognition algorithm for knee osteoarthritis is designed. The main steps of the algorithm are as follows: first. First, the acceleration information collected is preprocessed by denoising and filtering. The effective motion segment of lower limb motion is extracted, and the information of acceleration in time domain and frequency domain is used as the feature quantity of classification recognition by wavelet transform, and then the feature vector with the strongest classification ability is obtained by using particle swarm optimization algorithm for feature selection. Finally, the performance of the system is tested by using the characteristic vector to judge the normality of the action. The classification results of the system show that there are four kinds of actions (normative action, crooked leg) in the stage of leg lifting, maintaining and closing. The average recognition rate of lateral leg and jitter is above 95.9 鹵1.2%. A monitoring system of knee osteoarthritis exercise therapy based on ZigBee is designed in this paper. The system can effectively distinguish the nonstandard movement of knee osteoarthritis. The system is of high cost performance, small size and low power consumption. It has practical application value in the field of motion monitoring for knee osteoarthritis.
【學(xué)位授予單位】：福州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R684.3;TN92

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