基于ZigBee的膝骨性关节炎运动疗法监护系统设计

发布时间：2018-03-26 22:09

本文选题：膝骨性关节炎　切入点：ZigBee　出处：《福州大学》2014年硕士论文

【摘要】：随着社会的老龄化,膝骨性关节炎也越来越多的发病于中老年人群,发病率也逐年递增。此类疾病的症状一般表现为下肢关节的剧烈疼痛和下肢活动的运动功能障碍。目前虽然对膝骨性关节炎的治疗方法较多,但是都不能解决膝骨性关节炎对患者预后的功能活动带来的影响。患者在完成适量的运动疗法训练后,能使其下肢的耐力和肌力得到增强,对患者的血液循环和骨关节的活动性都有促进的作用,最为重要的是能够保持骨关节的稳定性。因此运动疗法对膝骨性关节炎具有巨大的临床应用价值。而在训练的过程中若不按一定的规范性进行运动,运动过于剧烈或者运动过轻,将对加剧患者的病情或者达不到训练的效果。因此本文将设计一基于ZigBee的膝骨性关节炎运动疗法的监护系统,对患者的动作进行规范性判别。本文的主要工作如下：(1)对人体的两种运动模型进行了分析和讨论,并针对膝骨性关节炎运动疗法的下肢运动特点提出了新的运动信息采集方案。该方案采用多个三维加速度传感器,分别固定放置在下肢小腿和大腿的质点位置。通过运动信息采集系统能有效的完成对人体下肢运动加速度信息的获取。(2)针对人体的多质点运动模型,设计了基于ZigBee的数据采集系统,对加速度信息进行采集,并依据ZigBee的终端子节点和协调器节点所需实现的具体功能进行了电路设计。(3)设计了一套针对膝骨性关节炎常见的动作规范性识别算法。该算法的主要步骤为：首先将采集的加速度信息进行去噪和滤波的预处理,对下肢运动的有效运动片段进行提取,利用小波变换将加速度的时域和频域信息作为分类识别的特征量,再利用粒子群算法进行特征选择得到分类能力最强的特征向量,最后利用特征向量对动作的规范性进行判别。(4)通过实验平台对系统性能进行测试。系统的分类结果显示,系统对抬腿、保持和收腿阶段中出现的四类动作(规范动作、曲腿、侧腿和抖动)的平均识别率都在95.9±1.2%以上。本文设计了一种基于ZigBee的膝骨性关节炎运动疗法监护系统,系统能有效的对动作的不规范性进行判别,满足监护系统的应用需求。该系统性价比高、体积小、功耗低,在针对膝骨性关节炎的运动监护领域具有实际应用的价值。
[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 卤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.
【学位授予单位】：福州大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：R684.3;TN92

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