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老年人日常步态监测与
跌倒风险评估
机械24 郝鸣
指导老师:陈 恳 教授
付成龙 副教授
2016年3月3日
1
CONTENT
目录
2
• 社会背景
• 研究意义
• 研究现状综述
• 原理分析
• 研究方案
• 工作计划
• 参考文献
社会背景
• 跌倒是老龄化社会面临的一项主要公共卫生问题
65 岁以上的老年人每年约有1/3 会发生跌倒
• 在我国,每年至少有2000万老年人发生约2500万次跌
倒
直接医疗费用在50 亿元人民币以上
社会代价约为160亿~800亿元人民币
• 预防老年人跌倒
准确的预测这部分高危人群
对这部分人群进行预防和干预
• 步态分析是一种非常有效的预测手段
可以有效预测慢性行走疾病
3
研究意义
• 传统步态分析工具存在不足
动作捕捉设备 —— 数据量小、精度高、成本高
医生现场进行观测 —— 依赖经验、无法横向对比
• 测量结果不准确
测试环境与生活环境相差甚远——测试时用“更好的”步态
存在实验者效应(意识到自己正在被别人观察的个人具有改变自
己行为的倾向)
4
动作捕捉:红外传感器+计算机视觉 计时起立-走测试:需要医生现场观测
研究意义
IMU应用于步态分析
• 惯性测量单元(Inertial Measurement Unit, IMU)
三轴陀螺仪+三轴加速度计+(三轴磁强计)
固定于身体各个部分,测量运动信息
具有可携带——应用于实验室外,监测日常生活
低功耗——长期监测
5
荷兰Xsens:MTx
美国YEI:YEI-BT 英国BAE: SiIMU01
医学领域
• 步态参数:
空间参数(步长、步宽、步高等)
时间参数(单足支撑时间、双足支撑时间等)
• 主要方法
实验设计:传统手段测量步态参数
问卷/观察:标定高危人群
统计方法:建立高危人群与步态参数的关系
• 研究表明,步宽与双足支撑时间与行走稳定性的关
系更明显
• 步长、步宽的变异系数( 标准偏差 / 平均值)可以
作为评估行走稳定性的重要指标(400步以上)
6
研究现状
室内定位
• 2005年,美国Foxlin制作出的导航鞋(NavShoe),IMU+GPS
• 2007年,美国密歇根大学的Lauro Ojeda等人使用了SiIMU01制
作了无GPS的个人导航推算系统
(No-GPS Personal Dead-reckoning System)
7
研究现状
步态测量
• 2008年,美国的Bamberg等人制作了步态鞋
(GaitShoe),用于帕金森综合症患者
只考虑了侧视平面内的运动,长度误差达到10cm量级
提出了长期监测概念
8
研究现状
• 2010年,瑞士的Mariani等人使用了小型的S-
Sense
测量步长、步高、转交、触地时间
测量了U形、8形轨迹的实验,动捕设备对比结果
与动捕设备相比,步长相差1.5±6.8 cm
9
研究现状
• 2012年,美国密歇根大学的John Rebula等人,
应用了更小的nIMU
测量步长、步宽
与动捕输数据对比 1±4%
10
研究现状
IMU固定于腰上
• 可以只通过一个IMU同时测量两只脚的行走参数
• 监测跌倒:国内学者Ge Wu,Guoru Zhao等人
11
研究现状
将脚作为刚体运动处理
• 步态划分
摆动相、支撑相
• 姿态求解
对角速度积分
求解旋转矩阵
• 速度估计
加速度坐标变换
积分求解速度
• 求解轨迹
对速度积分求解位移
12
原理分析
• 惯性元件漂移——直接积分误差随时间无限增大
• 匀速行走最大加速度3g~5g,姿态求解不宜使用加速度计+卡尔
曼滤波校正
• 脚在支撑相期间的速度为零
• ZUPT(Zero Velocity Update Technology,零速率校正)
 将积分区间控制在每一步内,从而遏制误差的无限增长
 利用边界条件,校正速度曲线
13
原理分析
• 目标1:提高精度:
改变固定位置
 影响步态划分
 减小晃动
 减少积分区间
利用ZUPT算法修正
在腰部增加一个IMU
 利用人的模型特点
 接近质心,反映人的质心运动
14
研究方案
• 目标2:识别运动状态
识别生活中主要的运动状态:行走、跑步、走斜坡、
上下楼梯等等
去除不需要的信息:坐着、开车、骑自行车等
实现对日常生活的监测
15
研究方案
16
研究方案
系统设计
上传至云端
• 目前进展
完成IMU的接口程序编写
做了一些简单实验
• 工作计划表
17
工作计划
时间 任务
3月 测量数据,观察数据特点;
初步编写计算算法。
4月 进行简单测试,优化算法;
设计实验。
5月 进行试验,对算法进行完善;
开始撰写论文。
6月 论文完善
[1] 杨波, 付成龙. 惯性测量单元在老年人跌倒风险评估中的应用进展[J]. 中国康复理论与实践, 2015(7):780-784.
[2] Allet L, Armand S, Bie R ,A D, et al. Gait alterations of diabetic patients while walking on different surfaces.[J]. Gait & Posture, 2009,
29(9):488–493
[3] Brach J S, Berlin J E, Vanswearingen J M, et al. Too much or too little step width variability is associated with a fall history in older persons who
walk at or near normal gait speed[J]. Journal of Neuroengineering & Rehabilitation, 2005, 2(1):1-8.
[4] Barak Y, Wagenaar R C, Holt K G. Gait characteristics of elderly people with a history of falls: a dynamic approach.[J]. Physical Therapy, 2006,
86(86):1501-10.
[5] Gabell A ,, Nayak U S. The Effect of Age on Variability in Gait[J]. Journal of Gerontology, 1984, 39(6):662-6.
[6] Owings T M, Grabiner M D. Measuring step kinematic variability on an instrumented treadmill: how many steps are enough?[J]. Journal of
Biomechanics, 2003, 36(8):1215-1218.
[7] Shirley S. WANG. From Athletes to the Elderly: The Science of Trips and Falls [N]. The WALL STREET JOURNAL, 2013-9-23
[8] Foxlin E. Pedestrian Tracking with shoe-mounted inertial sensors.[J]. IEEE Computer Graphics & Applications, 2005, 25(6):38-46.
[9] Ojeda L, Borenstein J. Non-GPS navigation for security personnel and first responders[J]. Journal of Navigation, 2007, 60(03):391-407.
[10] Bamberg S J M, Benbasat A Y, Donna Moxley S, et al. Gait analysis using a shoe-integrated wireless sensor system.[J]. IEEE Transactions on
Information Technology in Biomedicine A Publication of the IEEE Engineering in Medicine & Biology Society, 2008, 12(4):413 - 423.
[11] Mariani B, Hoskovec C, Rochat S, et al. 3D gait assessment in young and elderly subjects using foot-worn inertial sensors.[J]. Journal of
Biomechanics, 2010, 43(15):2999-3006.
[12] Rebula J R, Ojeda L V, Adamczyk P G, et al. Measurement of foot placement and its variability with inertial sensors ☆[J]. Gait & Posture, 2013,
38(4):974-980.
[13] Kose A, Cereatti A, Della C U. Estimation of traversed distance in level walking using a single inertial measurement unit attached to the
waist.[C]// Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE
Engineering in Medicine and Biology Society. Annual Conference. Conf Proc IEEE Eng Med Biol Soc, 2011:1125-1128.
[14] González R C, López A M, Rodriguez-Uría J, et al. Real-time gait event detection for normal subjects from lower trunk accelerations [J]. Gait
& Posture, 2010, 31(3):322-325.
[15] Ge W, Xue S. Portable preimpact fall detector with inertial sensors.[J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering A
Publication of the IEEE Engineering in Medicine & Biology Society, 2008, 16(2):178-83.
[16] Zhao, Guoru, Mei, et al. Exploration and Implementation of a Pre-Impact Fall Recognition Method Based on an Inertial Body Sensor
Network[J]. Sensors, 2012, 12(11):15338-15355.
18
参考文献
欢迎老师同学们批评指正
19
谢谢!

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毕设开题报告_郝鸣_20160301.pptx

Notes de l'éditeur

  1. 老师们,同学们,大家好! 我是郝鸣,来自机械24班 本次毕设,我的题目是xxx 我的指导老师是xxx
  2. 这是本次答辩的目录…分别从社会背景….工作计划介绍我的毕业设计
  3. 动捕设备,能在短距离内得到高精度量化数据,精度达到亚毫米级,目前认为在动作分析中精度最高 TUG,平衡测试、托马斯跌倒量表等 因此临床上急需一种长时间、定量、可重复、稳定的步态分析方法,能够准确而测量老年人日常的步态信息。
  4. 商业化的IMU,内部集成好了低通滤波器、卡尔曼滤波、姿态结算等算法
  5. 这部分调研是为了搞清需要测的参数, 步长、步宽是重要的参考量
  6. 早期的测量步长、步宽的研究来源于室内定位 使用了高精的的SiIMU,可以达到军用级别
  7. 与动捕测量结果进行了对比
  8. 步态划分,主要是设置阈值来判断
  9. 前7篇是医学文献 后9篇是关于IMU的文献
  10. 我的展示到此结束 谢谢大家 欢迎老师同学们批评指正