CN114052723A - 一种基于步态特征的跌倒风险评估方法及跌倒识别装置 - Google Patents
一种基于步态特征的跌倒风险评估方法及跌倒识别装置 Download PDFInfo
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Abstract
一种基于步态特征的跌倒风险评估方法及跌倒识别装置,属于医工结合技术领域,包括如下步骤:S1、通过吉步恩获取对象仅步行时的单任务步态数据和对象边步行边执行其他任务时的多任务步态数据;S2、根据单任务步态数据,确定对象的步态变异系数;S3、根据单任务步态数据和多任务步态数据,确定对象在两种任务下的步态消耗数据;S4、根据单/双任务步态数据、步态变异系数和步态消耗数据,评估对象的跌倒风险系数。本发明首次结合单双任务步态参数、步态变异系数和双任务步态消耗与跌倒风险系数的关系,快速的通过客观量化的步态参数得到准确的跌倒风险的评估结果,能够针对老人进行前期跌倒风险评估,后期康复训练和指导治疗。
Description
技术领域
本发明属于医工结合技术领域,具体涉及一种基于步态特征的跌倒风险评估方法。
背景技术
近年来研究表明,人行走时呈现的步态特征与信息能够反映人的行为功能情况,行为功 能障碍常表现为谨慎步态、步态平衡性降低等特征。大量临床试验通过步态检测的步态参数, 如步幅、步速、步频和跨步时间变化率等反映步态异常情况,进而评估受试者的运动功能情 况,并没有将这些客观参数应用到临床跌倒风险的评估。目前,跌倒风险评估方法主要有量 表诊断法或医生根据自身经验评估法,其具有较强的主观性,会根据医生的经验不同得出不 同的诊断结果,不具有客观性。近年来研究也缺少一种方法,可以将单、双任务评估的步态 数据转化为关于个人跌倒状态的临床可操作的知识。
发明内容
为了解决上述存在的问题,本发明提出:一种基于步态特征的跌倒风险评估方法,技术 方案如下:包括如下步骤:
S1、通过吉步恩获取对象仅步行时的单任务步态数据和对象边步行边执行其他任务时 的多任务步态数据;
S2、根据单任务步态数据,确定对象的步态变异系数;
S3、根据单任务步态数据和多任务步态数据,确定对象在两种任务下的步态消耗数据;
S4、根据单/双任务步态数据、步态变异系数和步态消耗数据,评估对象的跌倒风险系 数。
进一步地,所述步骤S1中,得到的步态数据如下:
Fv:在自由行走实验下的步速;
Fo:在自由行走实验下的摆动时间;
Ab:在动物流畅性实验下的制动力;
Bl:在倒数100实验下的步幅;
Btc:在倒数100试验下的步时变异性;
自由行走即参与者按照舒适的速度进行行走;动物流畅性即参与者以舒适的速度行走的 同时边思考动物的名字并大声说出来;倒数100即参与者以舒适的速度行走的同时边从100 开始倒数,100、99、98...;
步态数据去除走路过程中的第一步和最后一步。
进一步地,所述步骤S2中,步态变异系数的研究对象是参与者的左脚多个时间点的步 态数据,
步态变异系数Atc:在动物流畅性实验下,患者行走一步对应一个时间段,患者行走若 干步对应若干个时间段,计算所述若干个时间段的标准偏差和平均值的比值,即标准偏差与 平均值的比率,用百分比表示;
公式为Atc=SD/mean*100%
SD为多时间点步态标准偏差,mean为多时间点步态平均值。
进一步地,所述步骤S3中,双任务步态消耗的研究对象是参与者一个时间点的自由行 走和动物流畅性的步态数据,FAd:在单任务自由行走和双任务动物流畅性下的步态消耗,
双任务步态消耗FAd:单任务步态值与双任务步态值的差值与单任务步态值的比率,用 百分比表示;
公式为DTC=(ST-DT)/ST*100%
ST为一个时间点单任务步态值,DT为一个时间点双任务步态值。
进一步地,所述步骤S4中,参与者在自由行走状态下的步速Fv、摆动时间Fo、参与者在动物流畅性状态下制动力Ab、步时变异系数Atc、参与者在倒数100实验中的步幅Bl、步时变异性Btc以及在双任务动物流畅性实验和单任务自由行走实验中的步态消耗作为训练集,参与者跌倒风险评估表得分CA作为标签值,通过神经网络算法建立的定量分析模型进行跌倒风险评估,
最终得出跌倒风险系数K与7个步态参数的权重矩阵为:
K=[1.45,-0.10,-1.45,6.62,-0.02,-1.24,-0.04,-0.01]
即:
跌倒风险评分CA=K*[Fv,Fo,Ab,Atc,Bl,Btc,FAd]T。
进一步地,包括一双智能鞋和5个高精度低功耗的惯性传感器模块,采集数据时参与 者穿上智能鞋和用尼龙带将惯性传感器模块分别固定在参与者的左右大腿、左右小腿和躯 干,将采集到的运动信号传输到软件***中,经过计算,输出的步态参数。
本发明的有益效果为:
本发明首次结合单双任务步态参数、步态变异系数和双任务步态消耗与跌倒风险系数的 关系,快速的通过客观量化的步态参数得到准确的跌倒风险的评估结果,能够针对60岁以 上人群进行前期跌倒风险评估,后期康复训练和指导治疗。本发明克服了之前诊断的不足, 研究了一种基于步态参数的跌倒风险的评估方法。本方法力争通过简单的步态检测快速得到 评估结果,增加临床效率,节省人力物力。而且本方法模拟现实生活中的多任务情况,计算 出双任务步态参数及步态消耗,首次融合了单任务和双任务步态参数作为临床可操作的知 识,作为跌倒风险评估的关键指标。
该方法特别适于60岁以上的老年人,跌倒风险稍高的运动障碍患者,***能够针对老 年人群进行前期跌倒风险评估,后期康复训练和指导治疗。与同领域已有产品相比,该方法 的特点是,融合单双任务步态参数、步态变异系数和双任务步态消耗,自动分析并快速得到 评估结果。
附图说明
图1为本发明的流程图;
图2为本发明的吉步恩佩戴位置图。
具体实施方式
一种基于步态特征的跌倒风险评估方法,技术方案如下:如图1所示,包括如下步骤:
S1、通过吉步恩获取对象仅步行时的单任务步态数据和对象边步行边执行其他任务时 的多任务步态数据;
S2、根据单任务步态数据,确定对象的步态变异系数;
S3、根据单任务步态数据和多任务步态数据,确定对象在两种任务下的步态消耗数据;
S4、根据单/双任务步态数据、步态变异系数和步态消耗数据,评估对象的跌倒风险系 数。
其中,所述步骤S1中,得到的步态数据如下:
Fv:在自由行走实验下的步速;
Fo:在自由行走实验下的摆动时间;
Ab:在动物流畅性实验下的制动力;
Bl:在倒数100实验下的步幅;
Btc:在倒数100试验下的步时变异性;
自由行走即参与者按照舒适的速度进行行走;动物流畅性即参与者以舒适的速度行走的 同时边思考动物的名字并大声说出来;倒数100即参与者以舒适的速度行走的同时边从100 开始倒数,100、99、98...;
步态数据去除走路过程中的第一步和最后一步。
其中,所述步骤S2中,步态变异系数的研究对象是参与者的左脚多个时间点的步态数 据,
步态变异系数Atc:在动物流畅性实验下,患者行走一步对应一个时间段,患者行走若 干步对应若干个时间段,计算所述若干个时间段的标准偏差和平均值的比值,即标准偏差与 平均值的比率,用百分比表示;
公式为Atc=SD/mean*100%
SD为多时间点步态标准偏差,mean为多时间点步态平均值。
其中,所述步骤S3中,双任务步态消耗的研究对象是参与者一个时间点的自由行走和 动物流畅性的步态数据,FAd:在单任务自由行走和双任务动物流畅性下的步态消耗,
双任务步态消耗FAd:单任务步态值与双任务步态值的差值与单任务步态值的比率,用 百分比表示;
公式为DTC=(ST-DT)/ST*100%
ST为一个时间点单任务步态值,DT为一个时间点双任务步态值。
其中,所述步骤S4中,所述步骤S4中,参与者在自由行走状态下的步速Fv、摆动时间Fo、参与者在动物流畅性状态下制动力Ab、步时变异系数Atc、参与者在倒数100实验 中的步幅Bl、步时变异性Btc以及在双任务动物流畅性实验和单任务自由行走实验中的步 态消耗作为训练集,参与者跌倒风险评估表得分CA作为标签值,通过神经网络算法建立的 定量分析模型进行跌倒风险评估,
最终得出跌倒风险系数K与7个步态参数的权重矩阵为:
K=[1.45,-0.10,-1.45,6.62,-0.02,-1.24,-0.04,-0.01]
即:
跌倒风险评分CA=K*[Fv,Fo,Ab,Atc,Bl,Btc,FAd]T。
本发明是融合单任务与双任务的步态参数快速评估跌倒风险,解决了以往研究评估跌倒 风险的主观问题。本发明的创新点:结合单、双任务步态参数、步态变异系数和任务步态消 耗与跌倒风险的相关性,快速准确的评估患者的跌倒风险系数。
其中,包括一双智能鞋和5个高精度低功耗的惯性传感器模块,采集数据时参与者穿 上智能鞋和用尼龙带将惯性传感器模块分别固定在参与者的左右大腿、左右小腿和躯干,将 采集到的运动信号传输到软件***中,经过计算,输出的步态参数。
本发明特别适于60岁以上的老年人,跌倒风险稍高的运动障碍患者,***能够针对老 年人群进行前期跌倒风险评估,后期康复训练和指导治疗。与同领域已有产品相比,该方法 的特点是,融合单双任务步态参数、步态变异系数和双任务步态消耗,自动分析并快速得到 评估结果。
其中,如图2所示,包括一双智能鞋和5个高精度低功耗的惯性传感器模块,采集数据 时参与者穿上智能鞋和用尼龙带将惯性传感器模块分别固定在参与者的左右大腿、左右小腿 和躯干,将采集到的运动信号传输到软件***中,经过计算,输出的步态参数。
以上所述,仅为本发明较佳的具体实施方式,但本发明的保护范围并不局限于此,任何 熟悉本技术领域的技术人员在本发明披露的技术范围内,根据本发明的技术方案及其构思加 以等同替换或改变,都应涵盖在本发明的保护范围之内。
Claims (6)
1.一种基于步态特征的跌倒风险评估方法,其特征在于,包括如下步骤:
S1、通过吉步恩获取对象仅步行时的单任务步态数据和对象边步行边执行其他任务时的多任务步态数据;
S2、根据单任务步态数据,确定对象的步态变异系数;
S3、根据单任务步态数据和多任务步态数据,确定对象在两种任务下的步态消耗数据;
S4、根据单/双任务步态数据、步态变异系数和步态消耗数据,评估对象的跌倒风险系数。
2.如权利要求1所述的基于步态特征的跌倒风险评估方法,其特征在于,所述步骤S1中,得到的步态数据如下:
Fv:在自由行走实验下的步速;
Fo:在自由行走实验下的摆动时间;
Ab:在动物流畅性实验下的制动力;
Bl:在倒数100实验下的步幅;
Btc:在倒数100试验下的步时变异性;
自由行走即参与者按照舒适的速度进行行走;动物流畅性即参与者以舒适的速度行走的同时边思考动物的名字并大声说出来;倒数100即参与者以舒适的速度行走的同时边从100开始倒数,100、99、98...;
步态数据去除走路过程中的第一步和最后一步。
3.如权利要求1所述的基于步态特征的跌倒风险评估方法,其特征在于,所述步骤S2中,步态变异系数的研究对象是参与者的左脚多个时间点的步态数据,
步态变异系数Atc:在动物流畅性实验下,患者行走一步对应一个时间段,患者行走若干步对应若干个时间段,计算所述若干个时间段的标准偏差和平均值的比值,即标准偏差与平均值的比率,用百分比表示;
公式为Atc=SD/mean*100%
SD为多时间点步态标准偏差,mean为多时间点步态平均值。
4.如权利要求1所述的基于步态特征的跌倒风险评估方法,其特征在于,所述步骤S3中,双任务步态消耗的研究对象是参与者一个时间点的自由行走和动物流畅性的步态数据,FAd:在单任务自由行走和双任务动物流畅性下的步态消耗,
双任务步态消耗FAd:单任务步态值与双任务步态值的差值与单任务步态值的比率,用百分比表示;
公式为DTC=(ST-DT)/ST*100%
ST为一个时间点单任务步态值,DT为一个时间点双任务步态值。
5.如权利要求1所述的基于步态特征的跌倒风险评估方法,其特征在于,所述步骤S4中,参与者在自由行走状态下的步速Fv、摆动时间Fo、参与者在动物流畅性状态下制动力Ab、步时变异系数Atc、参与者在倒数100实验中的步幅Bl、步时变异性Btc以及在双任务动物流畅性实验和单任务自由行走实验中的步态消耗作为训练集,参与者跌倒风险评估表得分CA作为标签值,通过神经网络算法建立的定量分析模型进行跌倒风险评估,
最终得出跌倒风险系数K与7个步态参数的权重矩阵为:
K=[1.45,-0.10,-1.45,6.62,-0.02,-1.24,-0.04,-0.01]
即:
跌倒风险评分CA=K*[Fv,Fo,Ab,Atc,Bl,Btc,FAd]T。
6.一种基于步态特征的跌倒识别装置,其特征在于,包括一双智能鞋和5个高精度低功耗的惯性传感器模块,采集数据时参与者穿上智能鞋和用尼龙带将惯性传感器模块分别固定在参与者的左右大腿、左右小腿和躯干,将采集到的运动信号传输到软件***中,经过对加速度数据和姿态数据进行融合,并利用四元互补滤波技术计算输出步态参数。
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