外骨骼机器人助力效能测试方法及应用综述

doi:10.12382/bgxb.2023.0637

摘要/Abstract

摘要：

外骨骼机器人在军事、医疗及工业等领域展现出广阔的应用前景,迫切需要一套科学的外骨骼助力效能测试评估体系,以实现对外骨骼研发、产品迭代及实际使用的指导与规范。为全面把握现有性能测试方法,进行CiteSpace可视化科学知识图谱分析与数据统计,围绕代谢成本评估、生物电信号评估、运动学与动力学参数评估、工况表现评估和综合评价模型5类常见评估方法进行综述,总结其关键指标与技术局限,并从评估理论深入与测试方法、候选评估集合建立方法、综合评价模型结构设计与指标赋权方法、多场景测试方法4个方面,对外骨骼效能评估技术的未来发展提出展望,对完善外骨骼评估体系、实现技术横向比较具有实际意义。

关键词: 外骨骼机器人, 助力效能测试, 代谢成本, 生物电信号, 人体生物力学, 综合评价模型

Abstract:

Exoskeleton robots have shown promising applications in military, medical and industrial fields. A scientific exoskeleton assisting effectiveness evaluation system is urgently needed to achieve guidance and specification for exoskeleton research and development, product iteration, and practical use. CiteSpace visualized scientific knowledge mapping analysis and data statistics are conducted to comprehensively grasp the existing performance evaluation methods. The current research status of 5 common assisting effectiveness evaluation methods is reviewed in detail, including metabolic cost evaluation, bioelectrical signal evaluation, kinematic and kinetic parameters evaluation, work performance evaluation, and comprehensive evaluation models, and their key indicators and technical limitations are summarized. An outlook on the development trends and research priorities of exoskeleton assisting effectiveness evaluation technology is presented from 4 aspects: evaluation theories deepening and test method, establishment method of targeted candidate evaluation sets, comprehensive evaluation model structure design and index weights assignment method, and multi-scene test method, which has practical significance to improve the exoskeleton testing system and realize technology horizontal comparison.

Key words: exoskeleton robot, assisting effectiveness test, metabolic cost, bioelectrical signal, human biomechanics, comprehensive evaluation model

中图分类号:

TP242

刘亚丽, 鲁妍池, 徐小龙, 宋遒志. 外骨骼机器人助力效能测试方法及应用综述[J]. 兵工学报, 2024, 45(8): 2497-2519.

LIU Yali, LU Yanchi, XU Xiaolong, SONG Qiuzhi. A Review of the Evaluation Methods of Assisting Effectiveness of Exoskeleton Robot and Its Applications[J]. Acta Armamentarii, 2024, 45(8): 2497-2519.

图/表 13

图1 文献检索及筛选流程图

Fig.1 Document search and screening flowchart

图2 外骨骼研究及其助力效能测试领域发文统计

Fig.2 Article volume statistics in the field of exoskeleton research and assisting effectiveness evaluation

图3 国家/研究机构合作网络图谱

Fig.3 Mapping of countries/research institutes cooperation networks

表1 国家/研究机构发文量与中介中心性(Top 10)

Table 1 Article volume and intermediary centrality of countries/research institutions (Top 10)

序号	发文量/篇	中介中心性	国家/机构
1	232	0.84	USA(美国)
2	193	0.56	China(中国)
3	81	0.33	Italy(意大利)
4	59	0.26	South Korea(韩国)
5	41	0.17	Japan(日本)
6	36	0.15	Spain(西班牙)
7	35	0.05	Switzerland(瑞士)
8	30	0.14	Germany(德国)
9	23	0.15	Canada(加拿大)
10	21	0.02	Scuola Superiore Sant'anna (圣安娜高等学院)

图4 外骨骼助力效能研究领域的文献共被引关系网络图谱

Fig.4 Co-citation network mapping in the field of exoskeleton assisting effectiveness research

图5 外骨骼机器人应用情况调研

Fig.5 Exoskeleton robot application research

图6 外骨骼机器人助力效能评估方法统计注:“有源”指装配有电机、液压等驱动元件的外骨骼,“无源”指仅装配弹力拉带、扭簧、气动肌肉等弹性储能元件且不含驱动部件的外骨骼。

Fig.6 Statistics of exoskeleton robot assisting effectiveness evaluation methods

表2 代谢成本评估指标

Table 2 Metabolic cost evaluation indices

类型		原理
	单位净呼吸数值^{[29,31,33,35 - 36]}/(mL·kg^-1·min^-1)	分别在穿戴外骨骼前后测量运动前静息、运动中与运动后静息稳定段的呼吸数值V(代指耗氧量 $V O 2$ 或二氧化碳排放量 $V C O 2$ )并截取t(t为2~10min)数据,利用V=(V_MOVE+V_AFTER-2V_PRE)/m或V=(V_MOVE-V_PRE)/m计算单位净呼吸数值变化,式中V_PRE、V_MOVE、V_AFTER分别表示运动前、运动中与运动后t稳定段的平均呼吸数值(mL/min),m表示受试者体重(kg)。通过计算受试者穿戴外骨骼前、后的单位净呼吸数值V^NONE、V^EXO,获得单位呼吸数值变化率V%=(V^EXO-V^NONE)/V^NONE×100%。
呼吸数值	单位氧气消耗(Oxygen Cost, OC)^[37]/(mL·kg^-1·m^-1)	指未穿戴、穿戴外骨骼时单位净耗氧量 $V O 2$ 与平均步行速度v的比值,即OC^NONE= $V O 2 M O V E N O N E$ / $v M O V E N O N E$ 和OC^EXO= $V O 2 M O V E E X O$ / $v M O V E E X O$ 。通过计算受试者穿戴外骨骼前后的单位OC(OC^NONE、OC^EXO),获得单位OC变化率OC%=(OC^EXO-OC^NONE)/OC^NONE×100%。
	呼吸频率(Respiratory Rate, RR)^[37-38]/(次·min^-1)	指每分钟呼吸的次数,即1min内胸部起伏的次数,可通过密切观察被检者的胸部起伏状态或利用生理监测设备获得,通常关注RR的中位数,以避免平均数带来的异常值影响。
	分钟空气通量(Minute Ventilation, MV)^[11,33,37]/(L·min^-1)	指每分钟吸入或呼出肺的气体总量,是潮气量(Tidal Volume, TV)^[39]与呼吸频率的乘积,可用于衡量受试者运动强度。正常人静息时MV约为6~8L,体力劳动或剧烈运动时可达100L。
	呼吸交换率(Respiratory Exchange Ratio, RER)^[37]	指每分钟人体二氧化碳排放量与耗氧量之比值,当RER<1时,受试者处于有氧呼吸状态;当RER≥1时,受试者处于有氧与无氧共同作用的呼吸状态。RER的增加一定程度上反映了运动强度的增大与身体供能系统呼吸底物的变化。
代谢功率	基于标准方程的单位代谢功率^{[27,31,32,34,38]}/(W·kg^-1)	基于净呼吸数值,使用Brockway标准方程^[40]推导人体净消耗功率H=a $V O 2$ +b $V C O 2$ ,可取净耗氧量、净二氧化碳排放量相关系数为a=16.58和b=4.51。随后对净代谢率值进行受试者体重m标准化,得到单位净消耗功率H_m=H/m。鉴于有氧与无氧呼吸的机理性差异,需保证受试者处于有氧呼吸状态,通过计算受试者穿戴外骨骼前、后的单位净消耗功率 $H m N O N E$ 、 $H m E X O$ ,获得代谢功率变化率H_m%=( $H m N O N E$ - $H m E X O$ )/ $H m N O N E$ ×100%。
	基于呼吸熵(Respiratory Quotient,RQ)的单位代谢功率^[41]/(W·kg^-1)	RQ指每分钟细胞二氧化碳排出量与耗氧量之比值,基于RQ的单位代谢功率计算方法与基于呼吸数值的代谢功率类似,只是改为使用H=69.7 $V O 2$ ×(1.2341RQ+3.8124)^[42⇓-44]计算人体净消耗功率H,其中,69.7W·min/kcal是将单位Kcal/min转化为W的因子。单位净消耗功率H_m与代谢功率变化率H_m%计算方法与基于呼吸数值的代谢功率一致。
	常规心率指标^[39,45]	指心率平均值(beats/min)、心率峰值(beats/min)等。
	生理成本指数(Physiological Cost Index, PCI)^[46-47]/(beats·m^-1)	指运动前静息和运动中最后t(t为2~10min)的平均心率HR_PRE、HR_MOVE之差与运动总时长T内的平均速度v_MOVE(m/min)的比值,即PCI=(HR_MOVE-HR_PRE)/v_MOVE。
心率	心率变异性(Heart Rate Variability, HRV)^[38]	表征测量心率变化的不规律性,其降低与精神疲劳、心理压力的增加存在一定相关性。通常测量心电图R波时间间隔,使用时域、频域与非线性分析指标描述。以常用时域特征平均正态区间标准差(Standard Deviation of Normal-to-Normal Intervals, SDNN)为例,其定义为一定时间窗口内已移除伪影的心率间隔的标准差,通过计算运动中SDNN_MOVE与运动前静息SDNN_PRE,获得SDNN变化率SDNN%=(SDNN_MOVE-SDNN_PRE)/SDNN_PRE×100%。
	心率储备率(Kaevonen Method, KM)^[33,37]	美国运动医学会指出,运动强度的设定不能单纯以最大峰值心率进行表征,需考虑个体间差异性,因此使用KM(又称卡弗内方法)反映心率变化较为合理。通过测量运动中平均心率、运动前平均静息心率及运动中最大心率HR_MOVE、HR_PRE、HR_max,获得心率储备KM=(HR_MOVE-HR_PRE)/(HR_max-HR_PRE)×100%。

表2 代谢成本评估指标

Table 2 Metabolic cost evaluation indices

类型		原理
	单位净呼吸数值^{[29,31,33,35 - 36]}/(mL·kg^-1·min^-1)	分别在穿戴外骨骼前后测量运动前静息、运动中与运动后静息稳定段的呼吸数值V(代指耗氧量 $V O 2$ 或二氧化碳排放量 $V C O 2$ )并截取t(t为2~10min)数据,利用V=(V_MOVE+V_AFTER-2V_PRE)/m或V=(V_MOVE-V_PRE)/m计算单位净呼吸数值变化,式中V_PRE、V_MOVE、V_AFTER分别表示运动前、运动中与运动后t稳定段的平均呼吸数值(mL/min),m表示受试者体重(kg)。通过计算受试者穿戴外骨骼前、后的单位净呼吸数值V^NONE、V^EXO,获得单位呼吸数值变化率V%=(V^EXO-V^NONE)/V^NONE×100%。
呼吸数值	单位氧气消耗(Oxygen Cost, OC)^[37]/(mL·kg^-1·m^-1)	指未穿戴、穿戴外骨骼时单位净耗氧量 $V O 2$ 与平均步行速度v的比值,即OC^NONE= $V O 2 M O V E N O N E$ / $v M O V E N O N E$ 和OC^EXO= $V O 2 M O V E E X O$ / $v M O V E E X O$ 。通过计算受试者穿戴外骨骼前后的单位OC(OC^NONE、OC^EXO),获得单位OC变化率OC%=(OC^EXO-OC^NONE)/OC^NONE×100%。
	呼吸频率(Respiratory Rate, RR)^[37-38]/(次·min^-1)	指每分钟呼吸的次数,即1min内胸部起伏的次数,可通过密切观察被检者的胸部起伏状态或利用生理监测设备获得,通常关注RR的中位数,以避免平均数带来的异常值影响。
	分钟空气通量(Minute Ventilation, MV)^[11,33,37]/(L·min^-1)	指每分钟吸入或呼出肺的气体总量,是潮气量(Tidal Volume, TV)^[39]与呼吸频率的乘积,可用于衡量受试者运动强度。正常人静息时MV约为6~8L,体力劳动或剧烈运动时可达100L。
	呼吸交换率(Respiratory Exchange Ratio, RER)^[37]	指每分钟人体二氧化碳排放量与耗氧量之比值,当RER<1时,受试者处于有氧呼吸状态;当RER≥1时,受试者处于有氧与无氧共同作用的呼吸状态。RER的增加一定程度上反映了运动强度的增大与身体供能系统呼吸底物的变化。
代谢功率	基于标准方程的单位代谢功率^{[27,31,32,34,38]}/(W·kg^-1)	基于净呼吸数值,使用Brockway标准方程^[40]推导人体净消耗功率H=a $V O 2$ +b $V C O 2$ ,可取净耗氧量、净二氧化碳排放量相关系数为a=16.58和b=4.51。随后对净代谢率值进行受试者体重m标准化,得到单位净消耗功率H_m=H/m。鉴于有氧与无氧呼吸的机理性差异,需保证受试者处于有氧呼吸状态,通过计算受试者穿戴外骨骼前、后的单位净消耗功率 $H m N O N E$ 、 $H m E X O$ ,获得代谢功率变化率H_m%=( $H m N O N E$ - $H m E X O$ )/ $H m N O N E$ ×100%。
	基于呼吸熵(Respiratory Quotient,RQ)的单位代谢功率^[41]/(W·kg^-1)	RQ指每分钟细胞二氧化碳排出量与耗氧量之比值,基于RQ的单位代谢功率计算方法与基于呼吸数值的代谢功率类似,只是改为使用H=69.7 $V O 2$ ×(1.2341RQ+3.8124)^[42⇓-44]计算人体净消耗功率H,其中,69.7W·min/kcal是将单位Kcal/min转化为W的因子。单位净消耗功率H_m与代谢功率变化率H_m%计算方法与基于呼吸数值的代谢功率一致。
	常规心率指标^[39,45]	指心率平均值(beats/min)、心率峰值(beats/min)等。
	生理成本指数(Physiological Cost Index, PCI)^[46-47]/(beats·m^-1)	指运动前静息和运动中最后t(t为2~10min)的平均心率HR_PRE、HR_MOVE之差与运动总时长T内的平均速度v_MOVE(m/min)的比值,即PCI=(HR_MOVE-HR_PRE)/v_MOVE。
心率	心率变异性(Heart Rate Variability, HRV)^[38]	表征测量心率变化的不规律性,其降低与精神疲劳、心理压力的增加存在一定相关性。通常测量心电图R波时间间隔,使用时域、频域与非线性分析指标描述。以常用时域特征平均正态区间标准差(Standard Deviation of Normal-to-Normal Intervals, SDNN)为例,其定义为一定时间窗口内已移除伪影的心率间隔的标准差,通过计算运动中SDNN_MOVE与运动前静息SDNN_PRE,获得SDNN变化率SDNN%=(SDNN_MOVE-SDNN_PRE)/SDNN_PRE×100%。
	心率储备率(Kaevonen Method, KM)^[33,37]	美国运动医学会指出,运动强度的设定不能单纯以最大峰值心率进行表征,需考虑个体间差异性,因此使用KM(又称卡弗内方法)反映心率变化较为合理。通过测量运动中平均心率、运动前平均静息心率及运动中最大心率HR_MOVE、HR_PRE、HR_max,获得心率储备KM=(HR_MOVE-HR_PRE)/(HR_max-HR_PRE)×100%。

表3 生物电信号评估指标及肌肉疲劳特征

Table 3 Bioelectrical signal assessment indices and muscle fatigue characteristics

类型		原理
	肌电幅值^[59-60]/mV	直接比较sEMG信号线性包络曲线的振幅,反映肌肉激活度大小、肌肉收缩强度、峰值大小及位置。
	肌电直方图^[61]	由于肌肉高度收缩时sEMG信号偏离其基线,故提取不同幅值的sEMG采样个数作为信号特征。设定阈值并将正负阈值间距离等分成不同幅值段,检测各幅值段的信号个数,阈值水平和分段数可根据实验而定,反映研究肌肉收缩特征及变化规律。
	iEMG值^{[59,62⇓ -64]}/mV	指一定时间窗口内肌肉参与活动的运动单位放电总量,由离散sEMG信号的绝对值之和取平均求得,在中等负荷范围内,iEMG伴随肌力的增加和疲劳的产生而增长,反映肌肉激活程度。iEMG值计算公式为iEMG=( $∑ i = 1 n$ \|x_i\|)/n。
时域	RMS^{[38,57,64⇓⇓⇓⇓ -69]}/mV	指一定时间窗口内肌电幅值平方和均值的平方根,在中等负荷范围内,iEMG伴随肌力的增加和疲劳的产生而增长,反映肌肉活动强弱。肌电RMS计算公式为RMS= $(∑ i = 1 n x i 2) / n$ ,可结合多运动周期RMS的峰值或标准差进行助力效能评估。
	最大自主等长收缩(Maximal Voluntary Isometric Contraction, MVIC)^[70]/mV	指受试者进行抗阻运动时认为输出最高努力水平的肌电值,一般由RMS平滑滤波后取最值获得,通过比较进行最大自主收缩时目标肌肉原始肌电信号的变化,反映肌肉爆发力及疲劳情况。
	肌肉发力强度(Percentage of Maximal Voluntary Isometric Contraction, MVIC%)^{[33,58,60,71⇓⇓⇓⇓⇓⇓ -78]}	指一定时间窗口内归一化的肌电RMS,通常使用最大自主收缩肌电值(或多周期肌电RMS均值)作为归一化分母,反映肌肉产能情况。肌肉发力强度计算公式为MVIC%=RMS/MVIC×100%。可通过交叉相关分析,确定不同条件下sEMG信号在时域曲线形状上的相似性,相关系数计算方法为$R=\left(\sum M V I C \%_{i}^{\mathrm{EXO}} \cdot M V I C \%_{i}^{\mathrm{NONE}}\right) /\left[\left(\sum M^{2} I C \%_{i}^{\text {EXO }}\right)^{1 / 2}\left(\sum M V I C \%_{i}^{\text {NONE }}\right)^{1 / 2}\right], M V I C \%_{i}^{\text {EXO }} 、 M V I C \%_{i}^{\mathrm{NONE}}$分别为穿戴、未穿戴外骨骼时长度为n的时间窗口内第i个MVIC%数值。
	Welch谱估计方法^[64]	指一种用于估计随机信号PSD的改进平均周期图法,可通过每个频率成分处的功率谱值占总功率的百分比形式,表示外骨骼助力前后肌电信号的功率幅度变化。
	中位频率(Median Frequency, MF)^[79]/Hz	指肌肉收缩放电频率的中值,在静态收缩中,MF伴随运动时长增加而递减、伴随肌力的增加而增长,且受骨骼肌快慢肌纤维组成比例的影响,快肌纤维兴奋表现为高频放电,慢肌纤维则在低频,具有抗噪声干扰性强、适应负荷范围较广的特点。MF率计算公式为MF=0.5 $∫ 0 ∞$ PSD(f)df。
频域	平均功率频率(Mean Power Frequency, MPF)^[79]/Hz	指肌肉收缩放电频率的平均值,在静态收缩中,MPF伴随疲劳的产生而降低,且对于低负荷条件下的频谱变化高度敏感。MPF计算公式为 $∫ 0 ∞$ fPSDMPF $∫ 0 ∞$ fPSD=(f)df/ $∫ 0 ∞$ fPSD(f)df。
	FI指数^[79]	指通过对比不同频率能量的比例,评估肌电功率谱的偏移情况,FI指数伴随疲劳的产生而增加,理论上可取得比MPF和MF更高的灵敏度。FI指数计算公式为 $∫ 0 ∞$ fFI $∫ 0 ∞$ f=^-1PSD(f)df/ $∫ 0 ∞ f k c$ PSD(f)df,分子部分强调疲劳过程中功率谱低频成分的增多,分母部分强调功率谱高频成分的减少。

表3 生物电信号评估指标及肌肉疲劳特征

Table 3 Bioelectrical signal assessment indices and muscle fatigue characteristics

类型		原理
	肌电幅值^[59-60]/mV	直接比较sEMG信号线性包络曲线的振幅,反映肌肉激活度大小、肌肉收缩强度、峰值大小及位置。
	肌电直方图^[61]	由于肌肉高度收缩时sEMG信号偏离其基线,故提取不同幅值的sEMG采样个数作为信号特征。设定阈值并将正负阈值间距离等分成不同幅值段,检测各幅值段的信号个数,阈值水平和分段数可根据实验而定,反映研究肌肉收缩特征及变化规律。
	iEMG值^{[59,62⇓ -64]}/mV	指一定时间窗口内肌肉参与活动的运动单位放电总量,由离散sEMG信号的绝对值之和取平均求得,在中等负荷范围内,iEMG伴随肌力的增加和疲劳的产生而增长,反映肌肉激活程度。iEMG值计算公式为iEMG=( $∑ i = 1 n$ \|x_i\|)/n。
时域	RMS^{[38,57,64⇓⇓⇓⇓ -69]}/mV	指一定时间窗口内肌电幅值平方和均值的平方根,在中等负荷范围内,iEMG伴随肌力的增加和疲劳的产生而增长,反映肌肉活动强弱。肌电RMS计算公式为RMS= $(∑ i = 1 n x i 2) / n$ ,可结合多运动周期RMS的峰值或标准差进行助力效能评估。
	最大自主等长收缩(Maximal Voluntary Isometric Contraction, MVIC)^[70]/mV	指受试者进行抗阻运动时认为输出最高努力水平的肌电值,一般由RMS平滑滤波后取最值获得,通过比较进行最大自主收缩时目标肌肉原始肌电信号的变化,反映肌肉爆发力及疲劳情况。
	肌肉发力强度(Percentage of Maximal Voluntary Isometric Contraction, MVIC%)^{[33,58,60,71⇓⇓⇓⇓⇓⇓ -78]}	指一定时间窗口内归一化的肌电RMS,通常使用最大自主收缩肌电值(或多周期肌电RMS均值)作为归一化分母,反映肌肉产能情况。肌肉发力强度计算公式为MVIC%=RMS/MVIC×100%。可通过交叉相关分析,确定不同条件下sEMG信号在时域曲线形状上的相似性,相关系数计算方法为$R=\left(\sum M V I C \%_{i}^{\mathrm{EXO}} \cdot M V I C \%_{i}^{\mathrm{NONE}}\right) /\left[\left(\sum M^{2} I C \%_{i}^{\text {EXO }}\right)^{1 / 2}\left(\sum M V I C \%_{i}^{\text {NONE }}\right)^{1 / 2}\right], M V I C \%_{i}^{\text {EXO }} 、 M V I C \%_{i}^{\mathrm{NONE}}$分别为穿戴、未穿戴外骨骼时长度为n的时间窗口内第i个MVIC%数值。
	Welch谱估计方法^[64]	指一种用于估计随机信号PSD的改进平均周期图法,可通过每个频率成分处的功率谱值占总功率的百分比形式,表示外骨骼助力前后肌电信号的功率幅度变化。
	中位频率(Median Frequency, MF)^[79]/Hz	指肌肉收缩放电频率的中值,在静态收缩中,MF伴随运动时长增加而递减、伴随肌力的增加而增长,且受骨骼肌快慢肌纤维组成比例的影响,快肌纤维兴奋表现为高频放电,慢肌纤维则在低频,具有抗噪声干扰性强、适应负荷范围较广的特点。MF率计算公式为MF=0.5 $∫ 0 ∞$ PSD(f)df。
频域	平均功率频率(Mean Power Frequency, MPF)^[79]/Hz	指肌肉收缩放电频率的平均值,在静态收缩中,MPF伴随疲劳的产生而降低,且对于低负荷条件下的频谱变化高度敏感。MPF计算公式为 $∫ 0 ∞$ fPSDMPF $∫ 0 ∞$ fPSD=(f)df/ $∫ 0 ∞$ fPSD(f)df。
	FI指数^[79]	指通过对比不同频率能量的比例,评估肌电功率谱的偏移情况,FI指数伴随疲劳的产生而增加,理论上可取得比MPF和MF更高的灵敏度。FI指数计算公式为 $∫ 0 ∞$ fFI $∫ 0 ∞$ f=^-1PSD(f)df/ $∫ 0 ∞ f k c$ PSD(f)df,分子部分强调疲劳过程中功率谱低频成分的增多,分母部分强调功率谱高频成分的减少。

表4 运动学及动力学评估指标

Table 4 Kinematic and kinetic assessment indices

类型		指标
	关节运动学参数	关节活动范围(Range of Motion, ROM)^{[63,81⇓ -83]}、关节轨迹^{[82,84 -85]}、关节角度^{[33,65,72,75,82,84,86⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓ -97]}、关节角速度^[82,96]、关节角加速度^[82]等
运动学	人体运动中心	质心(Center of Mass, COM)^[69,97]、重心(Center of Gravity, COG)^[98]、躯干旋转角度(Angle of Trunk Rotation, ATR)^[99]等
	基本步态参数	步长^{[63,81,83,100⇓⇓⇓⇓ -105]}、步宽^[103⇓-105]、步态周期时间/频率^{[90,92,100,104]}、步速^{[34,90 -91,93,104,106⇓ -108]}、快速步态速度(Fast Gait Speed, FGS)^[109]、步态节奏^[76,104]、步数^[110-111]等
	描述性步态参数	步长不对称性^[76,101,109]、步速对称性^[104]、摆动相不对称性^[101]、步态变异性^[83]、摆动相占比^[11]、支撑相占比^{[11,83,104 -105]}等
	人机交互力	足底压力^{[36,99,112⇓⇓⇓⇓ -117]}、中心压力(Center of Pressure, COP)^{[58,118 -119]}等
动力学	关节动力学参数	关节力矩^{[59,67,69,82,84,87⇓ -89,94 -95,103,108,118,120⇓ -122]}、关节功率^[32,59]、关节做功^[32,59,123]等
	执行器输出	产生力矩^[72,123]、辅助力矩^{[34,35,57,123]}、电机电流/转速/输出功率^[57,96]等

表5 工况表现测试方法

Table 5 Work performance evaluation methods

类型		测试协议
重复性运动任务	下肢	6min步行测试(6-Minute Walk Test, 6MWT)^{[45⇓-47,75,83,87,107,111,132⇓⇓⇓⇓ -137]}、10m步行测试(10-Meter Walk Test, 10MWT)^{[46-47,83,107,110 -111,132⇓⇓⇓⇓⇓⇓⇓⇓ -141]}、自选行走速度(Self-Selected Walking Speed, SWS)^{[59,65,75,142]}、负重移动测试^[33]、坡地测试^[36]、爬梯测试^[36]等
	上肢	静态举重测试^[143]等
模式切换任务	下肢	定时起行运动(Timed-Up-and-Go,TUG)^{[47,83,87,110 -111,135⇓⇓ -138,141]}、坐-站运动(Sit-to-Stand, STS)^[65,83]、站-坐-站运动(Stand-Sit-Stand, SSS)^[108,142]、蹲起运动测试^[59]、蹲起举重测试^[33,58]等
	上肢	方盒方块测试^[60,144]、肘部屈曲运动^[145]、肘肩屈曲运动^[145]、多伦多康复研究所手功能测试(Toronto Rehabilitation Institute Hand Function Test, TRI-HFT)^[146]等
其他特定任务		穿卸测试^[136,147]、动/静态平衡测试^{[69,88,97,148]}、转身测试^[125,147]、扁石越障任务^[108]、越障迂回步行^[108]、模拟工程作业^[77,119]、军事越障巡逻任务^[149]、日常生活任务^[46]等

表6 常用康复外骨骼辅助效果评估量表

Table 6 Commonly used rehabilitation exoskeleton assisting effectiveness evaluation scales

类型	量表
行走功能测评	功能性运动分类(Functional Ambulatory Category, FAC)^[134]、SCI步行指数Ⅱ(Walking Index for SCI Version Ⅱ, WISCI II)^{[75,110 -111,132,137,140]}、Rivermead运动指数(Rivermead Mobility Index, RMI)^[134,141]、下肢运动评分(Lower Extremity Motor Score, LEMS)^[110,137]等
肢体平衡功能测评	伯格平衡量(Berg Balance Scale, BBS)表^{[135,139,158]}等
下肢痉挛程度测评	脊髓痉挛评估工具(Spinal Cord Assessment Tool for Spasticity, SCATS)^[46]等
神经性疼痛程度测评	疼痛评估量(Pain Scales Evaluation, PSE)表^[143]等

表7 常用主观感受评价量表

Table 7 Commonly used subjective perception evaluation scales

类型	量表	原理
认知努力与疲劳程度测评	自感用力度(Rating of Perceived Exertion, RPE) ^[77,147]	采用10分制Likert型量表对认知工作量进行评分,将运动程度定级为轻微运动(1分)、轻度运动(2~3分)、中度运动(4~6分)、剧烈运动(7~8分)、超剧烈运动(9分)和最大努力运动(10分),用于评估外骨骼用户主观努力付出,具有较高可靠性,但缺乏条目内容有效性的检验。
	美国航空航天局任务负荷指数(NASA-Task Load Index, NASA-TLX)^{[149,159⇓⇓ -162]}	采用10分制Likert型量表对心理需求、生理需求、时间需求、表现绩效、努力程度和受挫程度6个项目进行评分,用于衡量外骨骼用户在执行任务时的主观努力程度,其基本模型概念定义明确,Cronbach's α分析显示条目内容一致性与可靠性良好。
	主观工作量评估技术(Subjective Workload Assessment Technique, SWAT)^[162]	由时间负荷、心理努力负荷和心理压力负荷3项问卷组成,用于评估外骨骼使用过程中的认知工作量,其基本模型概念定义明确,Cronbach's α分析显示条目内容一致性与可靠性良好。
可用性与舒适度测评	视觉模拟量(Visual Analog Scale, VAS)表^[145]	采用10分制Likert型量表进行评分,通过可视化的彩色表情图标反映感知疼痛,并将其定级为无疼痛(0分)、轻度(1~3分)、中度(4~6分)、重度(7~9分)和超重度(10分),用于评估个体的主观疼痛感受,侧面反映外骨骼可用性。
	SUS表^{[60,144,163 -164]}	采用5分制Likert型量表对10项一维心理测评问卷进行评分,包括奇数项的正面陈述与偶数项的负面陈述,用于外骨骼等人机交互系统用户进行快速可用性评估。
	自我评估模型(Self-Assessment Manikin, SAM) ^[165]	采用非语义的图形评估技术对愉悦度、唤醒度与受控度三组图标进行9分制评分,用于衡量外骨骼用户的情绪反馈,具有测量简便直观的优点,但缺乏结果可靠性的检验且未提供评估程序描述。
	魁北克辅助技术满意度评估2.0(Quebec User Evaluation of Satisfaction with Assistive Technology 2.0, QUEST 2.0)^{[60,146,164 -165]}	采用5分制Likert型量表对12项二维心理测量问卷进行评分,包含尺寸、重量、安全性、耐用性、操作易用性和舒适性等8个辅助设备项目以及设备维修、设备售后等4个设备服务项目,1~5分依序表示非常不满意到非常满意,用于评估外骨骼等辅助设备特定功能及相关服务的用户满意度,Cronbach's α分析显示条目内容一致性与可靠性良好。
	AttrakDiff量表^[163]	采用7分制Likert型量表对28项三维心理测量问卷进行评分,包括设备实用性、娱乐性及吸引力3个维度,用于评衡量外骨骼等人机交互系统的主观易用性与可接受程度。Cronbach's α分析显示条目内容可靠性良好,且支持同时评估多个外骨骼产品或一个产品的多个改进版本,但缺乏有效性的检验。

表7 常用主观感受评价量表

Table 7 Commonly used subjective perception evaluation scales

类型	量表	原理
认知努力与疲劳程度测评	自感用力度(Rating of Perceived Exertion, RPE) ^[77,147]	采用10分制Likert型量表对认知工作量进行评分,将运动程度定级为轻微运动(1分)、轻度运动(2~3分)、中度运动(4~6分)、剧烈运动(7~8分)、超剧烈运动(9分)和最大努力运动(10分),用于评估外骨骼用户主观努力付出,具有较高可靠性,但缺乏条目内容有效性的检验。
	美国航空航天局任务负荷指数(NASA-Task Load Index, NASA-TLX)^{[149,159⇓⇓ -162]}	采用10分制Likert型量表对心理需求、生理需求、时间需求、表现绩效、努力程度和受挫程度6个项目进行评分,用于衡量外骨骼用户在执行任务时的主观努力程度,其基本模型概念定义明确,Cronbach's α分析显示条目内容一致性与可靠性良好。
	主观工作量评估技术(Subjective Workload Assessment Technique, SWAT)^[162]	由时间负荷、心理努力负荷和心理压力负荷3项问卷组成,用于评估外骨骼使用过程中的认知工作量,其基本模型概念定义明确,Cronbach's α分析显示条目内容一致性与可靠性良好。
可用性与舒适度测评	视觉模拟量(Visual Analog Scale, VAS)表^[145]	采用10分制Likert型量表进行评分,通过可视化的彩色表情图标反映感知疼痛,并将其定级为无疼痛(0分)、轻度(1~3分)、中度(4~6分)、重度(7~9分)和超重度(10分),用于评估个体的主观疼痛感受,侧面反映外骨骼可用性。
	SUS表^{[60,144,163 -164]}	采用5分制Likert型量表对10项一维心理测评问卷进行评分,包括奇数项的正面陈述与偶数项的负面陈述,用于外骨骼等人机交互系统用户进行快速可用性评估。
	自我评估模型(Self-Assessment Manikin, SAM) ^[165]	采用非语义的图形评估技术对愉悦度、唤醒度与受控度三组图标进行9分制评分,用于衡量外骨骼用户的情绪反馈,具有测量简便直观的优点,但缺乏结果可靠性的检验且未提供评估程序描述。
	魁北克辅助技术满意度评估2.0(Quebec User Evaluation of Satisfaction with Assistive Technology 2.0, QUEST 2.0)^{[60,146,164 -165]}	采用5分制Likert型量表对12项二维心理测量问卷进行评分,包含尺寸、重量、安全性、耐用性、操作易用性和舒适性等8个辅助设备项目以及设备维修、设备售后等4个设备服务项目,1~5分依序表示非常不满意到非常满意,用于评估外骨骼等辅助设备特定功能及相关服务的用户满意度,Cronbach's α分析显示条目内容一致性与可靠性良好。
	AttrakDiff量表^[163]	采用7分制Likert型量表对28项三维心理测量问卷进行评分,包括设备实用性、娱乐性及吸引力3个维度,用于评衡量外骨骼等人机交互系统的主观易用性与可接受程度。Cronbach's α分析显示条目内容可靠性良好,且支持同时评估多个外骨骼产品或一个产品的多个改进版本,但缺乏有效性的检验。

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