Assessment of over-pronated/over-supinated foot using foot-motion measured by an in-shoe motion sensor

被引:1
|
作者
Huang, Chenhui [1 ]
Wang, Zhenwei [1 ]
Fukushi, Kenichiro [1 ]
Nihey, Fumivuki [1 ]
Kajitani, Hiroshi [1 ]
Nakahara, Kentaro [1 ]
机构
[1] NEC Corp Ltd, Biometr Res Labs, Abiko, Chiba, Japan
来源
2021 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (IEEE BIOCAS 2021) | 2021年
关键词
gait analysis; over-pronation; foot-motion; in-shoe motion sensor; machine learning; ASSOCIATION; KINEMATICS; PLANUS;
D O I
10.1109/BIOCAS49922.2021.9644969
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Over-pronated foot and over-supinated foot are deviations from normal foot function recognized as risk factors for shin splints, knee pain, etc. Foot function has conventionally been assessed by the center of pressure excursion index (CPEI), but the measurement systems for this are complicated and expensive. We aim to establish a simpler method for foot function assessment using foot-motion measured by an in-shoe motion sensor (IMS) system. A total of 72 subjects (36 males, 36 females) participated in our study. We measured their CPEIs and foot-motions during straight-path walking at comfortable and faster speeds and analyzed their relationships by the Pearson product moment every 1 percent gait cycle (%GC). A group of continuous foot function-impacted %GCs were treated as a whole, which we called a "gait phase cluster (GPC)", and the integral average of the signal amplitude in the GPC was output as a single predictor. Results showed that concerning foot-motion, the influence of foot function on gait phases differed among genders. Foot function mainly impacted the rotational foot-motion in the coronal and transverse planes. In the male group, the foot-motion during pre-swing was influenced most by foot function and no significant correlations were found during mid-stance (MSt). In contrast, for the female group, significant correlations were found during MSt, no significant correlations were found during pre-swing (PS), and foot-motion immediately before heel strike was influenced most. We identified seven and six effective predictors for CPEI estimation for the male and female groups, respectively. The CPEI estimation models constructed by linear regression achieved RMSEs of 7.99 and 4.81 for the male and female groups, respectively, and achieved a "fair" agreement between estimated and true values. These results suggested that IMS is promising on achieve a simpler method for foot function assessment.
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页数:6
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