Foot-Healthcare Application Using Inertial Sensor: Estimating First Metatarsophalangeal Angle From Foot Motion During Walking

被引:5
|
作者
Huang, Chenhui [1 ]
Fukushi, Kenichiro [1 ]
Wang, Zhenwei [1 ]
Kajitani, Hiroshi [1 ]
Nihey, Fumiyuki [1 ]
Pokka, Hannah [2 ]
Narasaki, Hiroko [2 ]
Nakano, Hiroaki [2 ]
Nakahara, Kentaro [1 ]
机构
[1] NEC Corp Ltd, Biometr Res Labs, Abiko, Chiba 2701174, Japan
[2] NEC Corp Ltd, Business Innovat Unit, Minato City, Tokyo 1088001, Japan
关键词
Inertial sensor; foot-motion; foot health; gait analysis; hallux valgus; GROUND REACTION FORCES; HALLUX-VALGUS; OLDER-ADULTS; GAIT; DEFORMITIES; KINEMATICS; PRESSURE; CHILDREN; PLANUS; HEEL;
D O I
10.1109/JSEN.2021.3138485
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Our purpose was to demonstrate the possibility of providing foot-healthcare application by using an in-shoe motion sensor (IMS) throughvalidating the feasibilityof applying an IMS for measuring the first metatarsophalangeal angle (FMTPA), which is the most important parameter regarding the common foot problem hallux valgus. Methods: The IMS signals can represent foot motions when the mid-foot and hindfoot were modelled as a rigid body. FMTPAs can be estimated from the foot-motion signals measured using an IMS embedded beneath the foot arch near the calcaneus side using a machine-learning method. The foot-motion signals were collected from 50 participants with different FMTPAs. The true FMTPAs were assessed from digital photography. Correlation-based feature-selectionprocesses (significance levelp < 0.05) were used to search for the predictors from the foot-motion signals. Leave-one-subject-out cross-validation, root mean squared error, and intra-class coefficients were used for FMTPA-estimation model evaluation. Results: Eleven FMTPA-impacted gait-phase clusters, which were used to construct effective foot-motion predictors, were observed in all gait-cycle periods except terminal swing. The range of the foot motion in the sagittal and coronal planes significantly correlated with the FMTPA ( p < 0.05). Linear regression could be the best method for constructing an FMTPA estimation model with a root mean squared error and intra-class correlation coefficient of 4.2 degrees and 0.789, respectively. Conclusion: The results indicate the reliability of our FMTPA estimation model constructed from foot-motion signals and the possibility to providing foot-healthcare applications by using an IMS.
引用
收藏
页码:2835 / 2844
页数:10
相关论文
共 35 条
  • [21] An Open Data Set of Inertial, Magnetic, Foot-Ground Contact, and Electromyographic Signals From Wearable Sensors During Walking
    Miraldo, Desiree Camara
    Watanabe, Renato Naville
    Duarte, Marcos
    MOTOR CONTROL, 2020, 24 (04) : 558 - 570
  • [22] 3D analysis of the metatarsophalangeal joint in normal group and Hallux valgus patients during walking using a four-segment foot model
    Bora Jeong
    Seunghyeon Kim
    Jongsang Son
    Youngho Kim
    International Journal of Precision Engineering and Manufacturing, 2014, 15 : 299 - 303
  • [23] 3D Analysis of the Metatarsophalangeal Joint in Normal Group and Hallux Valgus Patients during Walking Using a Four-Segment Foot Model
    Jeong, Bora
    Kim, Seunghyeon
    Son, Jongsang
    Kim, Youngho
    INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 2014, 15 (02) : 298 - 302
  • [24] Estimation of stride length in level walking using an inertial measurement unit attached to the foot: A validation of the zero velocity assumption during stance
    Peruzzi, A.
    Della Croce, U.
    Cereatti, A.
    JOURNAL OF BIOMECHANICS, 2011, 44 (10) : 1991 - 1994
  • [25] Leg Joint Angle Estimation From a Single Inertial Sensor During Variety of Walking Motions: A Deep Learning Approach
    Alemayoh, Tsige Tadesse
    Lee, Jae Hoon
    Okamoto, Shingo
    IEEE ACCESS, 2023, 11 : 121978 - 121990
  • [26] Feature selection, construction and test of model for estimating lower extremity strength of older adults using foot motion measured by an in-shoe motion sensor
    Huang, Chenhui
    Nihey, Fumiyuki
    Fukushi, Kenichiro
    Kajitani, Hiroshi
    Nozaki, Yoshitaka
    Ihara, Kazuki
    Nakahara, Kentaro
    2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC, 2023,
  • [27] Estimating Whole-Body Walking Motion from Inertial Measurement Units at Wrist and Heels Using Deep Learning
    Kumano, Yuji
    Kanoga, Suguru
    Yamamoto, Masataka
    Takemura, Hiroshi
    Tada, Mitsunori
    INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY, 2023, 17 (03) : 217 - 225
  • [28] Ambulatory Measurement of Three-Dimensional Foot Displacement During Treadmill Walking Using Wearable Wireless Ultrasonic Sensor Network
    Qi, Yongbin
    Soh, Cheong Boon
    Gunawan, Erry
    Low, Kay-Soon
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2015, 19 (02) : 446 - 452
  • [29] A comparison of subtalar joint motion during anticipated medial cutting turns and level walking using a multi-segment foot model
    Jenkyn, T. R.
    Shultz, R.
    Giffin, J. R.
    Birmingham, T. B.
    GAIT & POSTURE, 2010, 31 (02) : 153 - 158
  • [30] Validation of foot pitch angle estimation using inertial measurement unit against marker-based optical 3D motion capture system
    Sharif Bidabadi S.
    Murray I.
    Lee G.Y.F.
    Biomedical Engineering Letters, 2018, 8 (3) : 283 - 290