Estimation of Vertical Ground Reaction Force Using Low-Cost Insole with Force Plate-Free Learning from Single Leg Stance and Walking

Ryo Eguchi, Ayanori Yorozu, Takahiko Fukumoto, Masaki Takahashi

研究成果: Article査読

4 被引用数 (Scopus)

抄録

For the evaluation of pathological gait, a machine learning-based estimation of the vertical ground reaction force (vGRF) using a low-cost insole is proposed as an alternative to costly force plates. However, learning a model for estimation still relies on the use of force plates, which is not accessible in small clinics and individuals. Therefore, this paper presents a force plate-free learning from a single leg stance (SLS) and natural walking measured only by the insoles. This method used a linear least squares regression that fits insole measurements during SLS to body weight in order to learn a model to estimate vGRF during walking. Constraints were added to the regression so that vGRF estimates during walking were of proper magnitude, and the constraint bounds were newly defined as a linear function of stance duration. Moreover, a lower bound for the estimated vGRF in mid-stance was added to the constraints to enhance estimation accuracy. The vGRF estimated by the proposed method was compared with force platforms for 4 healthy young adults and 13 elderly adults including patients with mild osteoarthritis, knee pain, and valgus hallux. Through the experiments, the proposed learning method had a normalized root mean squared error under 10% for healthy young and elderly adults with stance durations within a certain range (600-800 ms). From these results, the validity of the proposed learning method was verified for various users requiring assessment in the field of medicine and healthcare.

本文言語English
論文番号8811479
ページ(範囲)1276-1283
ページ数8
ジャーナルIEEE Journal of Biomedical and Health Informatics
24
5
DOI
出版ステータスPublished - 2020 5月

ASJC Scopus subject areas

  • バイオテクノロジー
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 健康情報管理

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