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

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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.

Original languageEnglish
Article number8811479
Pages (from-to)1276-1283
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
Publication statusPublished - 2020 May


  • Gait analysis
  • estimation
  • force plate
  • ground reaction force
  • instrumented insole
  • linear constraint
  • linear least squares regression
  • machine learning
  • single leg stance
  • walking

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications


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