دورية أكاديمية
Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking
| العنوان: | Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking |
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| المؤلفون: | Pimentel, Ricky, Armitano-Lago, Cortney, MacPherson, Ryan, Sathyan, Anoop, Twiddy, Jack, Peterson, Kaila, Daniele, Michael, Kiefer, Adam W., Lobaton, Edgar, Pietrosimone, Brian, Franz, Jason R. |
| المصدر: | PLOS Digital Health, 3(5) |
| بيانات النشر: | PLoS Journals |
| سنة النشر: | 2024 |
| المجموعة: | Carolina Digital Repository (UNC - University of North Carolina) |
| مصطلحات موضوعية: | Body weight, Sensory perception, Walking, Knee joints, Skeletal joints, Accelerometers, Knees, Osteoarthritis |
| الوصف: | Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms. Monitoring and maintaining typical vGRF profiles may support healthy gait biomechanics and joint tissue loading to prevent the onset and progression of knee osteoarthritis. Yet, the optimal number of sensors and sensor placements for predicting accurate vGRF from accelerometry remains unknown. Our goals were to: 1) determine how many sensors and what sensor locations yielded the most accurate vGRF loading peak estimates during walking; and 2) characterize how prescribing different loading conditions affected vGRF loading peak estimates. We asked 20 young adult participants to wear 5 accelerometers on their waist, shanks, and feet and walk on a force-instrumented treadmill during control and targeted biofeedback conditions prompting 5% underloading and overloading vGRFs. We trained and tested machine learning models to estimate vGRF from the various sensor accelerometer inputs and identified which combinations were most accurate. We found that a neural network using one accelerometer at the waist yielded the most accurate loading peak vGRF estimates during walking, with average errors of 4.4% body weight. The waist-only configuration was able to distinguish between control and overloading conditions prescribed using biofeedback, matching measured vGRF outcomes. Including foot or shank acceleration signals in the model reduced accuracy, particularly for the overloading condition. Our results suggest that a system designed to monitor changes in walking vGRF or to deploy targeted biofeedback may ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | https://doi.org/10.17615/3ehb-y873Test; https://cdr.lib.unc.edu/downloads/bc386w237?file=thumbnailTest; https://cdr.lib.unc.edu/downloads/bc386w237Test |
| DOI: | 10.17615/3ehb-y873 |
| الإتاحة: | https://doi.org/10.17615/3ehb-y873Test https://cdr.lib.unc.edu/downloads/bc386w237?file=thumbnailTest https://cdr.lib.unc.edu/downloads/bc386w237Test |
| رقم الانضمام: | edsbas.349460F3 |
| قاعدة البيانات: | BASE |
| DOI: | 10.17615/3ehb-y873 |
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