Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection

Bach, Margit M.; Dominici, Nadia; Daffertshofer, Andreas

doi:10.1101/2022.02.14.480318

Cited by 3 publications

(2 citation statements)

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“…Our Waist model aligns with other groups that have used machine learning or regression equations to estimate vGRF loading peak during walking or running [ 22 , 23 ]. Although lower leg sensor sites can accurately estimate vGRF signals under different scenarios [ 24 , 25 ], we found that accelerometers placed near the body’s center of mass may provide optimal features to estimate vGRF during walking. This outcome is theoretically supported by simple Newtonian physics-based models of walking [ 21 ], in that acceleration of the body’s center of mass is proportional to the net external force acting on the body.…”

Section: Discussionmentioning

confidence: 99%

“…However, those authors did not include comparisons to a waist- or hip-worn sensor [ 24 ]. Bach et al (2022) used only shank accelerometers to estimate vGRF profiles with R 2 = 0.97 and a normalized root mean square error of 5.2% [ 25 ]. Altogether, these studies highlight that various accelerometer numbers and locations can be used to estimate vGRF.…”

Section: Introductionmentioning

confidence: 99%

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Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking

Pimentel,

Armitano-Lago,

MacPherson

et al. 2024

PLOS Digit Health

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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 only need a single accelerometer located at the waist for healthy participants.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking

Pimentel,

Armitano-Lago,

MacPherson

et al. 2024

PLOS Digit Health

View full text Add to dashboard Cite

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Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking

Pimentel,

Armitano-Lago,

MacPherson

et al. 2023

Preprint

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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 errors around 4.3% 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 only need a single accelerometer located at the waist.

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Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns

Kiernan,

Dunn Siino,

Hawkins

2023

Sensors

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We evaluated 18 methods capable of identifying initial contact (IC) and terminal contact (TC) gait events during human running using data from a single wearable sensor on the shank or sacrum. We adapted or created code to automatically execute each method, then applied it to identify gait events from 74 runners across different foot strike angles, surfaces, and speeds. To quantify error, estimated gait events were compared to ground truth events from a time-synchronized force plate. Based on our findings, to identify gait events with a wearable on the shank, we recommend the Purcell or Fadillioglu method for IC (biases +17.4 and −24.3 ms; LOAs −96.8 to +131.6 and −137.0 to +88.4 ms) and the Purcell method for TC (bias +3.5 ms; LOAs −143.9 to +150.9 ms). To identify gait events with a wearable on the sacrum, we recommend the Auvinet or Reenalda method for IC (biases −30.4 and +29.0 ms; LOAs −149.2 to +88.5 and −83.3 to +141.3 ms) and the Auvinet method for TC (bias −2.8 ms; LOAs −152.7 to +147.2 ms). Finally, to identify the foot in contact with the ground when using a wearable on the sacrum, we recommend the Lee method (81.9% accuracy).

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Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection

Cited by 3 publications

References 99 publications

Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking

Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking

Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking

Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns

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