ABSTRACT
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.
ABSTRACT
BACKGROUND: Female gymnasts have a greater prevalence of back pain compared to other female athletes. There is little evidence that female artistic gymnasts with and without back pain demonstrate different movement patterns during gymnastics skills. The purpose of this study was to determine if there were differences in back movements during back walkovers and back handsprings among female artistic gymnasts. METHODS: Female artistic gymnasts (8-18 years old) with and without back pain wore inertial sensors on their torso, arms, and legs while performing back walkovers (N = 14) and back handsprings (N = 15) on the floor and balance beam at their training gymnastics facilities. FINDINGS: Gymnasts with back pain had similar spine peak extension, peak flexion, and range of motion during back walkovers and back handsprings compared to gymnasts without back pain. Additionally, no differences in sagittal plane spine kinematics were found between the groups at any specific time point during either the back walkover or back handspring skills. However, a large portion of the data collected was excluded during quality assurance, thus our final sample sizes are small. INTERPRETATION: These findings suggest that gymnasts with back pain have similar sagittal plane movements to those without back pain. The relationship between back pain and gymnastics training load/intensity is currently unclear. We suggest future studies to investigate common artistic gymnastics skills and back pain prevalence with more participants, full-body motion analysis with kinetic measurement capabilities, and longitudinally for those demonstrating back pain.
