Dataset Information

Using inertial measurement units to estimate spine joint kinematics and kinetics during walking and running.

ABSTRACT: Optical motion capture (OMC) is considered the best available method for measuring spine kinematics, yet inertial measurement units (IMU) have the potential to collect data outside the laboratory. When combined with musculoskeletal modeling, IMU technology may be used to estimate spinal loads in real-world settings. To date, IMUs have not been validated for estimates of spinal movement and loading during both walking and running. Using OpenSim Thoracolumbar Spine and Ribcage models, we compare IMU and OMC estimates of lumbosacral (L5/S1) and thoracolumbar (T12/L1) joint angles, moments, and reaction forces during gait across six speeds for five participants. For comparisons, time series are ensemble averaged over strides. Comparisons between IMU and OMC ensemble averages have low normalized root mean squared errors (< 0.3 for 81% of comparisons) and high, positive cross-correlations (> 0.5 for 91% of comparisons), suggesting signals are similar in magnitude and trend. As expected, joint moments and reaction forces are higher during running than walking for IMU and OMC. Relative to OMC, IMU overestimates joint moments and underestimates joint reaction forces by 20.9% and 15.7%, respectively. The results suggest using a combination of IMU technology and musculoskeletal modeling is a valid means for estimating spinal movement and loading.

SUBMITTER: Sibson BE

PROVIDER: S-EPMC10762015 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Using inertial measurement units to estimate spine joint kinematics and kinetics during walking and running.

Sibson Benjamin E BE Banks Jacob J JJ Yawar Ali A Yegian Andrew K AK Anderson Dennis E DE Lieberman Daniel E DE

Scientific reports 20240102 1

Optical motion capture (OMC) is considered the best available method for measuring spine kinematics, yet inertial measurement units (IMU) have the potential to collect data outside the laboratory. When combined with musculoskeletal modeling, IMU technology may be used to estimate spinal loads in real-world settings. To date, IMUs have not been validated for estimates of spinal movement and loading during both walking and running. Using OpenSim Thoracolumbar Spine and Ribcage models, we compare I ...[more]

PMID: 38168540

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Project description:BackgroundInertial measurement units (IMUs) are useful in monitoring running and alerting running-related injuries in various sports settings. However, the quantitative summaries of the validity and reliability of the measurements from IMUs during running are still lacking. The purpose of this review was to investigate the concurrent validity and test-retest reliability of IMUs for measuring gait spatiotemporal outcomes and lower extremity kinematics of health adults during running.MethodsPubMed, CINAHL, Embase, Scopus and Web of Science electronic databases were searched from inception until September 2021. The inclusion criteria were as follows: (1) evaluated the validity or reliability of measurements from IMUs, (2) measured specific kinematic outcomes, (3) compared measurements using IMUs with those obtained using reference systems, (4) collected data during running, (5) assessed human beings and (6) were published in English. Eligible articles were reviewed using a modified quality assessment. A meta-analysis was performed to assess the pooled correlation coefficients of validity and reliability.ResultsTwenty-five articles were included in the systematic review, and data from 12 were pooled for meta-analysis. The methodological quality of studies ranged from low to moderate. Concurrent validity is excellent for stride length (intraclass correlation coefficient (ICC) (95% confidence interval (CI)) = 0.937 (0.859, 0.972), p < 0.001), step frequency (ICC (95% CI) = 0.926 (0.896, 0.948), r (95% CI) = 0.989 (0.957, 0.997), p < 0.001) and ankle angle in the sagittal plane (r (95% CI) = 0.939 (0.544, 0.993), p = 0.002), moderate to excellent for stance time (ICC (95% CI) = 0.664 (0.354, 0.845), r (95% CI) = 0.811 (0.701, 0.881), p < 0.001) and good for running speed (ICC (95% CI) = 0.848 (0.523, 0.958), p = 0.0003). The summary Fisher's Z value of flight time was not statistically significant (p = 0.13). Similarly, the stance time showed excellent test-retest reliability (ICC (95% CI) = 0.954 (0.903, 0.978), p < 0.001) and step frequency showed good test-retest reliability (ICC (95% CI) = 0.896 (0.837, 0.933), p < 0.001).ConclusionsFindings in the current review support IMUs measurement of running gait spatiotemporal parameters, but IMUs measurement of running kinematics on lower extremity joints needs to be reported with caution in healthy adults.Trial registrationPROSPERO Registration Number: CRD42021279395.

Project description:Introduction: Children's walking patterns evolve with age, exhibiting less repetitiveness at a young age and more variability than adults. Three-dimensional gait analysis (3DGA) is crucial for understanding and treating lower limb movement disorders in children, traditionally performed using Optical Motion Capture (OMC). Inertial Measurement Units (IMUs) offer a cost-effective alternative to OMC, although challenges like drift errors persist. Machine learning (ML) models can mitigate these issues in adults, prompting an investigation into their applicability to a heterogeneous pediatric population. This study aimed at 1) quantifying personalized and generalized ML models' performance for predicting gait time series in typically developed (TD) children using IMUs data, 2) Comparing random forest (RF) and convolutional neural networks (CNN) models' performance, 3) Finding the optimal number of IMUs required for accurate predictions. Methodology: Seventeen TD children, aged 6 to 15, participated in data collection involving OMC, force plates, and IMU sensors. Joint kinematics and kinetics (targets) were computed from OMC and force plates' data using OpenSim. Tsfresh, a Python package, extracted features from raw IMU data. Each target's ten most important features were input in the development of personalized and generalized RF and CNN models. This procedure was initially conducted with 7 IMUs placed on all lower limb segments and then performed using only two IMUs on the feet. Results: Findings suggested that the RF and CNN models demonstrated comparable performance. RF predicted joint kinematics with a 9.5% and 19.9% NRMSE for personalized and generalized models, respectively, and joint kinetics with an NRMSE of 10.7% for personalized and 15.2% for generalized models in TD children. Personalized models provided accurate estimations from IMU data in children, while generalized models lacked accuracy due to the limited dataset. Furthermore, reducing the number of IMUs from 7 to 2 did not affect the results, and the performance remained consistent. Discussion: This study proposed a promising personalized approach for gait time series prediction in children, involving an RF model and two IMUs on the feet.

Dataset Information

Using inertial measurement units to estimate spine joint kinematics and kinetics during walking and running.

Publications

Using inertial measurement units to estimate spine joint kinematics and kinetics during walking and running.

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