Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors and Machine Learning Algorithms

Authors

Rahul Soangra, Chapman UniversityFollow
Yuxin Wen, Chapman UniversityFollow
Hualin Yang, Chapman University
Marybeth Grant-Beuttler, Chapman UniversityFollow

Document Type

Article

Publication Date

7-18-2022

Abstract

Idiopathic toe walking (ITW) is a gait abnormality in which children’s toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of Machine Learning (ML) algorithms in identifying initial foot contact (heel strike versus toe strike) utilizing wearable body sensors. Thirty-six children (Age 9.4±2.8 years) diagnosed with ITW participated in this study. Six ML algorithms, consisting of Support Vector Machines (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), Multi-layer Perceptron (MLP), and Gaussian process (GP), could successfully classify initial contact walking patterns among ITW. We found that a simple KNN algorithm resulted in the highest accuracy of 92.92% and an F1-score of 93.20% to differentiate toe walking gait versus best heel strike when using all four body sensors. We also found that toe walking resulted in higher variability in the sacral vertical accelerations among children diagnosed with ITW. Accurate quantification of toe walking steps in clinical applications is critical for assessing rehabilitation progress and designing new interventions for children diagnosed with ITW.

Comments

This article was originally published in IEEE Access, volume 10, in 2022. https://doi.org/10.1109/ACCESS.2022.3192136

Recommended Citation

R. Soangra, Y. Wen, H. Yang and M. Grant-Beuttler, "Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors and Machine Learning Algorithms," in IEEE Access, vol. 10, pp. 77054-77067, 2022, https://doi.org/10.1109/ACCESS.2022.3192136

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.