Abstract
Postural control is influenced by somatosensory, visual, and vestibular information. This study investigated the effects of open/closed eyes and external vibration stimuli on Achilles tendon on postural control in healthy adults. The visual and vibrational inputs were selected to induce somatosensory changes in quiet standing posture. Machine learning and wavelet analysis were used to classify the factors that influence postural control. Fifteen healthy subjects performed a quiet standing task under four different conditions: eyes closed, eye opened, with Achilles vibration, and without Achilles vibration. Results showed that both visual and vibration input conditions caused a significant difference in the mean velocity of anteroposterior direction in Center of Pressure (COP) shifting. This indicated that visual and somatosensory information changes have a significant role on postural control. The feasibility of using machine learning for the detection and classification of the factors influencing postural control function has been confirmed in this study. Combination of the features extracted by wavelet packet analysis and time-domain features showed to have the best classification outcome with F1-score of 96.77%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Osoba MY, Rao AK, Agrawal SK et al (2019) Balance and gait in the elderly: contemporary review. LaryngosCOPe Investigative Otolaryngol 4(1):143–153
Quijoux F, Nicolaï A, Chairi I et al (2021) A review of center of pressure (COP) variables to quantify standing balance in elderly people: algorithms and open‐access code. Physiol Rep 9(22):e15067
Jiang BC, Yang WH, Shieh JS et al (2013) Entropy-based method for COP data analysis. Theor Issues Ergon Sci 14(3):227–246
Chagdes JR, Rietdyk S, Haddad JM et al (2009) Multiple timescales in postural dynamics associated with vision and a secondary task are revealed by wavelet analysis. Exp Brain Res 197(3):297–310
Liang Z, Clark R, Bryant A et al (2014) Neck musculature fatigue affects specific frequency bands of postural dynamics during quiet standing. Gait Posture 39(1):397–403
Billot M, Handrigan GA, Simoneau M et al (2015) Reduced plantar sole sensitivity induces balance control modifications to compensate ankle tendon vibration and vision deprivation. J Electromyogr Kinesiol 25(1):155–160
McKay SM, Wu J, Angulo-Barroso RM (2014) Effect of Achilles tendon vibration on posture in children. Gait Posture 40(1):32–37
Doumas M, Valkanidis TC, Hatzitaki V (2019) Putting proprioception for balance to the test: contrasting and combining sway referencing and tendon vibration. Gait Posture 67:201–206
Howcroft J, Lemaire E D, Kofman J et al (2017) Elderly fall risk prediction using static posturography. PLoS ONE 12(2):e0172398
Lee CM, Park J, Park S et al (2020) Fall-detection algorithm using plantar pressure and acceleration data. Int J Precis Eng Manuf 21(4):725–737
Quek J, Brauer SG, Clark R et al (2014) New insights into neck-pain-related postural control using measures of signal frequency and complexity in older adults. Gait Posture 39(4):1069–1073
Chen G, Li QY, Li DQ et al (2019) Main frequency band of blast vibration signal based on wavelet packet transform. Appl Math Model 74:569–585
Acknowledgements
This research was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2021R1I1A3059769) and funded by BK21 FOUR (Fostering Outstanding Universities for Research) (No.: 5199990914048) and funded by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2018R1D1A1B07050037).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ning, X. et al. (2023). Classification of the Factors Influencing Center of Pressure Using Machine Learning and Wavelet Analysis. In: Park, J.S., Yang, L.T., Pan, Y., Park, J.H. (eds) Advances in Computer Science and Ubiquitous Computing. CUTECSA 2022. Lecture Notes in Electrical Engineering, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-99-1252-0_52
Download citation
DOI: https://doi.org/10.1007/978-981-99-1252-0_52
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1251-3
Online ISBN: 978-981-99-1252-0
eBook Packages: EngineeringEngineering (R0)