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ASSESSING THE EFFECT OF AGE-RELATED SENSORY INPUT CHANGES ON POSTURAL SWAY IRREGULARITY

Year 2023, , 1109 - 1120, 12.12.2023

Veysel Alcan

https://doi.org/10.17780/ksujes.1338361
Cited By: 1

Abstract

Age-related decline in sensory inputs in elderly people leads to postural instability that increases irregularity of postural sway. This study aimed to examine the effect of visual or somatosensory inputs on postural sway irregularity in the elderly by using machine learning (ML). The feature set was extracted from entropy measurements including sample, fuzzy, distribution, conditional, and permutation. Then, the variables were classified by ML including support vector machines (SVM), k-nearest neighbors (k-NN), and linear discriminant analysis (LDA) algorithms. Classification performances were compared with the confusion matrix. For the elderly, in the eyes closed condition on an unstable surface, the SVM algorithm achieved higher accuracy (77%), sensitivity (72%), specificity (85%), and precision (83%) for the cv dataset. For young, SVM also achieved high accuracy (86%), sensitivity (87%), specificity (84%), and precision (84%). For the elderly, under the eyes open on unstable surface conditions, the SVM exhibited an accuracy of 79%, sensitivity of 75%, specificity of 72%, and precision of 75%. However, for young, it did not reveal good results for both surfaces. In conclusion, the findings suggest that older people adapt their postural control mechanisms, relying more on somatosensory inputs. ML algorithms with entropy-based features can give insights into age-related differences in postural control.

Keywords

Older people balance postural sway entropy machine learning

References

  • Alcan, V. (2022). Nonlinear Analysis of Stride Interval Time Series in Gait Maturation Using Distribution Entropy. IRBM, 43(4), 309-316. https://doi.org/10.1016/j.irbm.2021.02.001.
  • Alcan, V. (2023, July). Evaluation Of The Effects Of Visual And Somatosensory Inputs On Balance In The Elderly By Usıng Machine Learning. In 2023 th International IFS and Contemporary Mathematics and Engineering Conference(IFSCOM-E 2023) (pp.223)
  • Bandt, C., & Pompe, B. (2002). Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17), 174102. https://doi.org/10.1103/PhysRevLett.88.174102
  • Barela, A. M. F., Caporicci, S., de Freitas, P. B., Jeka, J. J., & Barela, J. A. (2018). Light touch compensates for peripheral somatosensory degradation in postural control of older adults. Human movement science, 60, 122–130.
  • Chen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical engineering & physics, 31(1), 61–68. https://doi.org/10.1016/j.medengphy.2008.04.005
  • Cetin, E., &Bilgi,n S. (2019) Investigating effects of force and pressure center signals on stabilogram analysis. IET Science Measurement &Technology, 13(9): 1305– 1310. https://doi.org/10.1049/iet-smt.2019.0078 Garbus, R. B. S. C., Alouche, S. R., Prado-Rico, J. M., Aquino, C. M., & Freitas, S. M. S. F. (2019). From One to Two: Can Visual Feedback Improve the Light Touch Effects on Postural Sway? Journal of Motor Behavior, 51(5), 532–539. https://doi.org/10.1080/00222895.2018.1528201
  • Giovanini, L.H.F., Manffra, E.F., & Nievola, J.C. (2018). Discriminating Postural Control Behaviors from Posturography with Statistical Tests and Machine Learning Models: Does Time Series Length Matter? In: Shi, Y., et al. Computational Science – ICCS 2018. ICCS 2018. Lecture Notes in Computer Science, 10862, 350–357. https://doi.org/10.1007/978-3-319-93713-7_28
  • Hansen, C., Wei, Q., Shieh, J. S., Fourcade, P., Isableu, B., & Majed, L. (2017). Sample Entropy, Univariate, and Multivariate Multi-Scale Entropy in Comparison with Classical Postural Sway Parameters in Young Healthy Adults. Frontiers in human neuroscience, 11, 206. https://doi.org/10.3389/fnhum.2017.00206
  • Horak, F. B., Shupert, C. L., & Mirka, A. (1989). Components of postural dyscontrol in the elderly: a review. Neurobiology of aging, 10(6), 727–738. https://doi.org/10.1016/0197-4580(89)90010-9
  • Ito, T., Sakai, Y., Nishio, R. Yohei, I., Kazunori Y., & Yoshifumi M. (2020). Postural Sway in Adults and Elderly Individuals During Local Vibratory Stimulation of the Somatosensory System. SN Compr. Clin. Med. 2, 753–758.
  • Lee, C. H., Chen, S. H., Jiang, B. C., & Sun, T. L. (2020). Estimating Postural Stability Using Improved Permutation Entropy via TUG Accelerometer Data for Community-Dwelling Elderly People. Entropy (Basel, Switzerland), 22(10), 1097. https://doi.org/10.3390/e22101097
  • Li, P., Li, K., Liu, C., Zheng, D., Li, Z. M., & Liu, C. (2016). Detection of Coupling in Short Physiological Series by a Joint Distribution Entropy Method. IEEE Transactions on Bio-Medical Engineering, 63(11), 2231–2242. https://doi.org/10.1109/TBME.2016.2515543
  • Lord, S. R., Clark, R. D., & Webster, I. W. (1991). Postural stability and associated physiological factors in a population of aged persons. Journal of Gerontology, 46(3), M69–M76. https://doi.org/10.1093/geronj/46.3.m69
  • Maurer, C., Mergner, T., & Peterka, R. J. (2006). Multisensory control of human upright stance. Experimental brain research, 171(2), 231–250. https://doi.org/10.1007/s00221-005-0256-y
  • Mergner, T., Schweigart, G., Maurer, C., & Blümle, A. (2005). Human postural responses to motion of real and virtual visual environments under different support base conditions. Experimental brain research, 167(4), 535–556. https://doi.org/10.1007/s00221-005-0065-3
  • Ojie, O. D., & Saatchi, R. (2021). Kohonen Neural Network Investigation of the Effects of the Visual, Proprioceptive, and Vestibular Systems to Balance in Young Healthy Adult Subjects. Healthcare (Basel, Switzerland), 9(9), 1219. https://doi.org/10.3390/healthcare9091219
  • Patel, M., Fransson, P. A., Johansson, R., & Magnusson, M. (2011). Foam posturography: standing on foam is not equivalent to standing with decreased rapidly adapting mechanoreceptive sensation. Experimental brain research, 208(4), 519–527. https://doi.org/10.1007/s00221-010-2498-6
  • Peterka R. J. (2018). Sensory integration for human balance control. Handbook of Clinical Neurology, 159, 27–42. https://doi.org/10.1016/B978-0-444-63916-5.00002-1
  • Porta, A., Baselli, G., Liberati, D., Montano, N., Cogliati, C., Gnecchi-Ruscone, T., Malliani, A., & Cerutti, S. (1998). Measuring regularity using a corrected conditional entropy in sympathetic outflow. Biological cybernetics, 78(1), 71–78. https://doi.org/10.1007/s004220050414
  • Qiu, F., Cole, M. H., Davids, K. W., Hennig, E. M., Silburn, P. A., Netscher, H., & Kerr, G. K. (2012). Enhanced somatosensory information decreases postural sway in older people. Gait & posture, 35(4), 630–635. https://doi.org/10.1016/j.gaitpost.2011.12.013
  • Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American journal of physiology. Heart and circulatory physiology, 278(6), H2039–H2049. https://doi.org/10.1152/ajpheart.2000.278.6.H2039
  • Santos, D.A., & Duarte, M. (2016) A public data set of human balance evaluations. PeerJ, 4:e2648. https://doi.org/10.7717/peerj.2648
  • Seigle, B., Ramdani, S., & Bernard, P. L. (2009). Dynamical structure of the center of pressure fluctuations in elderly people. Gait & posture, 30(2), 223–226. https://doi.org/10.1016/j.gaitpost.2009.05.005
  • Shiota, K. (2015). Influence of Aging on Postural Control in Terms of Sensory Movements. In: Kanosue, K., Oshima, S., Cao, ZB., Oka, K. (eds) Physical Activity, Exercise, Sedentary Behavior and Health. Tokyo: Springer.
  • Sun, R., Hsieh, K. L., & Sosnoff, J. J. (2019). Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach. Scientific reports, 9(1), 16154. https://doi.org/10.1038/s41598-019-52697-2
  • Tanaka, H., & Uetake, T. (2005). Characteristics of postural sway in older adults standing on a soft surface. Journal of Human Ergology, 34(1-2), 35–40.
  • Wang, Y., Kenyon, R. V., & Keshner, E. A. (2010). Identifying the control of physically and perceptually evoked sway responses with coincident visual scene velocities and tilt of the base of support. Experimental brain research, 201(4), 663–672. https://doi.org/10.1007/s00221-009-2082-0
  • WMeier, I. K., Dalin, D., & Maurer, C. (2015). Elderly Use Proprioception Rather than Visual and Vestibular Cues for Postural Motor Control. Frontiers in aging neuroscience, 7, 97. https://doi.org/10.3389/fnagi.2015.00097

YAŞA BAĞLI DUYUSAL DEĞİŞİKLİKLERİN POSTURAL SALINIM DÜZENSİZLİĞİ ÜZERİNDEKİ ETKİSİNİN DEĞERLENDİRİLMESİ

Year 2023, , 1109 - 1120, 12.12.2023

Veysel Alcan

https://doi.org/10.17780/ksujes.1338361
Cited By: 1

Abstract

Yaşlılarda duyusal girdilerde yaşa bağlı azalma, postüral dengesizliğe yol açarak postüral salınımın düzensizliğini artırır. Bu çalışma, makine öğrenimi (ML) kullanarak görsel veya somatosensoriyel girdilerin yaşlılarda postural salınım düzensizliği üzerindeki etkisini incelemeyi amaçladı. Özellik seti örnek, bulanık, dağıtım, koşullu ve permütasyon dâhil Entropi ölçümlerinden çıkarıldı. Daha sonra değişkenler, destek vektör makineleri (SVM), k-en yakın komşular (k-NN) ve doğrusal diskriminant analizi (LDA) algoritmalarını içeren ML modelleri ile sınıflandırıldı. Modellerin sınıflandırma performansları hata matrisi ile karşılaştırıldı. Yaşlılar için, stabil olmayan bir yüzeyde gözleri kapalı durumda SVM algoritması test veri seti için daha yüksek doğruluk (%77), duyarlılık (%72), özgüllük (%85) ve kesinlik (%83) elde etti. Gençler içinde SVM yüksek doğruluk (%86), duyarlılık (%87), özgüllük (%84) ve kesinlik (%84) elde etti. Kararsız yüzey koşullarında gözleri açık olan yaşlılar için SVM %79 doğruluk, %75 duyarlılık, %72 özgüllük ve %75 kesinlik sergiledi. Ancak gençler için her iki yüzeyde de iyi sonuçlar ortaya çıkmadı. Sonuç olarak, bulgular yaşlı insanların postüral kontrol mekanizmalarını somatosensör girdilere daha fazla güvenerek uyarladıklarını göstermektedir. Entropi tabanlı özellik setine sahip ML algoritmaları, yaşlılarda postüral salınım dinamiklerini yöneten temel mekanizmalar hakkında fikir verebilir.

Keywords

Yaşlı insanlar denge postüral salınım entropi makine öğrenimi

References

  • Alcan, V. (2022). Nonlinear Analysis of Stride Interval Time Series in Gait Maturation Using Distribution Entropy. IRBM, 43(4), 309-316. https://doi.org/10.1016/j.irbm.2021.02.001.
  • Alcan, V. (2023, July). Evaluation Of The Effects Of Visual And Somatosensory Inputs On Balance In The Elderly By Usıng Machine Learning. In 2023 th International IFS and Contemporary Mathematics and Engineering Conference(IFSCOM-E 2023) (pp.223)
  • Bandt, C., & Pompe, B. (2002). Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17), 174102. https://doi.org/10.1103/PhysRevLett.88.174102
  • Barela, A. M. F., Caporicci, S., de Freitas, P. B., Jeka, J. J., & Barela, J. A. (2018). Light touch compensates for peripheral somatosensory degradation in postural control of older adults. Human movement science, 60, 122–130.
  • Chen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical engineering & physics, 31(1), 61–68. https://doi.org/10.1016/j.medengphy.2008.04.005
  • Cetin, E., &Bilgi,n S. (2019) Investigating effects of force and pressure center signals on stabilogram analysis. IET Science Measurement &Technology, 13(9): 1305– 1310. https://doi.org/10.1049/iet-smt.2019.0078 Garbus, R. B. S. C., Alouche, S. R., Prado-Rico, J. M., Aquino, C. M., & Freitas, S. M. S. F. (2019). From One to Two: Can Visual Feedback Improve the Light Touch Effects on Postural Sway? Journal of Motor Behavior, 51(5), 532–539. https://doi.org/10.1080/00222895.2018.1528201
  • Giovanini, L.H.F., Manffra, E.F., & Nievola, J.C. (2018). Discriminating Postural Control Behaviors from Posturography with Statistical Tests and Machine Learning Models: Does Time Series Length Matter? In: Shi, Y., et al. Computational Science – ICCS 2018. ICCS 2018. Lecture Notes in Computer Science, 10862, 350–357. https://doi.org/10.1007/978-3-319-93713-7_28
  • Hansen, C., Wei, Q., Shieh, J. S., Fourcade, P., Isableu, B., & Majed, L. (2017). Sample Entropy, Univariate, and Multivariate Multi-Scale Entropy in Comparison with Classical Postural Sway Parameters in Young Healthy Adults. Frontiers in human neuroscience, 11, 206. https://doi.org/10.3389/fnhum.2017.00206
  • Horak, F. B., Shupert, C. L., & Mirka, A. (1989). Components of postural dyscontrol in the elderly: a review. Neurobiology of aging, 10(6), 727–738. https://doi.org/10.1016/0197-4580(89)90010-9
  • Ito, T., Sakai, Y., Nishio, R. Yohei, I., Kazunori Y., & Yoshifumi M. (2020). Postural Sway in Adults and Elderly Individuals During Local Vibratory Stimulation of the Somatosensory System. SN Compr. Clin. Med. 2, 753–758.
  • Lee, C. H., Chen, S. H., Jiang, B. C., & Sun, T. L. (2020). Estimating Postural Stability Using Improved Permutation Entropy via TUG Accelerometer Data for Community-Dwelling Elderly People. Entropy (Basel, Switzerland), 22(10), 1097. https://doi.org/10.3390/e22101097
  • Li, P., Li, K., Liu, C., Zheng, D., Li, Z. M., & Liu, C. (2016). Detection of Coupling in Short Physiological Series by a Joint Distribution Entropy Method. IEEE Transactions on Bio-Medical Engineering, 63(11), 2231–2242. https://doi.org/10.1109/TBME.2016.2515543
  • Lord, S. R., Clark, R. D., & Webster, I. W. (1991). Postural stability and associated physiological factors in a population of aged persons. Journal of Gerontology, 46(3), M69–M76. https://doi.org/10.1093/geronj/46.3.m69
  • Maurer, C., Mergner, T., & Peterka, R. J. (2006). Multisensory control of human upright stance. Experimental brain research, 171(2), 231–250. https://doi.org/10.1007/s00221-005-0256-y
  • Mergner, T., Schweigart, G., Maurer, C., & Blümle, A. (2005). Human postural responses to motion of real and virtual visual environments under different support base conditions. Experimental brain research, 167(4), 535–556. https://doi.org/10.1007/s00221-005-0065-3
  • Ojie, O. D., & Saatchi, R. (2021). Kohonen Neural Network Investigation of the Effects of the Visual, Proprioceptive, and Vestibular Systems to Balance in Young Healthy Adult Subjects. Healthcare (Basel, Switzerland), 9(9), 1219. https://doi.org/10.3390/healthcare9091219
  • Patel, M., Fransson, P. A., Johansson, R., & Magnusson, M. (2011). Foam posturography: standing on foam is not equivalent to standing with decreased rapidly adapting mechanoreceptive sensation. Experimental brain research, 208(4), 519–527. https://doi.org/10.1007/s00221-010-2498-6
  • Peterka R. J. (2018). Sensory integration for human balance control. Handbook of Clinical Neurology, 159, 27–42. https://doi.org/10.1016/B978-0-444-63916-5.00002-1
  • Porta, A., Baselli, G., Liberati, D., Montano, N., Cogliati, C., Gnecchi-Ruscone, T., Malliani, A., & Cerutti, S. (1998). Measuring regularity using a corrected conditional entropy in sympathetic outflow. Biological cybernetics, 78(1), 71–78. https://doi.org/10.1007/s004220050414
  • Qiu, F., Cole, M. H., Davids, K. W., Hennig, E. M., Silburn, P. A., Netscher, H., & Kerr, G. K. (2012). Enhanced somatosensory information decreases postural sway in older people. Gait & posture, 35(4), 630–635. https://doi.org/10.1016/j.gaitpost.2011.12.013
  • Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American journal of physiology. Heart and circulatory physiology, 278(6), H2039–H2049. https://doi.org/10.1152/ajpheart.2000.278.6.H2039
  • Santos, D.A., & Duarte, M. (2016) A public data set of human balance evaluations. PeerJ, 4:e2648. https://doi.org/10.7717/peerj.2648
  • Seigle, B., Ramdani, S., & Bernard, P. L. (2009). Dynamical structure of the center of pressure fluctuations in elderly people. Gait & posture, 30(2), 223–226. https://doi.org/10.1016/j.gaitpost.2009.05.005
  • Shiota, K. (2015). Influence of Aging on Postural Control in Terms of Sensory Movements. In: Kanosue, K., Oshima, S., Cao, ZB., Oka, K. (eds) Physical Activity, Exercise, Sedentary Behavior and Health. Tokyo: Springer.
  • Sun, R., Hsieh, K. L., & Sosnoff, J. J. (2019). Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach. Scientific reports, 9(1), 16154. https://doi.org/10.1038/s41598-019-52697-2
  • Tanaka, H., & Uetake, T. (2005). Characteristics of postural sway in older adults standing on a soft surface. Journal of Human Ergology, 34(1-2), 35–40.
  • Wang, Y., Kenyon, R. V., & Keshner, E. A. (2010). Identifying the control of physically and perceptually evoked sway responses with coincident visual scene velocities and tilt of the base of support. Experimental brain research, 201(4), 663–672. https://doi.org/10.1007/s00221-009-2082-0
  • WMeier, I. K., Dalin, D., & Maurer, C. (2015). Elderly Use Proprioception Rather than Visual and Vestibular Cues for Postural Motor Control. Frontiers in aging neuroscience, 7, 97. https://doi.org/10.3389/fnagi.2015.00097
There are 28 citations in total.

Details

Primary Language English
Subjects Machine Learning (Other)
Journal Section Computer Engineering
Authors

Veysel Alcan 0000-0002-7786-8591

Publication Date December 12, 2023
Submission Date August 5, 2023
Published in Issue Year 2023

Cite

APA Alcan, V. (2023). ASSESSING THE EFFECT OF AGE-RELATED SENSORY INPUT CHANGES ON POSTURAL SWAY IRREGULARITY. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 26(Özel Sayı), 1109-1120. https://doi.org/10.17780/ksujes.1338361

Cited By

Effects of Sensory Input Interactions on Components of Nonlinear Dynamics of Postural Sway in Aging
Journal of Motor Behavior
https://doi.org/10.1080/00222895.2024.2317759