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Yaşlılarda Düşme Riskini Değerlendirmek için İvmelenme Sinyalinin Frekans Domeni Özellikleri

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 150 - 155, 15.08.2020
https://doi.org/10.31590/ejosat.779590

Öz

Her yıl dünya üzerinde 65 yaş ve üzerindeki insanların yaklaşık %28-35'i en az bir kere düşer ve bu sayı önümüzdeki yıllarda hızla aratacaktır. Düşme sonrası fiziki yaralanmanın dışında tedavi sonrasında bağımlılık, özerklik kaybı ve depresyon gibi düşme sonrası sendromlarda görülür. Düşmenin hem bireye hem de ekonomik olarak topluma olan etkisi göz ardı edilemeyecek seviyededir ve giderek artmaktadır. Ancak doğru yaklaşımlarla düşme önlenebilir. Düşmenin engellenebilmesi için yaşlıların sık sık denge değerlendirmesinin yapılması ve düşme riski olan yaşlılar için gerekli önlemlerin alınması gerekmektedir. Denge değerlendirmesi için basit anketlerden bilgisayarlı karmaşık testlere kadar geniş yelpazede araçlar bulunabilir. Ancak anketler sübjektiftir. Bilgisayarlı testlerin ise maliyet ve yer kaplamaları nedeniyle birinci basamak sağlık kuruluşlarında kullanılmaları uygun değildir. Bu yüzden birinci basamak sağlık kuruluşlarında kullanılabilecek basit bir yöntem geliştirmek oldukça önemlidir. İvmeölçerler hafif ucuz ve basit yapıları ile giyilebilir teknoloji alanında yerini almış ve denge değerlendirmesinde kullanılabilmektedir. Bu çalışmada PhysioNet veri tabanında yer alan 38’i kontrol 35’i düşme riski olan yaşlıdan kayıt edilen üç eksen ivmelenme sinyali kullanılarak yaşlılarda düşme riskini tanımlayıcı parametreler bulunmaya çalışılmıştır. Bunun için ivmelenme sinyalinden önce yerçekiminden kaynaklanan bileşen çıkarılmış, 0,5 Hz yüksek geçiren 25 Hz alçak geçiren filtre ile filtrelenmiş ve en büyük değere normalize edilmiştir. Daha sonra 25 model derecesinde özbağlanımlı model kullanılarak Burg yöntemi ile ivmelenme sinyallerinin güç spektrum yoğunlukları bulunmuştur. Güç spektrumunda oluşan birinci ve ikinci en büyük tepelere ait tanımlayıcı özellikler, güç spektrumunun statiksel özellikleri, güç spektrumunun enerjisi ile ilgili özellikleri olmak üzere 29 özellik her üç eksen için elde edilmiştir. Bu özellikler bağımsız-örneklem t-testi kullanılarak %99 güvenilirlik seviyesinde karşılaştırılmıştır. Sonuç olarak toplam da dört farklı özelliğin istatistiksel olarak iki grup arasında anlamlı fark gösterdiği görülmüştür. Bu özelliklerden güç spektrumunun kurtosisi ve ikinci en büyük tepenin genişliği özellikleri literatüre bu çalışma ile eklenmiştir.

Kaynakça

  • Altunkaya, S., Kara, S., Gormus, N., & Herdem, S. (2013). Comparison of first and second heart sounds after mechanical heart valve replacement. Computer Methods in Biomechanics and Biomedical Engineering, 16(4), 368-380. Retrieved from <Go to ISI>://WOS:000317722400003
  • Balaban, Ö., Nacır, B., Erdem, H. R., & Karagöz, A. (2009). Denge Fonksiyonunun De¤erlendirilmesi. Journal of Physical Medicine and Rehabilitation Sciences, 12, 9.
  • Castellini, G., Gianola, S., Stucovitz, E., Tramacere, I., Banfi, G., & Moja, L. (2019). Diagnostic test accuracy of an automated device as a screening tool for fall risk assessment in community-residing elderly: A STARD compliant study. Medicine (Baltimore), 98(39), e17105. doi:10.1097/MD.0000000000017105
  • Cho, C. Y., & Kamen, G. (1998). Detecting balance deficits in frequent fallers using clinical and quantitative evaluation tools. Journal of the American Geriatrics Society, 46(4), 426-430. Retrieved from <Go to ISI>://WOS:000072973100004
  • Diego Galar, U. K. (2017). eMaintenance (U. K. Diego Galar Ed.): Academic Press.
  • Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., . . . Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), E215-220. doi:10.1161/01.cir.101.23.e215
  • Howcroft, J., Kofman, J., & Lemaire, E. D. (2013). Review of fall risk assessment in geriatric populations using inertial sensors. Journal of Neuroengineering and Rehabilitation, 10. doi:Artn 9110.1186/1743-0003-10-91
  • Karantonis, D. M., Narayanan, M. R., Mathie, M., Lovell, N. H., & Celler, B. G. (2006). Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. Ieee Transactions on Information Technology in Biomedicine, 10(1), 156-167. doi:10.1109/Titb.2005.856864
  • Kojima, M., Obuchi, S., Henmi, O., & Ikeda, N. (2008). Comparison of Smoothness during Gait between Community Dwelling Elderly Fallers and Non-Fallers Using Power Spectrum Entropy of Acceleration Time-Series. Journal of Physical Therapy Science, 20(4), 243-248. doi:DOI 10.1589/jpts.20.243
  • Koyuncu, G., Tuna, F., Yavuz, S., Kabayel, D. D., Koyuncu, M., Özdemir, H., & N., S. (2017). The last station before fracture: Assessment of falling and loss of balance in elderly. Turk J Phys Med Rehab, 63(1), 9. doi:10.5606/tftrd.2017.90757
  • Liu, Y., Redmond, S. J., Wang, N., Blumenkron, F., Narayanan, M. R., & Lovell, N. H. (2011). Spectral analysis of accelerometry signals from a directed-routine for falls-risk estimation. IEEE Trans Biomed Eng, 58(8). doi:10.1109/TBME.2011.2151193
  • Mathie, M. J., Coster, A. C. F., Lovell, N. H., & Celler, B. G. (2004). Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement, 25(2), R1-R20. Retrieved from <Go to ISI>://WOS:000221075000001
  • Moncada, L. V. V., & Mire, L. G. (2017). Preventing Falls in Older Persons. American Family Physician, 96(4), 240-247. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/28925664
  • Najafi, B., Aminian, K., Loew, F., Blanc, Y., & Robert, P. A. (2002). Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. Ieee Transactions on Biomedical Engineering, 49(8), 843-851. doi:10.1109/Tbme.2002.800763
  • Sun, T. L., & Huang, C. H. (2019). Interactive visualization to assist fall-risk assessment of community-dwelling elderly people. Information Visualization, 18(1), 33-44. doi:10.1177/1473871617721243
  • Weiss, A., Brozgol, M., Dorfman, M., Herman, T., Shema, S., Giladi, N., & Hausdorff, J. M. (2013). Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings. Neurorehabil Neural Repair, 27(8), 742-752. doi:10.1177/1545968313491004
  • Weiss, A., Sharifi, S., Plotnik, M., van Vugt, J. P. P., Giladi, N., & Hausdorff, J. M. (2011). Toward Automated, At-Home Assessment of Mobility Among Patients With Parkinson Disease, Using a Body-Worn Accelerometer. Neurorehabilitation and Neural Repair, 25(9), 810-818. doi:10.1177/1545968311424869
  • WHO. (2007). WHO Global Report on Falls Prevention in Older Age. France: WHO Press.
  • Wu, C. H., Lee, C. H., Jiang, B. C., & Sun, T. L. (2019). Multiscale Entropy Analysis of Postural Stability for Estimating Fall Risk via Domain Knowledge of Timed-Up-And-Go Accelerometer Data for Elderly People Living in a Community. Entropy, 21(11). doi:ARTN 107610.3390/e21111076
  • Yang, C. C., & Hsu, Y. L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors (Basel), 10(8), 7772-7788. doi:10.3390/s100807772

Frequency Domain Features of Acceleration Signals to Evaluate Fall Risk of Elderly

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 150 - 155, 15.08.2020
https://doi.org/10.31590/ejosat.779590

Öz

Every year, about 28-35% of people aged 65 and over in the world fall at least once, and this number will increase rapidly in the coming years. Apart from physical injury after fall, it is seen in post-fall syndromes such as addiction, loss of autonomy and depression after treatment. The impact of the fall on the individual and economically on the society is at a level that cannot be ignored and is gradually increasing. However, the fall can be prevented by correct approaches. In order to prevent fall in the elderly, balance assessment should be done frequently and precaution should be taken for individuals at risk of fall. A wide range of tools are available for balance assessment, from simple questionnaires to complex computerized tests. However, questionnaire are subjective. Computerized tests, on the other hand, are not suitable to be used in primary health care centers due to their cost and volume. Therefore, it is very important to develop a simple method that can be used in primary health care centers. Accelerometers have taken their place in wearable technology with their light, cheap and simple structures and can be used in balance assessment. In this study, it was tried to find parameters that define fall risk of the elderly by using the three axis acceleration signal recorded from the elderly with 38 non-faller 35 faller in PhysioNet database. For this, the component caused by gravity was first removed from the acceleration signal, filtered with 0.5 Hz high pass and 25 Hz low pass filter and normalized to maximum. Then, power spectrum density of acceleration signals were found using autoregressive model with 25 model order by Burg's algorithm. 29 features were obtained for all three axes, namely the descriptive features of the first and second dominant peaks in the power spectrum, the statistical features of the power spectrum, and the features related to the energy of the power spectrum. These features were compared using the independent-sample t-test at 99% confidence level. As a result, it was observed that a total of four different features showed statistically significant difference between the two groups. Among these features, the kurtosis of the power spectrum and the width of the second largest hill are added to the literature with this study.

Kaynakça

  • Altunkaya, S., Kara, S., Gormus, N., & Herdem, S. (2013). Comparison of first and second heart sounds after mechanical heart valve replacement. Computer Methods in Biomechanics and Biomedical Engineering, 16(4), 368-380. Retrieved from <Go to ISI>://WOS:000317722400003
  • Balaban, Ö., Nacır, B., Erdem, H. R., & Karagöz, A. (2009). Denge Fonksiyonunun De¤erlendirilmesi. Journal of Physical Medicine and Rehabilitation Sciences, 12, 9.
  • Castellini, G., Gianola, S., Stucovitz, E., Tramacere, I., Banfi, G., & Moja, L. (2019). Diagnostic test accuracy of an automated device as a screening tool for fall risk assessment in community-residing elderly: A STARD compliant study. Medicine (Baltimore), 98(39), e17105. doi:10.1097/MD.0000000000017105
  • Cho, C. Y., & Kamen, G. (1998). Detecting balance deficits in frequent fallers using clinical and quantitative evaluation tools. Journal of the American Geriatrics Society, 46(4), 426-430. Retrieved from <Go to ISI>://WOS:000072973100004
  • Diego Galar, U. K. (2017). eMaintenance (U. K. Diego Galar Ed.): Academic Press.
  • Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., . . . Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), E215-220. doi:10.1161/01.cir.101.23.e215
  • Howcroft, J., Kofman, J., & Lemaire, E. D. (2013). Review of fall risk assessment in geriatric populations using inertial sensors. Journal of Neuroengineering and Rehabilitation, 10. doi:Artn 9110.1186/1743-0003-10-91
  • Karantonis, D. M., Narayanan, M. R., Mathie, M., Lovell, N. H., & Celler, B. G. (2006). Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. Ieee Transactions on Information Technology in Biomedicine, 10(1), 156-167. doi:10.1109/Titb.2005.856864
  • Kojima, M., Obuchi, S., Henmi, O., & Ikeda, N. (2008). Comparison of Smoothness during Gait between Community Dwelling Elderly Fallers and Non-Fallers Using Power Spectrum Entropy of Acceleration Time-Series. Journal of Physical Therapy Science, 20(4), 243-248. doi:DOI 10.1589/jpts.20.243
  • Koyuncu, G., Tuna, F., Yavuz, S., Kabayel, D. D., Koyuncu, M., Özdemir, H., & N., S. (2017). The last station before fracture: Assessment of falling and loss of balance in elderly. Turk J Phys Med Rehab, 63(1), 9. doi:10.5606/tftrd.2017.90757
  • Liu, Y., Redmond, S. J., Wang, N., Blumenkron, F., Narayanan, M. R., & Lovell, N. H. (2011). Spectral analysis of accelerometry signals from a directed-routine for falls-risk estimation. IEEE Trans Biomed Eng, 58(8). doi:10.1109/TBME.2011.2151193
  • Mathie, M. J., Coster, A. C. F., Lovell, N. H., & Celler, B. G. (2004). Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement, 25(2), R1-R20. Retrieved from <Go to ISI>://WOS:000221075000001
  • Moncada, L. V. V., & Mire, L. G. (2017). Preventing Falls in Older Persons. American Family Physician, 96(4), 240-247. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/28925664
  • Najafi, B., Aminian, K., Loew, F., Blanc, Y., & Robert, P. A. (2002). Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. Ieee Transactions on Biomedical Engineering, 49(8), 843-851. doi:10.1109/Tbme.2002.800763
  • Sun, T. L., & Huang, C. H. (2019). Interactive visualization to assist fall-risk assessment of community-dwelling elderly people. Information Visualization, 18(1), 33-44. doi:10.1177/1473871617721243
  • Weiss, A., Brozgol, M., Dorfman, M., Herman, T., Shema, S., Giladi, N., & Hausdorff, J. M. (2013). Does the evaluation of gait quality during daily life provide insight into fall risk? A novel approach using 3-day accelerometer recordings. Neurorehabil Neural Repair, 27(8), 742-752. doi:10.1177/1545968313491004
  • Weiss, A., Sharifi, S., Plotnik, M., van Vugt, J. P. P., Giladi, N., & Hausdorff, J. M. (2011). Toward Automated, At-Home Assessment of Mobility Among Patients With Parkinson Disease, Using a Body-Worn Accelerometer. Neurorehabilitation and Neural Repair, 25(9), 810-818. doi:10.1177/1545968311424869
  • WHO. (2007). WHO Global Report on Falls Prevention in Older Age. France: WHO Press.
  • Wu, C. H., Lee, C. H., Jiang, B. C., & Sun, T. L. (2019). Multiscale Entropy Analysis of Postural Stability for Estimating Fall Risk via Domain Knowledge of Timed-Up-And-Go Accelerometer Data for Elderly People Living in a Community. Entropy, 21(11). doi:ARTN 107610.3390/e21111076
  • Yang, C. C., & Hsu, Y. L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors (Basel), 10(8), 7772-7788. doi:10.3390/s100807772
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Sabri Altunkaya 0000-0002-0853-0095

Yayımlanma Tarihi 15 Ağustos 2020
Yayımlandığı Sayı Yıl 2020 Ejosat Özel Sayı 2020 (HORA)

Kaynak Göster

APA Altunkaya, S. (2020). Frequency Domain Features of Acceleration Signals to Evaluate Fall Risk of Elderly. Avrupa Bilim Ve Teknoloji Dergisi150-155. https://doi.org/10.31590/ejosat.779590