İvmelenme Sinyallerinin Frekans Domeni Özelliklerinden Yaşlılarda Düşmenin Erken Tespiti

Year 2020, , 13 - 18, 21.12.2020

Sabri Altunkaya

https://doi.org/10.5281/zenodo.4015274

Abstract

Yaşlı nüfusun artmasıyla düşme gibi yaşlılarda görülen sağlık problemlerinin sağlık sistemi üzerindeki ekonomik yükü artırmaktadır. Hem yaşlıların sağlığını korumak hem de sağlık sistemi üzerindeki ekonomik yükü azaltmak için düşmenin önceden belirlenmesi oldukça önemlidir. Düşmenin önceden tespiti için yaşlının düzenli olarak birinci basamak sağlık kuruluşunda denge kontrolünden geçmesi önerilir. Bu yüzden birinci basamak sağlık kuruluşlarında kolayca uygulanabilir, basit bir sisteminin geliştirilmesi güncel bir ihtiyaçtır. Bu çalışmada bir sensör ve bir aktivite esnasında kısa süreli bir kayıt ile bu işlemi gerçekleştirebilecek bir sistem geliştirilmesi amaçlanmıştır. Bunun için bir dakikalık yürüyüş esnasında 71 yaşlının bel bölgesindeki ivmeölçerden kayıt edilen ivmelenme sinyalleri kullanılmıştır. İvmelenme sensöründen elde edilen sinyallerden önce yerçekimi bileşeni çıkarılmış, filtreleme ve normalizasyonu yapıldıktan sonra güç spektrum yoğunlukları bulunmuştur. Daha sonra her eksenden 29 olmak üzere toplam 87 özellik elde edilmiş ve özellik seçme işlemi uygulanmış ve destek vektör makineleri kullanılarak sınıflama işlemi yapılmıştır. Çalışmada iki farklı sınıflama modeli kullanılmış ve en yüksek sınıflama doğruluğu %72,6 (AUC=0,8) olarak elde edilmiştir. Hem bir aktivite esnasında bir sensörden kayıt edilen verilerin kullanılarak problemin çözülmeye çalışılması hem de daha önce bu problemin çözümünde kullanılmayan farklı güç spektrumu yoğunluğu özelliklerinin kullanılması çalışmamızı literatürden ayıran noktalardır.

Keywords

İvmeölçer, Yaşlı, Düşme, Sınıflama

Early Detection Of Falls In The Elderly Using Frequency Domain Features Of The Acceleration Signals

Abstract

ealth problems in elderly population such as falls increase the economic burden on the health system. It is very important to determine the fall beforehand in order to protect the health of the elderly and reduce the economic burden on the health system. It is very important to determine the fall beforehand in order to protect the health of the elderly and reduce the economic burden on the health system. In order to determine the fall beforehand, the elderly should be regularly checked in the primary health care centers. Therefore, it is a current need to develop a simple and easily applicable system in primary health care centers. In this study, it is aimed to develop a system that can perform this process with a sensor and a short-term recording during an activity. For this, the acceleration signals recorded from the accelerometer in the waist region of the 71-aged person during a one-minute walk were used. The gravitational component was first extracted from the signals obtained from the acceleration sensor, and the power spectral densities were found after filtering and normalization. Later, a total of 87 features were obtained, 29 from each axis. The feature selection process has reduced the amount of features and the classification process has been made using support vector machines. Two different classification models were used in the study and the highest classification accuracy was obtained as 72.6% (AUC = 0.8). The fact that we try to separate the fall and the control group from the data recorded from a sensor during an activity and the use of different power spectrum features that have not been used to solve this problem before in the literature are the points that distinguish our study from the literature.

Keywords

Accelerometer, Old, Fall, Classification

References

• [1] WHO, WHO Global Report on Falls Prevention in Older Age. 2007, France: WHO Press.
• [2] Wu, C.H., et al., 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, 2019. 21(11).
• [3] Castellini, G., et al., 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), 2019. 98(39): p. e17105.
• [4] Koyuncu, G., et al., The last station before fracture: Assessment of falling and loss of balance in elderly. Turk J Phys Med Rehab 2017. 63(1): p. 9.
• [5] Balaban, Ö., et al., Denge Fonksiyonunun De¤erlendirilmesi. Journal of Physical Medicine and Rehabilitation Sciences, 2009. 12: p. 9.
• [6] Najafi, B., et al., 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, 2002. 49(8): p. 843-851.
• [7] Howcroft, J., J. Kofman, and E.D. Lemaire, Review of fall risk assessment in geriatric populations using inertial sensors. Journal of Neuroengineering and Rehabilitation, 2013. 10.
• [8] Sun, T.L. and C.H. Huang, Interactive visualization to assist fall-risk assessment of community-dwelling elderly people. Information Visualization, 2019. 18(1): p. 33-44.
• [9] Yang, C.C. and Y.L. Hsu, A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors (Basel), 2010. 10(8): p. 7772-88.
• [10] Mathie, M.J., et al., Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement, 2004. 25(2): p. R1-R20.
• [11] Caby, B., et al., Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry. Biomedical Engineering Online, 2011. 10.
• [12] Greene, B.R., et al., Classification of frailty and falls history using a combination of sensor-based mobility assessments. Physiological Measurement, 2014. 35(10): p. 2053-2066.
• [13] Riva, F., et al., Estimating fall risk with inertial sensors using gait stability measures that do not require step detection. Gait & Posture, 2013. 38(2): p. 170-174.
• [14] Greene, B.R., et al., Evaluation of Falls Risk in Community-Dwelling Older Adults Using Body-Worn Sensors. Gerontology, 2012. 58(5): p. 472-480.
• [15] Paterson, K., K. Hill, and N. Lythgo, Stride dynamics, gait variability and prospective falls risk in active community dwelling older women. Gait & Posture, 2011. 33(2): p. 251-255.
• [16] Weiss, A., et al., 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, 2013. 27(8): p. 742-52.
• [17] Goldberger, A.L., et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000. 101(23): p. E215-20.
• [18] Karantonis, D.M., et al., Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. Ieee Transactions on Information Technology in Biomedicine, 2006. 10(1): p. 156-167.
• [19] Altunkaya, S., Frequency Domain Features of Acceleration Signals to Evaluate Fall Risk of Elderly. Avrupa Bilim ve Teknoloji Dergisi, 2020. (): p. 150-155.
• [20] Boser, B.E., I.M. Guyon, and V.N. Vapnik, A training algorithm for optimal margin classifiers, in Proceedings of the fifth annual workshop on Computational learning theory. 1992, Association for Computing Machinery: Pittsburgh, Pennsylvania, USA. p. 144–152.
• [21] Cortes, C., Vapnik, V. , Support-Vector Networks. Machine Learning, 1995. 20: p. 273-297.
• [22] Altunkaya, S., Onur, İ., Detection of Mechanical Heart Valve Thrombosis Using Support Vector Machine. International Journal of Applied Mathematics Electronics and Computers, 2019. 7(2): p. 44-48.
• [23] Doi, T., et al., The harmonic ratio of trunk acceleration predicts falling among older people: results of a 1-year prospective study. Journal of Neuroengineering and Rehabilitation, 2013. 10.
• [24] Liu, Y., et al., Validation of an Accelerometer-based Fall Prediction Model. 2014 36th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc), 2014: p. 4531-+.