DTW를 이용한 보행 분석 시 warping path 특성
Characteristics of warping path on gait analysis using the DTW
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
The purpose of this study was to investigate the characteristics of the warping path on gait patterns using the DTW algorithm. Participants in this study were consist of 20’s age of 10males and 10 females. Gait data obtained by a 3D motion analysis system analyzed using the DTW program made by Matlab’s functions. The analysis was performed 120 times including normal walking and abnormal walking. The length of the warping path relative to the reference sequences, and deviations was analyzed for the warping path calculated by the DTW program. The statistical method used in this study was a correlation, independent t-test, one-way ANOVA, and regression. The statistical significance level was =.05. In this study, the correlation coefficient of Pearson was =.816 between the warping path length and the deviation, and this relationship was verified by regression analysis. The statistically significant difference between normal walking and abnormal walking appeared in the warping path length and in the deviation. The analysis according to walking phases showed a statistically significant defference between the support phase and the swing phase. In conclusion, if the length of the warping path is greater than about 127% compared to the diagonal length between two sequences, normal walking can be suspected, and if the mean of the warping path length deviations is greater than about 5.8, normal walking can be suspected also.
Abstract
The purpose of this study was to investigate the characteristics of the warping path on gait patterns using the DTW algorithm. Participants in this study were consist of 20’s age of 10males and 10 females. Gait data obtained by a 3D motion analysis system analyzed using the DTW program made by Matlab’s functions. The analysis was performed 120 times including normal walking and abnormal walking. The length of the warping path relative to the reference sequences, and deviations was analyzed for the warping path calculated by the DTW program. The statistical method used in this study was a correlation, independent t-test, one-way ANOVA, and regression. The statistical significance level was =.05. In this study, the correlation coefficient of Pearson was =.816 between the warping path length and the deviation, and this relationship was verified by regression analysis. The statistically significant difference between normal walking and abnormal walking appeared in the warping path length and in the deviation. The analysis according to walking phases showed a statistically significant defference between the support phase and the swing phase. In conclusion, if the length of the warping path is greater than about 127% compared to the diagonal length between two sequences, normal walking can be suspected, and if the mean of the warping path length deviations is greater than about 5.8, normal walking can be suspected also.
참고문헌 (29)
- 조우형, "하지 보행 불균형 상태에 따른 개인별 보행 특성 분석", 전자공학회논문지, 54(5), pp.109~119 (2017), 대한전자공학회
- Aach, J., "Aligning gene expression time series with time warping algorithms", Bioinformatics, 17(6), pp.495~508 (2001)
- Barth, J., "Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data", Sensors(Switzerland), 15(3), pp.6419~6440 (2015)[DOI]
- Chung, M. J., "The change of gait parameters during walking at different percentage of preferred walking speed for healthy adults aged 20-60 years", Gait and Posture, 31(1), pp.131~135 (2010)[DOI]
- Clément, J., "Hip-Knee-Ankle(HKA)angle modification during gait in healthy subjects", Gait and Posture, 72, pp.62~68 (2019)[DOI]
- Corradini, A., "Dynamic time warping for off-line recognition of a small gesture vocabulary", Proceedings of IEEE ICCV Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems, pp.82~89 (2001)
- Efrat, A., "Curve matching, time warping, and light fields : New algorithms for computing similarity between curves", Journal of Mathematical Imaging and Vision, 27(3), pp.203~216 (2007)[DOI]
- Gaspar, M., "Dynamic Time Warping compared to established methods for validation of musculoskeletal models", Journal of Biomechanics, 55, pp.156~161 (2017)[DOI]
- Hausdorff, J. M., "Is walking a random walk? Evidence for long-range correlations in stride interval of human gait", Journal of Applied Physiology, 78(1), pp.349~358 (1995)
- Huang, B., "ECG frame classification using dynamic time warping", IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373), 2, pp.1105~1110 (2002)
- Kang, H. G., "Separating the effects of age and walking speed on gait variability", Gait and Posture, 27(4), pp.572~577 (2008)[DOI]
- Kuzmanic, A., "Hand shape classification using DTW and LCSS as similarity measures for vision-based gesture recognition system", EUROCON 2007 - The International Conference on “Computer as a Tool”, pp.264~269 (2007)
- 이현섭, "Application of dynamic time warping algorithm for pattern similarity of gait", Journal of exercise rehabilitation, 15(4), pp.526~530 (2019), 한국운동재활학회[DOI]
- Long, J. T., "Variation inflation factor-based regression modeling of anthropometric measures and temporal-spatial performance:Modeling approach and implications for clinical utility", Clinical Biome- chanics, 51, pp.51~57 (2018)[DOI]
- McCrum, C., "Stability-normalised walking speed : A new approach for human gait perturbation research", Journal of Biomechanics, 87, pp.48~53 (2019)[DOI]
- Myers, C. S., "Performance trade‐offs in dynamic time warping algorithms for isolated word recognition", The Journal of the Acoustical Society of America, 66(S34), pp.S34~S35 (1979)[DOI]
- Niennattrakul, V., "On Clustering Multimedia Time Series Data Using K-Means and Dynamic Time Warping", 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE’07), pp.733~738 (2007)
- Nishikawa, S., "An evaluation method of normal gait pattern and its application to Down’s syndrome patients", International Journal of Applied Electromagnetics and Mechanics, 52(1-2), pp.487~493 (2016)[DOI]
- Ratanamahatana, C. A., "Everything you know about Dynamic Time Warping is Wrong", Third Workshop on Mining Temporal and Sequential Data, pp.32~ (2004)
- Sakoe, H., "Dynamic programming algorithm optimization for spoken word recognition", IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), pp.43~49 (1978)[DOI]
- Stansfield, B. W., "Regression analysis of gait parameters with speed in normal children walking at self-selected speeds", Gait and Posture, 23(3), pp.288~294 (2006)[DOI]
- Sudha, L. R., "An efficient spatio-temporal gait representation for gender classification", Applied Artificial Intelligence, 27(1), pp.62~75 (2013)[DOI]
- Syeda-Mahmood, T., "Shape-based Matching of ECG Recordings", 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.2012~2018 (2007)
- Tappert, C. C., "The State of the Art in On-Line Handwriting Recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(8), pp.787~808 (1990)
- Tsang, D. J., "Characterization of statistical persistence in joint angle variation during walking", Human Movement Science, 68, pp.1~11 (2019)[DOI]
- Tuzcu, V., "Dynamic time warping as a novel tool in pattern recognition of ECG changes in heart rhythm disturbances", Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 1, pp.182~186 (2005)
- Velichko, V. M., "Automatic recognition of 200 words", International Journal of Man-Machine Studies, 2(3), pp.223~234 (1970)[DOI]
- Yamamoto, H., "The change in knee angle during the gait by applying elastic tape to the skin", Journal of Physical Therapy Science, 26(7), pp.1075~1077 (2014)[DOI]
- Zhang, B., "Walking stability analysis by age based on Dynamic Time Warping", 2008 8th IEEE International Conference on Computer and Information Technology, pp.544~548 (2008)