Can Trunk Acceleration Differentiate Stroke Patient Gait Patterns Using Time- and Frequency-Domain Features?
Source abstract
Abstract
This study classified the gait patterns of normal and stroke participants by using time- and frequency-domain features obtained from data provided by an inertial measurement unit sensor placed on the subject’s lower back (L5). Twenty-three participants were included and divided into two groups: healthy group (young and older adults) and stroke group. Time- and frequency-domain features from an accelerometer were extracted, and a feature selection method comprising statistical analysis and signal-to-noise ratio (SNR) calculation was used to reduce the number of features. The features were then used to train four Support Vector Machine (SVM) kernels, and the results were subsequently compared. The quadratic SVM kernel had the highest accuracy (93.46%), as evaluated through cross-validation. Moreover, when different datasets were used on model testing, both the quadratic and cubic kernels showed the highest accuracy (96.55%). These results demonstrated the effectiveness of this study’s classification method in distinguishing between normal and stroke gait patterns, with only using a single sensor placed on the L5.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Gait Recognition and Analysis · Balance, Gait, and Falls Prevention · Diabetic Foot Ulcer Assessment and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
National Defense Medical Center, Taipei 11490, Taiwan
System Integration and Application Department, Information and Communication Research Laboratory, Industrial Technology Research Institute, Hsinchu 31057, Taiwan
Department of Gerontological Health Care, National Taipei University of Nursing and Health Sciences, Taipei 112, Taiwan
Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
Department of Neurological Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan
References (40)
Show source-provided references
1. Gait analysis in neurological populations: Progression in the use of wearables10.1016/j.medengphy.2020.11.005 ↗
2. Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson’s Disease: What Counts?10.1109/OJEMB.2020.2966295 ↗
3. Teufl, W., Lorenz, M., Miezal, M., Taetz, B., Fröhlich, M., and Bleser, G. (2019). Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters. Sensors, 19.10.3390/s19225006 ↗
4. Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study10.1371/journal.pone.0169649 ↗
5. A machine learning approach to assessing gait patterns for complex regional pain syndrome10.1016/j.medengphy.2011.09.018 ↗
6. Free-living gait characteristics in ageing and Parkinson’s disease: Impact of environment and ambulatory bout length10.1186/s12984-016-0154-5 ↗
7. Spatiotemporal features of human motion for gait recognition10.1007/s11760-018-1365-y ↗
8. A comprehensive assessment of gait accelerometry signals in time, frequency and time-frequency domains10.1109/TNSRE.2013.2265887 ↗
9. Stability and harmony of gait in children with cerebral palsy10.1016/j.ridd.2011.08.031 ↗
10. Hsu, W.-C., Sugiarto, T., Lin, Y.-J., Yang, F.-C., Lin, Z.-Y., Sun, C.-T., Hsu, C.-L., and Chou, K.-N. (2018). Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders. Sensors, 18.10.3390/s18103397 ↗
11. IMU-Based Classification of Parkinson’s Disease From Gait: A Sensitivity Analysis on Sensor Location and Feature Selection10.1109/JBHI.2018.2865218 ↗
12. Mannini, A., Trojaniello, D., Cereatti, A., and Sabatini, A.M. (2016). A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients. Sensors, 16.10.3390/s16010134 ↗
13. Two Shank-Mounted IMUs-Based Gait Analysis and Classification for Neurological Disease Patients10.1109/LRA.2020.2970656 ↗
14. Godfrey, A., Del Din, S., Barry, G., Mathers, J.C., and Rochester, L. (2014, January 26–30). Within trial validation and reliability of a single tri-axial accelerometer for gait assessment. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago, IL, USA.10.1109/EMBC.2014.6944969 ↗
15. Validation of an Accelerometer to Quantify a Comprehensive Battery of Gait Characteristics in Healthy Older Adults and Parkinson’s Disease: Toward Clinical and at Home Use10.1109/JBHI.2015.2419317 ↗
16. Gonzalez, R.C., Alvarez, D., Lopez, A.M., and Alvarez, J.C. (2007, January 22–26). Modified pendulum model for mean step length estimation. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France.10.1109/IEMBS.2007.4352553 ↗
17. Trunk-acceleration based assessment of gait parameters in older persons: A comparison of reliability and validity of four inverted pendulum based estimations10.1016/j.gaitpost.2013.04.021 ↗
18. Real-Time Identification of Gait Events in Impaired Subjects Using a Single-IMU Foot-Mounted Device10.1109/JSEN.2019.2951923 ↗
19. Gadaleta, M., Cisotto, G., Rossi, M., Rehman, R.Z.U., Rochester, L., and Din, S.D. (2019, January 23–27). Deep Learning Techniques for Improving Digital Gait Segmentation. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.10.1109/EMBC.2019.8856685 ↗
20. Iosa, M., Morone, G., Fusco, A., Pratesi, L., Bragoni, M., Coiro, P., Multari, M., Venturiero, V., De Angelis, D., and Paolucci, S. (2011). Effects of walking endurance reduction on gait stability in patients with stroke. Stroke Res. Treat., 2012.10.1155/2012/810415 ↗
21. Acceleration patterns of the head and pelvis during gait in older people with Parkinson’s disease: A comparison of fallers and nonfallers10.1093/gerona/glp009 ↗
22. Gait stability assessment in Down and Prader-Willi syndrome children using inertial sensors10.1016/j.gaitpost.2016.07.046 ↗
23. Marching to the beat of the same drummer: The spontaneous tempo of human locomotion10.1152/japplphysiol.00138.2005 ↗
24. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences10.3758/BF03193146 ↗
25. Goh, L., Song, Q., and Kasabov, N. (2004, January 18–22). A novel feature selection method to improve classification of gene expression data. Proceedings of the Second Conference on Asia-Pacific Bioinformatics, Dunedin, New Zealand.
26. Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors10.1007/s10439-013-0917-0 ↗
27. Ly, Q.T., Handojoseno, A.M.A., Gilat, M., Chai, R., Martens, K.A.E., Georgiades, M., Naik, G.R., Tran, Y., Lewis, S.J.G., and Nguyen, H.T. (2017, January 11–15). Detection of gait initiation Failure in Parkinson’s disease based on wavelet transform and Support Vector Machine. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Korea.10.1109/EMBC.2017.8037500 ↗
28. Tahafchi, P., Molina, R., Roper, J.A., Sowalsky, K., Hass, C.J., Gunduz, A., Okun, M.S., and Judy, J.W. (2017, January 11–15). Freezing-of-Gait detection using temporal, spatial, and physiological features with a support-vector-machine classifier. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Korea.10.1109/EMBC.2017.8037455 ↗
29. Zheng, H., Yang, M., Wang, H., and McClean, S. (2009). Machine learning and statistical approaches to support the discrimination of neuro-degenerative diseases based on gait analysis. Intelligent Patient Management, Springer.10.1007/978-3-642-00179-6_4 ↗
30. Support vector machine for classification of walking conditions of persons after stroke with dropped foot10.1016/j.humov.2008.12.003 ↗
31. Support vector machines for automated gait classification10.1109/TBME.2005.845241 ↗
32. Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media.
33. Das, K.D., Saji, A.J., and Kumar, C.S. (2017, January 20–21). Frequency analysis of gait signals for detection of neurodegenerative diseases. Proceedings of the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, India.
34. Selection of clinical features for pattern recognition applied to gait analysis10.1007/s11517-016-1546-1 ↗
35. Activity classification in persons with stroke based on frequency features10.1016/j.medengphy.2014.11.008 ↗
36. Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry10.1186/1475-925X-10-1 ↗
37. Parisi, F., Ferrari, G., Baricich, A., Innocenzo, M.D., Cisari, C., and Mauro, A. (2016, January 14–17). Accurate gait analysis in post-stroke patients using a single inertial measurement unit. Proceedings of the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA, USA.10.1109/BSN.2016.7516284 ↗
38. Analysis of stroke patient walking dynamics using a tri-axial accelerometer10.1016/j.gaitpost.2009.02.017 ↗
39. Gait stability indices and their relationship with clinical scale scores in stroke patients10.1016/j.gaitpost.2015.07.024 ↗
40. Gait classification in post-stroke patients using artificial neural networks10.1016/j.gaitpost.2009.04.010 ↗