References
- M. Montero-Odasso, M. Schapira, E. R. Soriano, M. Varela, R. Kaplan, L.A. Camera, and L. M. Mayorga, “Gait velocity as a single predictor of adverse events in healthy seniors aged 75 years and older.”, The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, vol. 60, no. 10, pp. 1304-1309, 2020. Available: https://academic.oup.com/biomedgerontology/article/60/10/1304/553147
- K. Bhakta, J. Camargo, W. Compton, K. Herrin, and A. Young, “Evaluation of continuous walking speed determination algorithms and embedded sensors for a powered knee & ankle prosthesis,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4820–4826, 2021. doi: 10.1109/LRA.2021.3068711.
- J. Camargo, W. Flanagan, N. Csomay-Shanklin, B. Kanwar, and A. Young, “A Machine Learning Strategy for Locomotion Classification and Parameter Estimation Using Fusion of Wearable Sensors,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 5, pp. 1569–1578, 2021. doi: 10.1109/TBME.2021.3065809.
- A. M. Nasrabadi, A. R. Eslaminia, P. R. Bakhshayesh, M. Ejtehadi, L. Alibiglou, and S. Behzadipour, “A new scheme for the development of IMU-based activity recognition systems for telerehabilitation,” Medical Engineering & Physics, vol. 108, 2022. doi: 10.1016/j.medengphy.2022.103876.
- M. I. A. S. N. Ferreira, F. A. Barbieri, V. C. Moreno, T. Penedo, and J. M. R. S. Tavares, “Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters,” Gait Posture, vol. 98, pp. 49–55, 2022. doi: 10.1016/j.gaitpost.2022.08.014.
- D. Trabassi, M. Serrao, T. Varrecchia, A. Ranavolo, G. Coppola, R. De Icco, and S. F. Castiglia, “Machine Learning Approach to Support the Detection of Parkinson’s Disease in IMU-Based Gait Analysis,” Sensors, vol. 22, no. 10, 2022. doi: 10.3390/s22103700.
- M. De Vos, J. Prince, T. Buchanan, J. J. FitzGerald, and C. A. Antoniades, “Discriminating progressive supranuclear palsy from Parkinson’s disease using wearable technology and machine learning,” Gait Posture, vol. 77, pp. 257–263, 2020. doi: 10.1016/j.gaitpost.2020.02.007.
- P. Panyakaew, N. Pornputtapong, and R. Bhidayasiri, “Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson’s disease,” Parkinsonism & Related Disorders, vol. 82, pp. 77–83, 2021. doi: 10.1016/j.parkreldis.2020.11.014.
- E. Reznick, K. R. Embry, R. Neuman, E. Bolívar-Nieto, N. P. Fey, and R. D. Gregg, “Lower-limb kinematics and kinetics during continuously varying human locomotion,” Scientific Data, vol. 8, no. 1, 2021. doi: 10.1038/s41597-021-01057-9.
- D. Jung, D. Nguyen, M. Park, J. Kim, and K.-R. Mun, “Multiple Classification of Gait Using Time-Frequency Representations and Deep Convolutional Neural Networks,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 4, 2020. doi: 10.1109/TNSRE.2020.2977049.
- O. Dehzangi and M. Taherisadr, “Human gait identification using two dimensional multi-resolution analysis,” Smart Health, vol. 19, 2021. doi: 10.1016/j.smhl.2020.100167.
- R. Kapoor, O. Mishra, and M. M. Tripathi, “Human action recognition using descriptor based on selective finite element analysis,” Journal of Electrical Engineering, vol. 70, no. 6, pp. 443–453, 2020. doi: 10.2478/jee-2019-0077.
- E. Genc, M. E. Yildirim, and Y. B. Salman, “Human activity recognition with fine-tuned CNN-LSTM,” Journal of Electrical Engineering, vol. 75, no. 1, pp. 8–13, 2024. doi: 10.2478/jee-2024-0002.
- O. Dehzangi, M. Taherisadr, and R. Changalvala, “IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion”, Sensors, vol. 17, no. 12, pp. 2735, 2017. doi: 10.3390/s17122735.
- R. Zhong, P.-L. P. Rau, and X. Yan, “Gait Assessment of Younger and Older Adults with Portable Motion-Sensing Methods: A User Study”, Mobile Information Systems, vol. 2019, no. 1, pp. 1093514, 2019. doi: 10.1155/2019/1093514.
- A. Kececi, A. ˘an Yildirak, K. Ozyazici, G. Ayluctarhan, O. Agbulut, and I. Zincir, “Implementation of machine learning algorithms for gait recognition”, Engineering Science and Technology, an International Journal, vol. 23, no. 4, pp. 931-937, 2020. doi: 10.1016/j.jestch.2020.01.005.
- M. A. R. Ahad, T. T. Ngo, A. D. Antar, M. Ahmed, T. Hossain, D. Muramatsu and, Y. Yagi, “Wearable sensor-based gait analysis for age and gender estimation”, Sensors (Switzerland), vol. 20, no. 8, 2020. doi: 10.3390/s20082424.
- J. Camargo, A. Ramanathan, W. Flanagan, and A. Young, “A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions,” Journal of Biomechanics, vol. 119, 2021, doi: 10.1016/j.jbiomech.2021.110320.
- H. Kuduz and F. Kaçar, “A deep learning approach for human gait recognition from time-frequency analysis images of inertial measurement unit signal”, International Journal of Applied Methods in Electronics and Computers, vol. 11, no. 3, pp. 165-173, 2023, doi: 10.58190/ijamec.2023.44.