References
- A. Al-Kababji, F. Bensaali, and S. P. Dakua. Scheduling techniques for liver segmentation: Reducelronplateau vs onecyclelr. ArXiv, 2022.
- L. Alcock, B. Galna, R. Perkins, S. Lord, and L. Rochester. Step length determines minimum toe clearance in older adults and people with Parkinson’s disease. Journal of Biomechanics, 71(1), 2018.
- B. Breine, P. Malcolm, E. C. Frederick, and D. De Clercq. Relationship between running speed and initial foot contact patterns. Medicine and Science in Sports and Exercise, 46(8), 2014.
- S. Butterworth. On the theory of filter amplifiers. Wireless Engineer, 7(6), 1930.
- R. Caruana. Multi-task learning. Machine Learning, 28, 1997.
- W. Chen, Z. Wang, H. Xie, and W. Yu. Characterization of surface EMG signal based on fuzzy entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 2007.
- Y.-S. Cho, S.-H. Jang, J.-S. Cho, M.-J. Kim, H. D. Lee, S. Y. Lee, and S.-B. Moon. Evaluation of validity and reliability of inertial measurement unit-based gait analysis systems. Annals of Rehabilitation Medicine, 42(6), 2018.
- I. Delimaris. Potential adverse biological effects of excessive exercise and overtraining among healthy individuals. Acta Medica Martiniana, 14(3), 2014.
- H. T. Duong, K. M. Hoang, H. V. Pham, and V. A. Trinh. High-performance stacked ensemble model for stride length estimation with potential application in indoor positioning systems. Journal of Communications, 17(8), 2022.
- E. T. B. Ekanayake. Entropy analysis of kinetic and kinematic gait parameters as a potential tool to predict osteoarthritis onset. Master’s thesis, University of Central Oklahoma, 2019.
- M. Falbriard, F. Meyer, B. Mariani, G. P. Millet, and K. Aminian. Accurate estimation of running temporal parameters using foot-worn inertial sensors. Frontiers in Physiology, 9(1), 2018.
- A. Ferrari, D. Micucci, M. Mobilio, and P. Napoletano. Hand-crafted features vs residual networks for human activities recognition using accelerometer. In Proceedings of IEEE International Symposium on Consumer Technologies, 2019.
- F. Fritsch and R. Carlson. Monotone piecewise cubic interpolation. SIAM Journal on Numerical Analysis, 17(2), 1980.
- J. Hannink, T. Kautz, C. F. Pasluosta, J. Barth, S. Schülein, K.-G. Gaßmann, J. Klucken, and B. M. Eskofier. Mobile stride length estimation with deep convolutional neural networks. IEEE Journal of Biomedical and Health Informatics, 22(2), 2017.
- J. Hannink, T. Kautz, C. F. Pasluosta, K.-G. Gaß-mann, J. Klucken, and B. M. Eskofier. Sensor-based gait parameter extraction with deep convolutional neural networks. IEEE Journal of Biomedical and Health Informatics, 21(1), 2017.
- H. Huang, P. Zhou, Y. Li, and F. Sun. A lightweight attention-based CNN model for efficient gait recognition with wearable IMU sensors. Sensors, 21(8), 2021.
- I. Hunter and G. Smith. Preferred and optimal stride frequency, stiffness and economy: Changes with fatigue during a 1-h high-intensity run. European Journal of Applied Physiology, 100(6), 2007.
- C. Ison, C. Neilsen, J. DeBerardinis, M. B. Trabia, and J. S. Dufek. Use of pressure-measuring insoles to characterize gait parameters in simulated reduced-gravity conditions. Sensors, 21(18), 2021.
- Ł. Kidziński, B. Yang, J. L. Hicks, A. Rajagopal, S. L. Delp, and M. H. Schwartz. Deep neural networks enable quantitative movement analysis using single-camera videos. Nature Communications, 11(1), 2020.
- J. Kim, H. Jang, D.-H. Hwang, and C. Park. A step, stride and heading determination for the pedestrian navigation system. Journal of Global Positioning Systems, 3(1-2), 2004.
- D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In Proceedings of International Conference on Learning Representations, 2015.
- Q. Ladetto. On foot navigation: continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering. In Proceedings of International Technical Meeting of the Satellite Division of The Institute of Navigation, 2000.
- C. Liu and G. Jia. Industrial big data and computational sustainability: Multi-method comparison driven by high-dimensional data for improving reliability and sustainability of complex systems. Sustainability, 11(17), 2019.
- Y. Ma, B. Sheng, R. Hart, and Y. Zhang. The validity of a dual azure kinect-based motion capture system for gait analysis: A preliminary study. In Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2020.
- B. Mariani, C. Hoskovec, S. Rochat, C. Büla, J. Penders, and K. Aminian. 3d gait assessment in young and elderly subjects using foot-worn inertial sensors. Journal of Biomechanics, 43(15), 2010.
- B. Mariani, S. Rochat, C. Büla, and K. Aminian. Heel and toe clearance estimation for gait analysis using wireless inertial sensors. IEEE Transactions on Bio-medical Engineering, 59(11), 2012.
- B. Mariani, H. Rouhani, X. Crevoisier, and K. Aminian. Quantitative estimation of foot-flat and stance phase of gait using foot-worn inertial sensors. Gait and Posture, 37(2), 2013.
- E. Martini, T. Fiumalbi, F. Dell’Agnello, Z. Ivanić, M. Munih, N. Vitiello, and S. Crea. Pressure-sensitive insoles for real-time gait-related applications. Sensors, 20(5), 2020.
- H. Masum, S. Chattopadhyay, R. Ray, and S. Bhaumik. Measurement of walking speed from gait data using kurtosis and skewness based approximate and detailed coefficients. IET Science, Measurement and Technology, 12(4), 2018.
- W. Ng, B. Minasny, W. de Sousa Mendes, and J. Demattê. The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. SOIL, 6(2), 2020.
- E. Panero, E. Digo, V. Agostini, and L. Gastaldi. Comparison of different motion capture setups for gait analysis : Validation of spatio-temporal parameters estimation. In Proceedings of IEEE International Symposium on Medical Measurements and Applications, 2018.
- S. Qin, X. Chen, P. Li, W. Li, Z. Wu, H. Jiang, Z. Liu, and R. Zhang. Modeling and evaluating full-cycle natural gait detection based on human electrostatic field. IEEE Transactions on Instrumentation and Measurement, 72, 2023.
- O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015.
- N. Roth, A. Küderle, D. Prossel, H. Gassner, B. M. Eskoer, and F. Kluge. An inertial sensor-based gait analysis pipeline for the assessment of real-world stair ambulation parameters. Sensors, 21(19), 2021.
- M. Sarshar, S. Polturi, and L. Schega. Gait phase estimation by using LSTM in IMU-based gait analysis—proof of concept. Sensors, 21(17), 2021.
- J. Scarlett. Enhancing the performance of pedometers using a single accelerometer. Application Note, Analog Devices, 41(1), 2007.
- J.-D. Sui and T.-S. Chang. IMU based deep stride length estimation with self-supervised learning. IEEE Sensors Journal, 21(6), 2021.
- E. Suter, B. Marti, and F. Gutzwiller. Jogging or walking—comparison of health effects. Annals of Epidemiology, 4(5), 1994.
- R. Vaishya and A. Vaish. Falls in older adults are serious. Indian Journal of Orthopaedics, 54(1), 2020.
- Q. Wang, L. Ye, H. Luo, A. Men, F. Zhao, and Y. Huang. Pedestrian stride-length estimation based on LSTM and denoising autoencoders. Sensors, 19(4), 2019.
- H. Weinberg. Using the ADXL202 in pedometer and personal navigation applications. Analog Devices AN-602 Application Note, 2(2), 2002.
- M. Zhang, Y. Gan, X. Wang, Z. Wang, T. Feng, and Y. Zhang. Gait performance and non-motor symptoms burden during dual-task condition in Parkinson’s disease. Neurological Sciences, 44(1), 2023.
- M. Zhang, Q. Wang, D. Liu, B. Zhao, J. Tang, and J. Sun. Real-time gait phase recognition based on time domain features of multi-mems inertial sensors. IEEE Transactions on Instrumentation and Measurement, 70, 2021.
- X. Zhu, J. Gao, Y. Dai, J. Zhang, W. Zhang, D. Sun, and W. Gu. Three steps from grid-less pressure sensors to gait recognition. IEEE Transactions on Instrumentation and Measurement, 72, 2023.
- M. Zrenner, S. Gradl, U. Jensen, M. Ullrich, and B. M. Eskofier. Comparison of different algorithms for calculating velocity and stride length in running using inertial measurement units. Sensors, 18(12), 2018.